The U.S. Geological Survey in cooperation with Mount Pleasant Water Works updated an existing three-dimensional model (MODFLOW-2000) by Fine, Petkewich, and Campbell (2017) (https://doi.org/10.3133/sir20175128) to evaluate two water-management scenarios and predict the effects of increased pumpage on the groundwater flow and groundwater-level conditions in the Mount Pleasant, South Carolina area. This model was originally developed in 2007, by Petkewich and Campbell (https://pubs.er.usgs.gov/publication/sir20075126), then updated and recalibrated to conditions from 1900 to 2015. The updated model was used to simulate six scenario simulations (scenarios 1-6) for the Mount Pleasant Water Works which are published in a U.S. Geological Survey (USGS) Scientific Investigations Report (https://doi.org/10.3133/sir20175128). The associated model input and output files are available in a USGS data release (https://doi.org/10.5066/F7S181FC). In 2018, using the updated and recalibrated model from 2017, seven additional MODFLOW-2000 scenarios (numbered 7-13), were developed to evaluate additional withdrawal strategies. The archived model input and output files for those scenarios are available in a USGS data release (https://doi.org/10.5066/P9GZEE4E). For these scenarios future groundwater withdrawals for Mount Pleasant Water Works were modified while maintaining 2015 pumping rates for all other pumping wells. The model simulates from 1900-2015 with the addition of 2016-2500 for the predictive scenarios. This data release present the model data sets for 2 additional scenarios. The 2017 model, by Fine and others, was slightly updated to simulate two predictive water-management scenarios that evaluate potential changes in groundwater flow and groundwater-level conditions from the increased withdrawals in the Mount Pleasant, South Carolina area. The model was updated to include 2016-2019 groundwater use data for the Charleston aquifer wells in the Charleston, SC area, along with several periodic tape-down measurements at two recording wells (CHN-14 and BRK-431). The model was not recalibrated for this study. Two scenario simulations were completed, and the results are included in this data release. In scenario 1, Mount Pleasant Waterworks demonstrated reasonable need of 2,409 million gallons per year. This scenario simulates 5 of the 6 Mount Pleasant wells each pumping 1.32 million gallons per day from 2020 to 2050, for a total of 6.6 million gallons per day. No withdrawals from the sixth Mount Pleasant well are simulated during the 2020-2050 time period. In scenario 2, the South Carolina Department of Health and Environmental Control recommended withdrawal of 1,679 million gallons per year is simulated. This scenario simulates 5 of the 6 Mount Pleasant wells each pumping 0.92 million gallons per day from 2020 to 2050, for a total of 4.6 million gallons per day. No withdrawals from the sixth Mount Pleasant well are simulated during the 2020-2050 time period. This USGS data release contains all the input and output files for the simulations described above and in the readme.txt file of this data release (https://doi.org/10.5066/P9FA07XD).

Outage Scenario Modeling Market Outlook

According to our latest research, the global Outage Scenario Modeling Market size in 2024 stands at USD 2.14 billion, driven by the increasing necessity for robust business continuity planning and risk management across critical infrastructure sectors. The market is projected to grow at a strong CAGR of 11.3% from 2025 to 2033, reaching an estimated USD 5.6 billion by 2033. This growth is propelled by the rising frequency of both cyber and physical disruptions, intensifying the demand for advanced scenario modeling solutions that can forecast, simulate, and mitigate outage events.

A key growth driver for the Outage Scenario Modeling Market is the surge in digital transformation initiatives across sectors such as energy, utilities, telecommunications, and manufacturing. As organizations increasingly digitize their operations, their exposure to potential outages—whether from cyber-attacks, equipment failures, or natural disasters—heightens. Advanced outage scenario modeling solutions offer predictive analytics and simulation capabilities, enabling organizations to proactively identify vulnerabilities and design effective response strategies. This proactive approach not only minimizes downtime but also preserves operational integrity and customer trust, making these solutions indispensable for modern enterprises.

Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) technologies into outage scenario modeling tools has significantly enhanced their predictive accuracy and scalability. These technologies enable real-time data analysis, adaptive learning from historical outage events, and the automation of scenario simulations, thus empowering organizations to respond more swiftly and efficiently to emerging threats. The growing adoption of cloud-based solutions further accelerates this trend, as cloud platforms facilitate seamless data integration, remote accessibility, and scalable deployment models. As a result, both large enterprises and small and medium enterprises (SMEs) are increasingly investing in sophisticated outage scenario modeling platforms to safeguard their operations and ensure regulatory compliance.

Another critical factor fueling market expansion is the tightening of regulatory frameworks around risk management and business continuity, especially in sectors deemed as critical infrastructure. Governments and regulatory bodies across North America, Europe, and Asia Pacific are mandating comprehensive outage preparedness plans, often requiring organizations to conduct regular scenario-based risk assessments. This regulatory pressure is driving widespread adoption of outage scenario modeling solutions, as organizations seek to not only comply with legal requirements but also to demonstrate resilience to stakeholders and customers. The market is further buoyed by the rising awareness of the financial and reputational costs associated with unplanned outages, prompting a shift from reactive to proactive risk management strategies.

Regionally, North America currently dominates the Outage Scenario Modeling Market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, leads in terms of technology adoption and regulatory enforcement, with significant investments from both the public and private sectors. Europe is witnessing rapid growth, driven by stringent EU directives on critical infrastructure protection, while Asia Pacific is emerging as a high-growth region due to rapid industrialization and increasing awareness of operational risks. Latin America and the Middle East & Africa, although currently smaller markets, are expected to experience steady growth as organizations in these regions increasingly recognize the value of outage scenario modeling in ensuring business continuity and resilience.

Component Analysis

The Outage Scenario Modeling Market by component is segmented into software and services, each playing a pivotal role in enabling organizations to anticipate and manage outage events effectively. Software solutions form the backbone of this market, offering advanced simulation, predictive analytics, and visualization capabilities. These platforms are designed to integrate seamlessly with existing IT and operational technology (OT) systems, providing real-time insights into outage risks and enabling automated scenario generation. The continual evol

Scenario Analysis Platforms for Planners Market Outlook

According to our latest research, the global Scenario Analysis Platforms for Planners market size reached USD 2.67 billion in 2024. The market is poised for robust expansion, projecting a CAGR of 13.4% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 8.48 billion. This impressive growth trajectory is primarily driven by the increasing demand for advanced decision-support tools among urban planners, government agencies, and enterprises seeking to enhance resilience and optimize resource allocation in the face of growing urbanization, climate change, and complex regulatory landscapes.

