The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1 - 30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modelling methods and 5200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest R2 (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.

Riihimäki, Kemppinen, Kopecký & Luoto (Preprint). Topographic Wetness Index as a proxy for soil moisture: the importance of flow-routing algorithm and grid resolution. Zenodo.

These are the data from Riihimäki & Kemppinen et al. (Preprint).

Stream flowline delineation from high-resolution digital elevation models (HRDEMs) can be biased partly due to the as absence of information on the locations of anthropogenic drainage structures (ADS) such as bridges and culverts on the grid surface. Without the ADS, the roads may act as ‘digital dams’ that prevent the overland drainages correctly crossing through in the flowline delineation. However, it is unclear if a combination of variables for terrain-based hydrologic modeling can be used to mitigate the effect of ‘digital dams.’ This study assessed the impacts of ADS locations, spatial resolution (ranging from 1-m to 10-m), depression processing methods (filling, breaching, and stream burning), and flow direction algorithms (D8, D-Infinity, and MFD-md) on hydrologic connectivity through ADS in an agrarian landscape of Nebraska. The assessment was conducted based on the offset distances between modeled stream crossings and original ADS on the road. Results suggested that a) stream burning in combination with D8 or D-Infinity flow direction algorithm is the best option for modeling stream flowlines from HRDEMs in an agrarian landscape; b) the smoothing effect associated with increasing the HRDEM resolution was not found significant for producing accurate drainage crossing near ADS locations; and c) D8 and D-Infinity flow direction algorithms resulted similar outputs with respect to hydrologic drainage crossing at ADS locations.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

The runoff map shows the lines in the landscape where the water potentially flows off concentrated after a rain shower, taking into account the topography and the watercourses present. A distinction is made between the runoff map with single streamlines and the runoff map with multiple streamlines. In the single streamline runoff map, the calculation algorithm directs all runoff entering a pixel to the lowest surrounding pixel (Single Flow Direction). This creates concentrated, clearly delineated runoff lines that represent the locations in the landscape with the greatest chance of runoff. When using multiple streamlines, the calculation algorithm directs all runoff entering a pixel proportionally to all lower surrounding pixels, with the lowest pixel receiving the most runoff and the lowest pixel receiving the least runoff (Multiple Flow Direction) . This creates diffuse runoff lines that give a more realistic picture of runoff over a slope.

Topographic Wetness Index (TWI) is calculated as log_e(specific catchment area / slope) and estimates the relative wetness within a catchment.

The TWI product was derived from the partial contributing area product (CA_MFD_PARTIAL), which was computed from the Hydrologically enforced Digital Elevation Model (DEM-H; ANZCW0703014615), and from the percent slope product, which was computed from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). Both DEM-S and DEM-H are based on the 1 arcsecond resolution SRTM data acquired by NASA in February 2000.

Note that the partial contributing area product does not always represent contributing areas larger than about 25 km2 because it was processed on overlapping tiles, not complete catchments. This only impacts TWI values in river channels and does not affect values on the land around the river channels. Since the index is not intended for use in river channels this limitation has no impact on the utility of TWI for spatial modelling.

The TWI data are available in gridded format at 1 arcsecond and 3 arcsecond resolutions.

The 3 arcsecond resolution TWI product was generated from the 1 arcsecond TWI product and masked by the 3” water and ocean mask datasets. Lineage: Source data 1.\t1 arcsecond resolution partial contributing area derived from the DEM-H (ANZCW0703014615). 2.\t1 arcsecond resolution slope percent derived from DEM-S (ANZCW0703014016) 3.\t3 arcsecond resolution SRTM water body and ocean mask datasets

TWI calculation TWI was calculated from DEM-H following the methods described in Gallant and Wilson (2000). The program uses a slope-weighted multiple flow algorithm for flow accumulation, but uses the flow directions derived from the interpolation (ANUDEM) where they exist. In this case, they are the ANUDEM-derived flow directions only on the enforced stream lines, so the flow accumulation will follow the streams. The different spacing in the E-W and N-S directions due to the geographic projection of the data was accounted for by using the actual spacing in metres of the grid points calculated from the latitude.

Contributing area was converted to specific catchment area using the square root of cell area as the best estimate of cell width on the approximately rectangular cells. The contributing area value was also reduced by half of one grid cell to provide better estimates at tops of hills.

Slope was converted from percent to ratio, as required by the TWI calculation, by dividing by 100. A minimum slope of 0.1% was imposed to prevent division by zero.

The TWI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.

The 3 arcsecond resolution version was generated from the 1 arcsecond TWI product. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The 3” TWI data were then masked using the SRTM 3” ocean and water body datasets.

Note that the limitation of partial contributing area due to tiled processing, so that catchment areas extending beyond about 5 km from a tile edge are not captured, has little impact on topographic wetness index. TWI is useful as a measure of position in the landscape on hillslopes (not river channels) and all hillslope areas will be accurately represented by the partial contributing area calculations.

