Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins

Geocarto International, Journal Year: 2020, Volume and Issue: 37(9), P. 2541 - 2560

Published: Sept. 28, 2020

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change human interventions. Hazard mapping is essential for local policymaking prevention, planning the mitigation actions, also adaptation extremes. This study proposes novel predictive models susceptibility flood erosion. Furthermore, this elaborates on prioritizing existing sub-basins in terms of erosion susceptibility. A comparative analysis generalized linear model (GLM), flexible discriminate analyses (FDA), multivariate adaptive regression spline (MARS), random forest (RF), their ensemble performed ensure highest performance. priority sensitivity was determined based best model. results showed that GLM, FDA, MARS, RF, had an area under curve (AUC) 0.91, 0.92, 0.89, 0.93 0.94, respectively, modeling Also, AUC 0.93, 0.96, 0.97, determining Priority assessment model, approach, indicated SW3 SW5 were found have high soil erosion, respectively.

Language: Английский

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Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment

Journal of Environmental Management, Journal Year: 2020, Volume and Issue: 265, P. 110485 - 110485

Published: April 20, 2020

Language: Английский

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DEM resolution effects on machine learning performance for flood probability mapping

Journal of Hydro-environment Research, Journal Year: 2021, Volume and Issue: 40, P. 1 - 16

Published: Nov. 9, 2021

Floods are among the devastating natural disasters that occurred very frequently in arid regions during last decades. Accurate assessment of flood susceptibility mapping is crucial sustainable development. It helps respective authorities to prevent as much possible their irreversible consequences. The Digital Elevation Model (DEM) spatial resolution one most base layer factors for modeling Flood Probability Maps (FPMs). Therefore, main objective this study was assess influence DEMs 12.5 m (ALOS PALSAR) and 30 (ASTER) on accuracy probability prediction using three machine learning models (MLMs), including Random Forest (RF), Artificial Neural Network (ANN), Generalized Linear (GLM). This selected 14 causative independent variables, 220 locations were dependent variables. Dependent variables divided into training (70%) validation (30%) modeling. Receiver Operating Characteristic Curve (ROC), Kappa index, accuracy, other statistical criteria used evaluate models' accuracy. results showed resolving DEM alone cannot significantly affect regardless applied MLM independently model performance In contrast, such altitude, precipitation, distance from river have a considerable impact floods region. Also, evaluation RF (AUC12.5,30m = 0.983, 0.975) more accurate preparing FPM than ANN 0.949, 0.93) GLM 0.965, 0.949) models. study's solution-oriented findings might help water managers decision-makers make effective adaptation mitigation measures against potential flooding.

Language: Английский

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Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Water, Journal Year: 2021, Volume and Issue: 13(21), P. 3115 - 3115

Published: Nov. 4, 2021

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used obtain risk indices for Galikesh River basin, Northern Iran. With aid Landsat 8 satellite imagery Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 (Elevation (El), Slope (Sl), Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Water (NDWI), Topographic Wetness (TWI), Distance (RD), Waterway Density (WRD), Soil Texture (ST]), Maximum One-Day Precipitation (M1DP)) were provided. next step, all these imported into ArcMap 10.8 (Esri, West Redlands, software index normalization better visualize graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which robust data mining technique, compute importance degree each hazard map. According results, WRD, RD, M1DP, El accounted about 68.27 percent total risk. Among indices, WRD containing 23.8 greatest impact on floods. FRM mapping, 21 18 areas stood at higher highest areas, respectively.

Language: Английский

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Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

Geocarto International, Journal Year: 2021, Volume and Issue: 37(25), P. 7462 - 7487

Published: Aug. 31, 2021

This study presents two machine learning models, namely, the light gradient boosting (LightGBM) and categorical (CatBoost), for first time predicting flash flood susceptibility (FFS) in Wadi System (Hurghada, Egypt). A inventory map with 445 sites was produced randomly divided into groups training (70%) testing (30%). Fourteen controlling factors were selected evaluated their relative importance occurrence prediction. The performance of models assessed using various indexes comparison to common random forest (RF) method. results show areas under receiver operating characteristic curves (AUROC) above 97% all that LightGBM outperforms other terms classification metrics processing time. developed FFS maps demonstrate highly populated are most susceptible floods. present proves employed algorithms (LightGBM CatBoost) can be efficiently used mapping.

Language: Английский

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