One of the most significant growth factors for the Scenario Analysis Platforms for Planners market is the accelerating pace of urbanization worldwide. Rapid urban growth has necessitated the deployment of sophisticated scenario analysis tools to support sustainable city planning, infrastructure development, and resource management. These platforms enable planners to simulate various scenarios, assess potential risks, and make informed decisions that balance economic, social, and environmental objectives. The integration of real-time data, GIS, and predictive analytics into scenario analysis platforms has further improved their accuracy and utility, making them indispensable for modern urban planning initiatives. As cities continue to expand and new urban centers emerge, the demand for scenario analysis platforms is expected to rise steadily.

Another critical driver fueling the market’s growth is the heightened focus on disaster management and business continuity planning. The increasing frequency and severity of natural disasters, such as floods, wildfires, and hurricanes, have underscored the need for robust scenario analysis solutions. Government agencies and enterprises are leveraging these platforms to anticipate potential disruptions, evaluate response strategies, and ensure operational resilience. Additionally, regulatory pressures and the need for compliance with environmental and safety standards have prompted organizations to adopt scenario analysis platforms as part of their risk management frameworks. The ability to model complex scenarios and test contingency plans in a virtual environment has become a strategic imperative for both public and private sector stakeholders.

Technological advancements and the proliferation of cloud-based solutions have also played a pivotal role in the market’s expansion. Cloud-based scenario analysis platforms offer scalability, accessibility, and cost-efficiency, enabling organizations of all sizes to harness advanced planning capabilities without the need for significant upfront investments in IT infrastructure. The growing adoption of artificial intelligence, machine learning, and big data analytics has further enhanced the functionality of these platforms, enabling more sophisticated scenario modeling and real-time decision-making. As digital transformation initiatives gain momentum across industries, scenario analysis platforms are increasingly being integrated with other enterprise systems, such as ERP, CRM, and GIS, to provide a holistic view of organizational risks and opportunities.

From a regional perspective, North America continues to dominate the Scenario Analysis Platforms for Planners market, accounting for the largest share in 2024. This leadership position is attributed to the presence of advanced technology ecosystems, high adoption rates among government agencies and enterprises, and significant investments in smart city and infrastructure projects. Europe follows closely, driven by stringent regulatory requirements and a strong emphasis on sustainable urban development. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, infrastructure investments, and increasing awareness of disaster management solutions. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as governments and organizations in these regions recognize the value of scenario analysis platforms in addressing unique local challenges.

S and PM indicate the prediction interval score and proportion of measurements within the prediction envelope and are reported for prediction of steady-state BV response (150-180 min, i.e., S150 − 180 and PM150−180), prediction of transient response (45-80 min, i.e., S45−80 and PM45−80), and leave-one-out prediction (i.e., SLeave-One-Out and PMLeave-One-Out). P-values for the S and PM are obtained using paired t-test and Chi-squared test, respectively. Underline bold numbers indicates significant difference between the two models.

AI in Scenario Planning Market Outlook

According to our latest research, the global AI in Scenario Planning market size reached USD 1.62 billion in 2024, reflecting the rapid adoption of artificial intelligence technologies across diverse industries for strategic decision-making. The market is projected to grow at a robust CAGR of 22.4% from 2025 to 2033, forecasting a valuation of USD 8.36 billion by 2033. This growth is primarily driven by the increasing need for advanced analytics, risk mitigation, and agile business strategies in an increasingly volatile global business environment.

The primary growth factor for the AI in Scenario Planning market is the accelerating digital transformation across enterprises, which necessitates dynamic and data-driven decision-making processes. Organizations are increasingly leveraging AI-powered scenario planning tools to simulate various business outcomes, assess potential risks, and optimize resource allocation. The integration of machine learning and natural language processing enables these platforms to analyze massive datasets, identify emerging trends, and provide actionable insights with unprecedented speed and accuracy. This capability is particularly valuable in industries facing frequent disruptions, such as finance, healthcare, and manufacturing, where the ability to anticipate and respond to change can be a significant competitive advantage.

Another key driver is the rising complexity of global supply chains and market dynamics, which has heightened the demand for sophisticated scenario analysis. As companies expand their operations internationally, they encounter multifaceted risks, including geopolitical uncertainties, regulatory changes, and fluctuating market demands. AI in scenario planning empowers organizations to model multiple scenarios, evaluate the impact of external variables, and develop robust contingency plans. This not only enhances business resilience but also supports proactive strategic planning, enabling firms to capitalize on emerging opportunities and minimize potential losses. The ongoing advancements in AI algorithms and cloud computing infrastructure further amplify the scalability and accessibility of scenario planning solutions, making them increasingly viable for businesses of all sizes.

Furthermore, the growing emphasis on risk management and compliance in regulated sectors, such as BFSI and healthcare, is fueling the adoption of AI-driven scenario planning. Regulatory bodies are mandating more rigorous risk assessment and reporting standards, compelling organizations to implement advanced tools that can automate scenario analysis and ensure adherence to compliance requirements. AI-powered platforms provide real-time monitoring, predictive analytics, and automated reporting capabilities, streamlining the risk management process and reducing the likelihood of regulatory breaches. As regulatory landscapes continue to evolve, the demand for intelligent scenario planning solutions is expected to rise, further propelling market growth.

From a regional perspective, North America currently dominates the AI in Scenario Planning market, accounting for the largest revenue share in 2024, driven by early technology adoption, strong presence of leading AI vendors, and high investment in digital transformation initiatives. Europe follows closely, with substantial growth observed in sectors such as finance, manufacturing, and healthcare. The Asia Pacific region is emerging as the fastest-growing market, fueled by rapid economic development, increasing digitalization, and government initiatives to promote AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a comparatively slower pace, as organizations in these regions gradually embrace advanced scenario planning solutions to enhance competitiveness and resilience.