Some typical values for TWI in different positions on the landscape are:

Position\t\t\t Specific catch. Slope (%)\tTWI area (m)\t Upper slope\t\t\t 50\t\t\t\t 20\t\t5.5 Mid slope\t\t\t 150\t\t\t\t 10\t\t7.3 Convergent lower\t 3000\t\t\t 3\t\t11.5 slope

In channels, some typical values would be (using flow width of 30 m):

Contributing \t Specific catch.\tSlope (%)\tTWI area (km2) area (103 m) 1\t\t\t\t 33\t\t\t\t1\t\t15.0 25\t\t\t\t833\t\t\t\t0.5\t\t18.9 1000\t\t\t\t33,333\t\t\t0.1\t\t24.2

Values of TWI larger than about 12 are most likely in channels or extremely flat areas where the physical concepts behind TWI are invalid and probably are not useful for measuring relative wetness, topographic position or any other geomorphic property. Contributing area (for channels) and MrVBF are more likely to be useful indicators of geomorphic properties in these areas. See, for example, McKenzie, Gallant and Gregory (2003) where soil depth is estimated using TWI on hillslopes and MrVBF in flat valley floors: the range of validity for TWI in that example was approximately 4.8 to somewhat beyond 8.5.

Hence the omission of contributing areas larger than about 25 km2 has no effect on the practical applications of TWI.

Gallant, J.C. and Wilson, J.P. (2000) Primary topographic attributes, chapter 3 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

McKenzie, N.J., Gallant, J.C. and Gregory, L. (2003) Estimating water storage capacities in soil at catchment scales. Cooperative Research Centre for Catchment Hydrology Technical Report 03/3.

Logistic regression model (coefficients)–flood susceptibility.

The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.

The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that take into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Network (SDN) controller. The designed routing algorithm uses Per-Packet multipath routing to make the best use of the network’s resources. Per-Packet multipath is known to have adverse affects when used with TCP, so we propose modifications to the Multipath TCP (MPTCP) protocol to overcome this. Network simulations are performed on a real world network model with 41 nodes and 60 bidirectional links. Results for the EA routing solution with the modified MPTCP protocol show a 29% increase in the total network Goodput, and a more than 50% average reduction in a flow’s end-to-end delay, when compared to OSPF and standard TCP under the same network topology and flow request conditions.

DEM (digital elevation model) is the abbreviation of digital elevation model, which is an important original data for watershed terrain and feature recognition. The principle of DEM is to divide the watershed into M rows and N columns of quadrilateral (cell), calculate the average elevation of each quadrilateral, and then store the elevation in a two-dimensional matrix. Because DEM data can reflect the local terrain features of a certain resolution, a large amount of surface morphology information can be extracted by DEM, which includes the slope, slope direction and the relationship between cells of watershed grid unit [7]. At the same time, the surface water flow path, river network and watershed boundary can be determined by certain algorithm. Therefore, to extract basin features from DEM, a good basin structure model is the premise and key of the design algorithm.

description: This Climate Data Record (CDR) contains total solar irradiance (TSI) as a function of time created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Total solar irradiance is the total, spectrally integrated energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged TSI data range from 1882 to the present, and annual-averaged TSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.; abstract: This Climate Data Record (CDR) contains total solar irradiance (TSI) as a function of time created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Total solar irradiance is the total, spectrally integrated energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged TSI data range from 1882 to the present, and annual-averaged TSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

This Climate Data Record (CDR) of monthly mean High Resolution Infrared Radiation Sounder (HIRS) Outgoing Longwave Radiation (OLR) flux at the top of the atmosphere in all sky conditions is on a 2.5 degree x 2.5 degree grid with global coverage from January 1979 to the present and continuing monthly. Grid dimensions are 144 x 72, with 10,368 total grids over the globe. The data set was derived using a multispectral HIRS OLR algorithm on the HIRS level 1b dataset from the TIROS-N to NOAA-19 series satellites with inter-satellite and radiance calibration adjustments employing an empirical diurnal model for the monthly mean derivation. The data file format is netCDF-4 with CF metadata, and it is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

description: This Climate Data Record (CDR) contains solar spectral irradiance (SSI) as a function of time and wavelength created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Solar spectral irradiance is the wavelength-dependent energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2 per nm. Also included is the value of total (spectrally integrated) solar irradiance in units W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged SSI data range from 1882 to the present, and annual-averaged SSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.; abstract: This Climate Data Record (CDR) contains solar spectral irradiance (SSI) as a function of time and wavelength created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Solar spectral irradiance is the wavelength-dependent energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2 per nm. Also included is the value of total (spectrally integrated) solar irradiance in units W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged SSI data range from 1882 to the present, and annual-averaged SSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