Component Analysis

The AI in Scenario Planning market is segmented by component into software and services, each playing a critical role in shaping the market landscape. Software solutions form the backbone of scenario planning, providing advanced analytics, simulation, and visualization capabilities that empower organizations to model complex business scenarios and derive actionable insights. These platforms leverage cutting-edge AI technologies such as machine learning, deep learning, and natural language processing to process vast volumes of structured and unstructured data, identify patterns, and generate predictive models. The continuous evolu

Shelf-Life Modeling Software for MAP Products Market Outlook

According to our latest research, the global shelf-life modeling software for MAP products market size reached USD 412.3 million in 2024, with a robust compound annual growth rate (CAGR) of 12.8% expected through the forecast period. By 2033, the market is projected to attain a value of USD 1,238.7 million, driven by the increasing adoption of advanced predictive analytics and automation in food packaging and preservation. The growth of this market is primarily fueled by the rising demand for Modified Atmosphere Packaging (MAP) solutions across diverse food segments, growing regulatory focus on food safety, and the integration of artificial intelligence and machine learning in shelf-life prediction models.

The shelf-life modeling software for MAP products market is experiencing significant growth due to the increasing complexity of global food supply chains and the heightened need for accurate shelf-life prediction. As food manufacturers and packaging companies strive to minimize waste and ensure compliance with stringent food safety regulations, the adoption of advanced software solutions has become indispensable. These platforms enable users to simulate and optimize the interaction between packaging materials, atmospheric conditions, and product characteristics, thereby ensuring optimal preservation. The heightened consumer demand for fresh, high-quality, and minimally processed foods further amplifies the need for precise shelf-life estimation, compelling market players to invest in innovative modeling technologies.

Another key driver propelling the shelf-life modeling software for MAP products market is the rapid technological advancements in software development, particularly the integration of AI and machine learning algorithms. These technologies enhance the predictive accuracy of shelf-life models, enabling real-time adjustments and scenario planning for a wide range of food products. The software’s ability to process large datasets and generate actionable insights supports food manufacturers in optimizing their packaging processes, reducing returns due to spoilage, and improving overall operational efficiency. Moreover, the increasing adoption of cloud-based platforms has democratized access to sophisticated modeling tools, making them available to small and medium-sized enterprises (SMEs) as well as large corporations.

The shelf-life modeling software for MAP products market is also benefiting from a growing emphasis on sustainability and waste reduction across the food industry. By providing precise predictions of product shelf-life under varying MAP conditions, these solutions help companies reduce over-packaging, minimize food loss, and enhance supply chain transparency. Regulatory bodies in North America, Europe, and Asia Pacific are mandating stricter compliance with food safety and labeling standards, further encouraging the adoption of advanced shelf-life modeling solutions. The convergence of these factors not only drives market expansion but also fosters innovation in the development of user-friendly, customizable, and scalable software platforms.

Regionally, North America and Europe remain at the forefront of the shelf-life modeling software for MAP products market, owing to their advanced food processing industries, strong regulatory frameworks, and high levels of technological adoption. Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, expanding middle-class populations, and increasing investments in food safety infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local food producers and exporters seek to enhance product quality and extend market reach. The global outlook for this market remains highly positive, with diverse opportunities for stakeholders across the value chain.

Component Analysis

The component segment of the shelf-life modeling software for MAP products market is bifurcated into software and services. Software solutions represent the core of this market, offering advanced predictive analytics, scenario modeling, and data visualization capabilities tailored to the unique requirements of MAP products. These platforms are designed to integrate seamlessly with existing enterprise resource planning (ERP) and manufacturing execution systems (MES), fa

Outage Scenario Modeling Market Outlook

According to our latest research, the global outage scenario modeling market size in 2024 is valued at USD 2.89 billion, with a robust compound annual growth rate (CAGR) of 14.2% expected throughout the forecast period. This rapid expansion is driven primarily by the increasing need for business continuity planning and critical infrastructure resilience across industries. By 2033, the outage scenario modeling market is forecasted to reach approximately USD 8.61 billion, reflecting the growing importance of proactive risk management and digital transformation strategies in the face of escalating outage risks.

The primary growth driver for the outage scenario modeling market is the rising complexity and interdependence of modern infrastructure systems. As organizations become more digitalized, the consequences of unplanned outages—whether due to cyberattacks, natural disasters, or equipment failures—grow exponentially. Outage scenario modeling enables enterprises to simulate, predict, and prepare for a wide range of disruptive events, minimizing downtime and financial losses. Increasing adoption of advanced analytics, artificial intelligence, and machine learning within these modeling solutions further enhances their predictive accuracy and value, making them indispensable for sectors such as energy, telecommunications, and transportation.

Another significant factor fueling the growth of the outage scenario modeling market is the tightening regulatory landscape. Governments and industry bodies worldwide are mandating more stringent risk assessment and continuity planning protocols, especially for critical infrastructure operators. Compliance requirements are pushing organizations to adopt sophisticated outage scenario modeling tools that can simulate cascading failures and assess the impact of various outage scenarios in real-time. This regulatory pressure, combined with heightened stakeholder awareness regarding operational resilience, is accelerating the integration of outage modeling solutions across both public and private sectors.

Furthermore, the surge in cyber threats and the increasing frequency of extreme weather events due to climate change are compelling organizations to invest in robust outage scenario modeling capabilities. The financial and reputational stakes associated with outages have never been higher, particularly in sectors like BFSI, healthcare, and utilities, where service continuity is paramount. By leveraging outage scenario modeling, these organizations can not only anticipate potential disruptions but also develop effective mitigation and response strategies, thereby safeguarding their assets and customer trust.

From a regional perspective, North America currently leads the outage scenario modeling market, accounting for over 38% of global revenue in 2024, driven by high digitalization, stringent compliance standards, and significant investments in critical infrastructure. Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 16.5% through 2033, fueled by rapid industrialization, urbanization, and increasing awareness of disaster preparedness. Europe also demonstrates strong demand, particularly in energy and transportation sectors, as regional governments intensify their focus on infrastructure resilience and sustainability.

Component Analysis

The outage scenario modeling market is segmented by component into software and services. The software segment holds the largest share, driven by the increasing need for advanced modeling platforms that can simulate complex outage scenarios and provide actionable insights. Modern outage scenario modeling software leverages artificial intelligence, machine learning, and big data analytics to deliver real-time simulations and predictive analytics, enabling organizations to anticipate and respond to outages with greater agility. As digital transformation ac

A three-dimensional groundwater flow model was developed to characterize groundwater resources of the uppermost principal aquifers in the Williston structural basin in parts of Montana, North Dakota, and South Dakota in the United States and of Manitoba and Saskatchewan in Canada as part of a detailed assessment of the groundwater availability of the area. The uppermost principal aquifers are comprised of the glacial, lower Tertiary, and Upper Cretaceous aquifer systems. The model was developed as a part of the U.S. Geological Survey Water Availability and Use Science Program's effort to conduct large-scale multidisciplinary regional studies of groundwater availability. The numerical model was used to (1) simulate hydrologic scenarios of interest to groundwater managers and to advance the understanding of groundwater budgets and components including recharge, discharge, and aquifer storage for the entire system, (2) compute historical and projected system response to natural and anthropogenic stresses, and (3) evaluate potential hydrologic monitoring programs at a scale relevant to basin-wide water-management decisions. This model was previously published by the U.S. Geological Survey in a Scientific Investigations Report (https://doi.org/10.3133/sir20175158) and the model input and output files are available in a data release (https://doi.org/10.5066/F75B01CZ). The underlying directories contain all of the input and output files for predictive simulations of groundwater response to selected scenarios for the uppermost principal aquifer systems in the Williston Basin, United States and Canada. The predictive simulations were created using base model files from a model developed by Davis and Long and documented in the U.S. Geological Survey Scientific Investigations Report 2017-5158 (https://doi.org/10.3133/sir20175158). Model archive files are documented and are available in an online data release (https://doi.org/10.5066/F75B01CZ). The three-dimensional groundwater-flow model was developed using the numerical modeling software, MODFLOW-NWT. For this study, the numerical groundwater-flow model was used to simulated three predictive scenarios: scenario 1 was focused on flowing artesian wells, and was used to simulate 1960‒2035 hydraulic-head changes that would result if none of the flowing artesian wells in the model area were capped or plugged during this period and other conditions remained constant; scenario 2 simulated 10-year drought for 2006‒15, with no increases in groundwater pumping after 2005; and scenario 3 was identical to scenario 2, except that it also applied the increased groundwater withdrawals necessary to fill the needs of energy-resource production for 2006‒15. A data-worth analysis for evaluation of potential hydrologic monitoring networks was also accomplished using the numerical model. This USGS data release contains all of the input and output files for the model described in the associated model documentation report (https://doi.org/10.3133/pp1841). This data release also includes MODFLOW-NWT (version 1.0.9) source code.

Scenario Generation Toolchains Market Outlook

According to our latest research, the global Scenario Generation Toolchains market size in 2024 stands at USD 1.7 billion, with a robust compound annual growth rate (CAGR) of 13.2% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 5.1 billion, driven by the increasing demand for advanced modeling and simulation tools across various industries. The market's growth is propelled by the rising need for risk assessment, strategic planning, and operational optimization in sectors like BFSI, energy, healthcare, and defense, as organizations strive to navigate an increasingly complex global landscape.

One of the primary growth factors for the Scenario Generation Toolchains market is the escalating complexity of business environments and the corresponding need for sophisticated simulation tools. Organizations are facing unprecedented levels of uncertainty due to global economic fluctuations, regulatory changes, and technological disruptions. Scenario generation toolchains enable enterprises to model multiple future scenarios, assess potential risks, and make informed decisions. The adoption of these tools is particularly pronounced in industries such as financial services and energy, where accurate forecasting and risk mitigation are critical for operational resilience and regulatory compliance. As digital transformation accelerates, the integration of artificial intelligence and machine learning into scenario generation platforms further enhances their predictive capabilities, making them indispensable for organizations aiming to maintain a competitive edge.

Another significant driver is the growing emphasis on regulatory compliance and risk management across various sectors. In highly regulated industries like BFSI and healthcare, scenario generation toolchains support organizations in meeting stringent compliance requirements by simulating the impact of regulatory changes and stress-testing operational models. This proactive approach to risk management not only helps in avoiding costly penalties but also fosters greater stakeholder confidence. Moreover, the increasing frequency and severity of global disruptions, such as cyber-attacks, supply chain interruptions, and geopolitical tensions, have highlighted the need for robust scenario planning tools. As a result, enterprises are investing heavily in scenario generation toolchains to ensure business continuity and agility in the face of unforeseen events.

The rapid evolution of technology is also fueling the expansion of the Scenario Generation Toolchains market. The advent of cloud computing, big data analytics, and advanced visualization techniques has transformed the way organizations approach scenario modeling. Cloud-based deployment modes offer scalable, cost-effective solutions that can be easily integrated with existing enterprise systems, enabling real-time scenario analysis and collaboration across geographically dispersed teams. Furthermore, the rise of Industry 4.0 and the proliferation of IoT devices are generating vast amounts of data, which can be leveraged by scenario generation toolchains to create more accurate and dynamic models. This technological synergy is expected to drive further adoption of scenario generation tools across diverse industry verticals.

Regionally, North America continues to dominate the Scenario Generation Toolchains market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, early adoption of advanced analytics, and high levels of investment in digital transformation initiatives contribute to North America's leadership position. Europe is also witnessing substantial growth, driven by stringent regulatory frameworks and a strong focus on sustainability and risk management. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rapid industrialization, urbanization, and increasing awareness of the benefits of scenario generation tools in optimizing operations and mitigating risks.