The dataset is a classical arithmetic example containing task orientation attributes (Left , Right and free E), task time and task sequential relationships for product processing, see figures: P-9, P-12, P-16, P-24 and P-65, see file: Task time for each model. Prioritization diagrams for classical algorithms were obtained from the literature, and links to the relevant literature are provided in the "References" section. The study aims to solve a class of balancing problems of mixed model parallel two-sided assembly line, and mainly accomplishes the following aspects: (1) Define the "r-q" partition conceptual model of parallel two-sided assembly line with mixed-model, see the file: Assembly line schematic and vector drawing. (2) According to the conceptual model of partitioning, construct a comprehensive priority relationship diagram of products, realize the design of mixed-model production scheduling, and establish the first type of balanced mathematical model of mixed-model parallel two-sided assembly line. (3) For the first time, an ant colony algorithm has been successfully applied to solve the first type of balancing mathematical model of mixed-model parallel two-sided assembly line. (4) The assembly balancing problem of product flow production was calculated by this dataset, and the results include the number of workstations, the number of multiline stations, the number of positions, the balancing rate, and the smoothing index for each algorithm, see the file: study results. The results are compared with those of heuristic algorithm and artificial fish swarm algorithm, and the ant colony algorithm has better computational results, which verifies that the model and algorithm for a class of balancing problems of mixed-model parallel two-sided assembly lines are effective, and solves the joint optimization problem of a complex assembly line system that considers mixed-model sorting and product time balancing.

The Replication Package (data & code) AND A Simple Demo of EHAdvisor.######## Introduction of Replication Package (replication.zip) #########READMEThis is the replication package of the paper "Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective".## ContentThis package includes the codes to replicate the experiments in the paper as following:* Graph: This folder includes the codes to use AST Parser JDT to parse the source codes in a project and construct the numerical samples that are represented with the four features (i.e. architectural feature, project feature, functional feature, and exception feature). The entry is App.java. You can use it to construct samples for any other Java project.* Dataset: For the convenience to replicate, we release the extracted samples from the 29 popular Java projects. This folder includes the data we adopt in the experiments.* Train: This folder includes the scripts to train the binary classification model with AutoGluon.* FeatImportanceAnalysis: This folder includes the scripts to calculate the importance score of each feature with AutoGluon.## Replication Instruction### Package Requirement* Python 3.8* AutoGluon 0.15For more details about the package requiment, we provide a package list in requirements.txt. You can type the command pip install -r requirements.txt to easily construct the experiment environment.### Exepriment Instruction:* Train and apply the classification model with a specific scenario (i.e. one of across-project, intra-project, and intra-project+) as following, where PROJECTID is the id of the target project that request for the recommendation. This step will generate a binary-classification model as well as the predicted catch probability on each method for each its related exception. * Across-project recommendation: Run the across.py script in train directory as python across.py --train_id=PROJECTID. * Intra-project recommendation: Run the intra.py script in train directory as python intra.py --train_id=PROJECTID. * Intra-project+ recommendation: Run the intraplus.py script in train directory as python intraplus.py --train_id=PROJECTID.* Run the top.py script in train directory to get the recommendation result and the recommendation performance in terms of SuccRate@1,2,3.* Other: * You can run the calcImportance.py script in FeatImportanceAnalysis directory to get the importance score of each type of featres with AutoGluon. * You can specify the training time limit and the output directory for AutoGluon through changing the value of time_limites and oupput_directory in across.py, intra.py, intraplus.py, and calcImportance.py.######## Introduction of Simple Demo (demo.zip) ######### READMEThis is the simple demo of the paper "Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective". We release this demo to simply illustrate the preliminary use flow of EHAdvisor.Given a project and a specific method, this demo will output the location recommendations to handle the exceptions thrown by this method on all its call chains.## Usage1. Obtain a base binary-classification model. You can use a pre-trained model we release on our webpage or train a model with python trainModel.py (please refer to the replication package instruction for the details about training).2. Run python tools.py to execute the demo script.3. Input the path to the source code of the target project after the script prompts "Please input the path of the source folder:", e.g., shiro.4. Input the path to the description text of the target project after the script prompts "Please input the path of the project document file:", e.g., shiro/readme.txt.5. Input the method requiring for the location recommendations after the scripts promts "Please input the name of method:", e.g., org.apache.shiro.cache.ehcache.EhCacheManager$ensureCacheManager.6. Then, you will get the location recommendations to handle the exceptions thrown by this method on all its call chains.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This Climate Data Record (CDR) of monthly mean High Resolution Infrared Radiation Sounder (HIRS) Outgoing Longwave Radiation (OLR) flux at the top of the atmosphere in all sky conditions is on a 2.5 degree x 2.5 degree grid with global coverage from January 1979 to the present and continuing monthly. Grid dimensions are 144 x 72, with 10,368 total grids over the globe. The data set was derived using a multispectral HIRS OLR algorithm on the HIRS level 1b dataset from the TIROS-N to NOAA-19 series satellites with inter-satellite and radiance calibration adjustments employing an empirical diurnal model for the monthly mean derivation. The data file format is netCDF-4 with CF metadata, and it is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This Climate Data Record (CDR) contains total solar irradiance (TSI) as a function of time created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Total solar irradiance is the total, spectrally integrated energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged TSI data range from 1882 to the present, and annual-averaged TSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.