Component Analysis

The Scenario Generation Toolchains market by component is segmented into software and services, each playing a pivotal role in enabling organizations to simulate and analyze complex scenarios. Software solutions form the backbone of scenario generation toolchains, offering robust modeling, s

Climate Scenario Analysis Platforms Market Outlook

According to our latest research, the global climate scenario analysis platforms market size reached USD 1.72 billion in 2024, reflecting the rapidly growing demand for advanced climate risk assessment tools. The market is projected to expand at a robust CAGR of 18.4% from 2025 to 2033, reaching an estimated USD 8.13 billion by 2033. This impressive growth trajectory is driven by the increasing need for organizations to assess climate-related risks, comply with evolving regulatory frameworks, and integrate climate considerations into strategic planning.

A primary growth driver for the climate scenario analysis platforms market is the intensifying global focus on climate risk disclosure and sustainable finance. Regulatory bodies such as the Task Force on Climate-related Financial Disclosures (TCFD) and the International Sustainability Standards Board (ISSB) have set new expectations for organizations to transparently report on climate risks and opportunities. As a result, financial institutions, large corporates, and government agencies are adopting climate scenario analysis platforms to model potential climate impacts on assets, portfolios, and business operations. This regulatory momentum is compelling organizations to invest in sophisticated platforms capable of integrating diverse climate scenarios, physical and transition risk variables, and granular geospatial data, thereby fueling market expansion.

Another significant factor propelling the market is the increasing integration of artificial intelligence, machine learning, and advanced analytics within climate scenario analysis platforms. These technological advancements enable platforms to process vast amounts of climate, environmental, and economic data, offering predictive insights and more granular scenario modeling. The ability to simulate various climate pathways—ranging from best-case to worst-case emissions scenarios—empowers organizations to make informed decisions regarding risk mitigation, capital allocation, and long-term sustainability strategies. The convergence of climate science and digital innovation is thus transforming the market landscape, with vendors continually enhancing their offerings to deliver greater accuracy, scalability, and user-friendly interfaces.

Furthermore, the growing recognition of climate change as a material financial risk across sectors such as banking, insurance, energy, and manufacturing is accelerating the uptake of climate scenario analysis platforms. Investors and stakeholders are increasingly demanding evidence of robust climate risk management and resilience planning. This trend is particularly pronounced among financial institutions, which are leveraging these platforms for stress testing, portfolio analysis, and regulatory compliance. Additionally, energy and utility companies are utilizing scenario analysis tools to assess the impact of extreme weather events, transition risks, and evolving policy landscapes on their assets and operations. The broadening end-user base, combined with heightened stakeholder expectations, is set to sustain market growth over the forecast period.

Regionally, North America and Europe are at the forefront of adoption, driven by stringent regulatory requirements, mature financial markets, and high awareness of climate risks. North America, led by the United States and Canada, benefits from a strong ecosystem of technology providers, financial institutions, and sustainability-focused corporates. Europe, meanwhile, is propelled by progressive climate policies, the European Green Deal, and robust ESG investment trends. The Asia Pacific region is rapidly emerging as a high-growth market, fueled by increasing climate vulnerability, urbanization, and government initiatives aimed at climate adaptation and resilience. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by international development programs and growing awareness of climate risks in critical sectors such as agriculture and energy.

Component Analysis

The climate scenario analysis platforms market is segmented by component into software and services, each playing a pivotal role in enabling organizations to effectively assess and manage climate risks. The software segment, which includes both on-premises and cloud-based solutio

Stormwater Digital Twin Scenario Planning Market Outlook

According to our latest research, the global stormwater digital twin scenario planning market size reached USD 1.62 billion in 2024, driven by rapid advancements in digital twin technologies and increasing urbanization. The market is expected to grow at a robust CAGR of 15.7% from 2025 to 2033, with the forecasted market size projected to reach USD 5.08 billion by 2033. This remarkable growth is primarily fueled by the urgent need for efficient stormwater management solutions, the rising frequency of extreme weather events, and enhanced regulatory pressures on urban infrastructure resilience.

One of the primary growth factors propelling the stormwater digital twin scenario planning market is the escalating demand for predictive analytics in urban water management. As cities expand and climate change intensifies, the unpredictability and frequency of flooding events have surged, necessitating advanced tools for proactive risk assessment. Digital twin technologies enable real-time simulation and scenario planning, allowing municipalities and utilities to visualize, test, and optimize stormwater infrastructure responses before actual events occur. The integration of IoT sensors, machine learning, and big data analytics into these platforms further enhances their predictive accuracy, facilitating better decision-making and resource allocation. This, in turn, reduces operational costs and infrastructure failures, making digital twin solutions a strategic investment for urban planners and water authorities worldwide.

Another significant driver is the growing emphasis on sustainable urban development and regulatory compliance. Governments and regulatory bodies across the globe are tightening requirements for stormwater management, especially in flood-prone and rapidly urbanizing regions. Digital twin scenario planning platforms offer a comprehensive, data-driven approach to meeting these mandates by enabling continuous monitoring, scenario analysis, and compliance reporting. By simulating various stormwater events and management strategies, these systems help stakeholders identify optimal solutions that minimize environmental impact and ensure public safety. The ability to demonstrate compliance through digital records and predictive models is increasingly becoming a differentiator for municipalities and private stakeholders seeking funding and public support for infrastructure projects.

Technological innovation is also playing a pivotal role in the expansion of the stormwater digital twin scenario planning market. The convergence of cloud computing, edge devices, and artificial intelligence has significantly enhanced the scalability and accessibility of digital twin solutions. Modern platforms can now integrate vast datasets from disparate sources, including weather forecasts, hydrological sensors, and urban planning databases, to deliver highly accurate, real-time insights. This technological leap has lowered entry barriers for smaller municipalities and private entities, broadening the market’s reach. Furthermore, the emergence of open-source frameworks and interoperability standards is fostering collaboration among technology vendors, utilities, and government agencies, accelerating solution development and deployment.

From a regional perspective, North America currently dominates the stormwater digital twin scenario planning market, accounting for the largest revenue share in 2024. This leadership is attributed to substantial investments in smart city initiatives, advanced infrastructure, and a high level of digital adoption among municipalities. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, driven by rapid urbanization, frequent flooding events, and increased government focus on sustainable water management. Europe also represents a significant market, with stringent environmental regulations and a strong emphasis on technological innovation supporting widespread adoption of digital twin platforms.

Component Analysis

The stormwater digital twin scenario planning market is segmented by component into software, hardware, and services, each playing a crucial role in the overall ecosystem. Software forms the backbone of digital twin platforms, providing the simulation, data analytics, and visualization capabilities required for effective scenario planning. These solut

AI-Driven Financial Scenario Planning Market Outlook

According to our latest research, the AI-Driven Financial Scenario Planning market size reached USD 4.1 billion in 2024, with a robust year-on-year growth driven by increasing adoption across industries. The market is set to expand at a CAGR of 19.8% from 2025 to 2033, projecting a value of USD 20.1 billion by 2033. This impressive growth trajectory is fueled by the surging demand for advanced analytics, real-time scenario modeling, and the critical need for agile financial decision-making in a volatile global economy. As per our latest research, organizations are increasingly leveraging AI-powered tools to enhance the accuracy, speed, and strategic value of their financial planning processes.

One of the primary growth factors for the AI-Driven Financial Scenario Planning market is the escalating complexity of global financial environments. Enterprises today face unprecedented market volatility, regulatory changes, and disruptive competition, all of which necessitate agile and accurate scenario planning. AI-driven solutions are uniquely positioned to address these challenges, offering predictive analytics, machine learning-based forecasting, and automated risk assessment capabilities. These features empower organizations to simulate multiple financial outcomes, anticipate risks, and optimize resource allocation, thereby improving overall business resilience. The integration of AI into financial planning processes is further accelerated by the increasing availability of big data and advancements in data processing technologies, enabling more granular and dynamic scenario modeling.

Another significant driver is the rising adoption of digital transformation initiatives across various sectors, particularly in banking, financial services, insurance (BFSI), healthcare, and retail. Organizations are investing heavily in AI-driven financial scenario planning tools to streamline budgeting, forecasting, and strategic planning functions. These tools facilitate real-time collaboration among finance teams, automate repetitive tasks, and provide actionable insights for decision-makers. The shift towards cloud-based deployment models has further democratized access to advanced financial planning solutions, allowing small and medium enterprises (SMEs) to benefit from capabilities that were previously accessible only to large corporations. The convergence of AI with other emerging technologies such as natural language processing and robotic process automation is also enhancing the functionality and user experience of financial scenario planning platforms.

The growing emphasis on risk management and regulatory compliance is also propelling the adoption of AI-driven financial scenario planning solutions. In an era of stringent regulatory requirements and heightened scrutiny, organizations are leveraging AI to ensure compliance, monitor financial health, and detect anomalies in real time. AI-powered platforms can automatically generate compliance reports, flag potential risks, and provide early warnings about financial discrepancies. This not only reduces the risk of regulatory penalties but also enhances stakeholder confidence and corporate governance. As financial regulations continue to evolve globally, the demand for robust, AI-enabled scenario planning tools is expected to surge, further contributing to market growth.

The integration of FP&A AI into financial scenario planning is revolutionizing how organizations approach budgeting and forecasting. By leveraging artificial intelligence, finance teams can now analyze vast amounts of data with unprecedented speed and accuracy. This technological advancement allows for more precise financial predictions, helping businesses to make informed decisions in real-time. FP&A AI tools enhance the ability to identify trends and anomalies early, providing a competitive edge in a rapidly changing market environment. As companies strive for greater efficiency and agility, the adoption of FP&A AI is becoming increasingly essential for maintaining financial health and achieving strategic objectives.

From a regional perspective, North America currently dominates the AI-Driven Financial Scenario Planning market, accounting for a significant share of global revenues in 2024. This leadership position is attributed to the high concentration of technology-driv

As described in the README.md file, the GitHub repository github.com/USEPA/PRTR-QSTR-models/tree/data-driven are Python scripts written to run Quantitative Structure–Transfer Relationship (QSTR) models based on chemical structure-based machine learning (ML) models for supporting environmental regulatory decision-making. Using features associated with annual chemical transfer amounts, chemical generator industry sectors, environmental policy stringency, gross value added by industry sectors, chemical descriptors, and chemical unit prices, as in the GitHub repository PRTR_transfers, the QSTR models developed here can predict potential EoL activities for chemicals transferred to off-site locations for EoL management. Also, this contribution shows that QSTR models aid in estimating the mass fraction allocation of chemicals of concern transferred off-site for EoL activities. Also, it describes the Python libraries required for running the code, how to use it, the obtained outputs files after running the Python script, and how to obtain all manuscript figures and results. This dataset is associated with the following publication: Hernandez-Betancur, J.D., G.J. Ruiz-Mercado, and M. Martín. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, USA, 11(9): 3594-3602, (2023).

Headcount Planning and Scenario Modeling Market Outlook

As per our latest research, the global Headcount Planning and Scenario Modeling market size reached USD 2.7 billion in 2024, and is expected to grow at a robust CAGR of 11.2% during the forecast period, reaching USD 7.2 billion by 2033. The market’s expansion is being driven by the increasing need for agile workforce management, dynamic business environments, and the rising adoption of advanced analytics and digital transformation strategies across industries. Organizations are prioritizing data-driven approaches to optimize their workforce, reduce costs, and enhance operational efficiency, which is fueling the demand for sophisticated headcount planning and scenario modeling solutions globally.

One of the primary growth factors propelling the Headcount Planning and Scenario Modeling market is the rapid digitalization of human resource functions. As businesses face unpredictable market conditions and workforce disruptions, the need for real-time workforce analytics and scenario planning has become paramount. Advanced headcount planning and scenario modeling solutions empower organizations to simulate various workforce scenarios, assess the impact of strategic decisions, and plan for contingencies efficiently. This capability is particularly crucial in today’s volatile economic landscape, where companies must quickly adapt to changing business requirements, fluctuating demand, and evolving regulatory environments. Furthermore, the integration of artificial intelligence and machine learning in these solutions is enhancing predictive accuracy, enabling organizations to make more informed decisions regarding talent acquisition, workforce optimization, and succession planning.

Another significant driver is the increasing focus on cost optimization and resource allocation. Organizations across industries are under constant pressure to maintain lean operations while ensuring they have the right talent in place to achieve strategic objectives. Headcount planning and scenario modeling tools provide HR leaders and business managers with granular insights into workforce composition, skills gaps, and future talent needs. By leveraging these insights, companies can align their human capital strategies with business goals, minimize overstaffing or understaffing risks, and improve overall productivity. The growing trend of remote and hybrid work models has further amplified the need for flexible workforce planning solutions that can accommodate dynamic work arrangements and facilitate effective collaboration across geographically dispersed teams.

Moreover, regulatory compliance and reporting requirements are driving the adoption of advanced headcount planning and scenario modeling solutions. Industries such as BFSI, healthcare, and manufacturing are subject to stringent labor laws and reporting standards, necessitating accurate headcount tracking and workforce forecasting. With increasing scrutiny from regulatory agencies and stakeholders, organizations are investing in robust planning tools to ensure compliance, mitigate risks, and demonstrate transparency in workforce management practices. The ability to generate comprehensive reports, conduct what-if analyses, and model workforce scenarios in real-time is becoming a critical differentiator for organizations seeking to maintain a competitive edge in highly regulated markets.

From a regional perspective, North America continues to dominate the Headcount Planning and Scenario Modeling market owing to its mature technological infrastructure, high adoption of HR analytics, and the presence of leading solution providers. However, Asia Pacific is emerging as a high-growth region, driven by rapid economic development, increasing digital transformation initiatives, and the growing emphasis on workforce optimization in countries such as China, India, and Japan. Europe also holds a significant market share, supported by strong regulatory frameworks and a focus on organizational agility. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, fueled by the rising awareness of workforce planning benefits and the gradual adoption of cloud-based solutions.

Component Analysis

The Component segment of the Headcount Planning and Scenario Modeling market is broadly categorized into Software and Services</

Deposit Forecasting Software Market Outlook

Based on our latest research, the global deposit forecasting software market size reached USD 1.12 billion in 2024, reflecting robust adoption across the banking and financial sectors. The market is expected to grow at a CAGR of 11.3% from 2025 to 2033, resulting in a projected value of USD 2.97 billion by 2033. This remarkable growth is primarily driven by the increasing need for advanced analytics to manage liquidity, optimize interest income, and comply with evolving regulatory requirements in the financial services industry.

One of the most significant growth factors for the deposit forecasting software market is the ongoing digital transformation in the banking and financial sectors. Financial institutions are increasingly leveraging data analytics and artificial intelligence to enhance their decision-making processes. As customer expectations evolve and competition intensifies, banks and credit unions are under pressure to accurately predict deposit inflows and outflows. This capability enables them to optimize their balance sheets, manage liquidity risks, and improve overall profitability. Furthermore, the integration of deposit forecasting tools with core banking systems and enterprise resource planning platforms is streamlining operations, reducing manual intervention, and minimizing errors. The growing focus on automation and real-time data analysis is expected to further drive the adoption of deposit forecasting software globally.

Another key driver propelling the deposit forecasting software market is the increasing regulatory scrutiny on liquidity management. Regulatory bodies around the world are enforcing stringent guidelines for liquidity coverage ratios (LCR) and net stable funding ratios (NSFR), compelling financial institutions to adopt sophisticated forecasting tools. Deposit forecasting software enables banks and financial institutions to maintain compliance by providing accurate and timely projections of deposit levels, supporting stress testing, and scenario analysis. Additionally, the rise of open banking and the proliferation of digital channels have led to more dynamic and volatile deposit patterns, making traditional forecasting methods obsolete. As a result, there is a growing demand for advanced software solutions that can process large volumes of data from multiple sources and deliver actionable insights in real-time.

The surge in adoption of cloud-based solutions is also contributing significantly to the growth of the deposit forecasting software market. Cloud deployment offers numerous advantages, such as scalability, flexibility, and cost-effectiveness, making it an attractive option for both large enterprises and small and medium-sized financial institutions. The ability to access forecasting tools remotely and integrate them with other cloud-based applications enhances collaboration and decision-making across geographically dispersed teams. Moreover, cloud-based software providers are continuously innovating by incorporating machine learning algorithms and predictive analytics, which further enhances the accuracy and reliability of deposit forecasts. This trend is expected to accelerate as more financial institutions prioritize digital agility and operational resilience.

From a regional perspective, North America continues to dominate the deposit forecasting software market, accounting for the largest share in 2024. This leadership is attributed to the presence of major financial institutions, early adoption of advanced technologies, and a highly regulated environment that emphasizes risk management and compliance. Europe follows closely, driven by the rapid digitalization of banks and increasing regulatory mandates. The Asia Pacific region is emerging as a high-growth market, fueled by the expansion of the banking sector, rising fintech investments, and the adoption of cloud-based solutions. Latin America and the Middle East & Africa are also witnessing increased adoption, although at a relatively slower pace, due to growing awareness of the benefits of deposit forecasting and the need for improved liquidity management.

Component Analysis

The deposit forecasting software market is segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment comprises standalone applications and integrated platforms designed to provide predictive analytics, scenario modeling, and real-time reporting. These solutio

This data release contains data and supporting information from a systematic literature review of predictive and forecasting models of Harmful Algal Blooms (HABs) in flowing waters, primarily rivers but also in-stream reservoirs (e.g., run-of-river reservoir and lock-and-dams) and tidal or estuarine environments where unidirectional flow dominates. The systematic literature review began with queries from multiple scientific publication databases, followed by a three-level screening process, and finally information extraction by the authors of this data release. We included only those models that make predictions beyond the calibration datasets in time or space or are utilized for sensitivity or scenario analysis. We excluded purely empirical studies. We required that the modeling effort was motivated by a desire to understand and predict HABs in flowing waters and we did not limit our review to only cyanobacteria or specific modeling endpoints. To extract information from each art ...

Scenario Planning Software Market Outlook

According to our latest research, the global Scenario Planning Software market size reached USD 2.13 billion in 2024, with strong momentum expected to continue. The market is projected to expand at a robust CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 6.31 billion by 2033. This growth is primarily driven by the increasing need for advanced risk management, business continuity planning, and strategic decision-making tools across diverse industries in a rapidly changing global environment.

One of the primary growth factors fueling the Scenario Planning Software market is the accelerating pace of business disruption, driven by factors such as geopolitical instability, economic volatility, and technological advancements. Organizations are facing unprecedented levels of uncertainty, compelling them to adopt sophisticated scenario planning tools that enable data-driven forecasting and agile response strategies. The integration of artificial intelligence and machine learning capabilities into scenario planning solutions is further enhancing their predictive accuracy and usability, making them indispensable for enterprises seeking to maintain competitiveness and resilience. As businesses increasingly recognize the value of proactive scenario analysis, the demand for customizable, scalable, and user-friendly scenario planning software is expected to surge across all major sectors.

Another significant growth driver is the rising emphasis on regulatory compliance and risk management, particularly in highly regulated industries such as BFSI, healthcare, and government. Regulatory bodies are mandating more robust risk assessment and continuity planning frameworks, prompting organizations to invest in advanced scenario planning solutions that can automate compliance processes and provide real-time risk insights. The shift toward integrated risk management platforms, which consolidate scenario planning, risk analytics, and financial modeling, is streamlining operations and reducing manual workloads. This trend is especially pronounced among large enterprises, which require enterprise-grade solutions capable of supporting complex, multi-dimensional scenario modeling across global operations.

The growing adoption of cloud-based deployment models is also playing a pivotal role in market expansion. Cloud-based scenario planning software offers enhanced scalability, lower upfront costs, and seamless integration with other enterprise systems, making it an attractive option for both large organizations and small and medium enterprises (SMEs). The proliferation of remote and hybrid work models post-pandemic has accelerated the need for accessible, collaborative scenario planning tools that support distributed teams. As cloud infrastructure becomes more secure and reliable, cloud-based scenario planning solutions are expected to outpace on-premises alternatives, driving overall market growth and enabling organizations to respond more effectively to dynamic risk landscapes.

From a regional perspective, North America continues to dominate the Scenario Planning Software market, accounting for the largest share in 2024 due to the presence of major industry players, high technology adoption rates, and strong demand from sectors such as BFSI, healthcare, and IT. However, Asia Pacific is emerging as the fastest-growing region, with a CAGR exceeding 15%, fueled by rapid digital transformation, increasing awareness of risk management, and the expansion of multinational enterprises in countries like China, India, and Japan. Europe also represents a significant market, driven by stringent regulatory requirements and a strong focus on business continuity planning among financial institutions and large corporates. The Middle East & Africa and Latin America are witnessing steady growth, supported by rising investments in digital infrastructure and a growing recognition of the importance of scenario planning in volatile economic environments.

Component Analysis

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Large Language Model on 2025-11-11. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Introduction

Dive into the thrilling world of superhero and villain battles with our unique dataset. This data captures the essence of epic showdowns between beloved characters from Marvel and DC Comics. It presents a fun and challenging way for data scientists to predict battle outcomes based on character attributes and special abilities.

Dataset Overview

This dataset contains information on fictional characters and their attributes, such as strength, speed, intelligence, special abilities, and weaknesses. The goal is to predict the outcome of battles between these characters based on these features.

Warning: Unusual Dataset

This dataset is unusual; you will see characters like Spiderman with the Telekinesis power and other weird powers and weaknesses for many heroes. This is not a mistake in the dataset. You will also notice that different production companies are associated with characters, even when they are not typically related to those characters. The reason behind this mix of combinations is to examine how all these variables affect the battle outcomes of the characters against their opponents and to create characters never seen before

Opponents

The battle outcomes are simulated based on the attributes of individual characters without specifying a direct opponent. Each character's performance is evaluated against an "average opponent" with default attributes.

Dataset Variety

This dataset features a variety of characters where powers and attributes have been switched and shuffled to get unpredictable scenarios.

Variables Explained

1. Character:

   • Categorical variable representing the name of the fictional character.
   • Values: Spider-Man, Iron Man, Captain America, Thor, Batman, Superman, Wonder Woman, Flash.

2. Universe:

   • Categorical variable representing the universe or franchise from which the character originates.
   • Values: Marvel, DC Comics.

3. Strength:

   • Numerical attribute representing the character's physical strength on a scale from 1 to 10.

4. Speed:

   • Numerical attribute representing the character's speed or agility on a scale from 1 to 10.

5. Intelligence:

   • Numerical attribute representing the character's intelligence or strategic thinking on a scale from 1 to 10.

6. Special Abilities:

   • Categorical variable representing special powers or abilities possessed by the character.
   • Values: Flight, Invisibility, Super Strength, Telekinesis.

7. Weaknesses:

   • Categorical variable representing vulnerabilities or weaknesses of the character.
   • Values: Kryptonite, Magic, Wooden Stake, Silver.

8. Battle Outcome (Target Variable):

   • Binary variable indicating the outcome of the battle.
   • Values: 1 (Character 1 wins), 0 (Character 2 wins).

Potential Uses

This dataset offers a fun and engaging way for data scientists to apply and hone their skills. Here are several potential uses:

1. Predictive Modeling:

   • Build machine learning models to predict the outcome of battles based on character attributes.
   • Explore different algorithms like logistic regression, decision trees, and neural networks.

2. Feature Importance Analysis:

   • Analyze the importance of different attributes in determining battle outcomes.
   • Identify which factors (strength, speed, intelligence, special abilities, weaknesses) play the most significant role.

3. Scenario Simulation:

   • Simulate different battle scenarios by adjusting character attributes.
   • Explore how changes in special abilities or weaknesses impact the outcome.

4. Game Development:

   • Use the dataset as a foundation for developing games or simulations.
   • Create interactive experiences where players can predict or influence battle outcomes.

5. Educational Tool:

   • Utilize the dataset in educational settings to teach data science concepts.
   • Engage students with a fun and relatable dataset for practical learning.

Conclusion

This fictional character battle dataset provides a playful yet challenging platform for data scientists to test their skills. By predicting battle outcomes based on character attributes, one can explore various machine learning techniques and uncover the hidden dynamics of these epic showdowns. Get ready to embark on a data science adventure and see who emerges victorious in the ultimate battle of heroes and villains!