GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

Ecological Indicators, Journal Year: 2020, Volume and Issue: 117, P. 106620 - 106620

Published: June 21, 2020

Flood is a devastating natural hazard that may cause damage to the environment infrastructure, and society. Hence, identifying susceptible areas flood an important task for every country prevent such dangerous consequences. The present study developed framework flood-prone of Topľa river basin, Slovakia using geographic information system (GIS), multi-criteria decision making approach (MCDMA), bivariate statistics (Frequency Ratio (FR), Statistical Index (SI)) machine learning (Naïve Bayes Tree (NBT), Logistic Regression (LR)). To reach goal, different physical-geographical factors (criteria) were integrated mapped. access relationship interdependences among criteria, decision-making trial evaluation laboratory (DEMATEL) analytic network process (ANP) used. Based on experts' decisions, DEMATEL-ANP model was used compute relative weights criteria GIS-based linear combination performed derive susceptibility index. Separately, index computation through NBT-FR NBT-SI hybrid models assumed, in first stage, estimation weight each class/category conditioning factor SI FR integration these values NBT algorithm. application LR stand-alone required calculation by analysing their spatial relation with location historical events. revealed very high classes covered between 20% 47% area, respectively. validation results, past points, highlighted most performant Area Under ROC curve higher than 0.97, accuracy 0.922 value HSS 0.844. presented methodological identification can serve as alternative updating preliminary risk assessment based EU Floods Directive.

Language: Английский

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Integrated machine learning methods with resampling algorithms for flood susceptibility prediction

The Science of The Total Environment, Journal Year: 2019, Volume and Issue: 705, P. 135983 - 135983

Published: Dec. 6, 2019

Language: Английский

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Evaluating urban flood risk using hybrid method of TOPSIS and machine learning

International Journal of Disaster Risk Reduction, Journal Year: 2021, Volume and Issue: 66, P. 102614 - 102614

Published: Oct. 1, 2021

With the growth of cities, urban flooding has increasingly become an issue for regional and national governments. The destructive effects floods are magnified in cities. Accurate models flood susceptibility required to mitigate this hazard mitigation build resilience In paper, we evaluate riskin Jiroft city, Iran, using a combination machine learning decision-making methods. Flood maps were created three state-of-the-art methods (support vector machine, random forest, boosted regression tree). metadata supporting our analysis comprises 218 inundation points variety derived factors: slope aspect, elevation, angle, rainfall, distance streets, rivers, land use/land cover, drainages, drainage density, curve number. We then employed TOPSIS tool vulnerability analysis, which is based on socio-economic factors such as building population history, conditions. Finally, risk map maps. Of tested, forest model yielded most accurate map. results indicate that density drainages important modeling. As might be expected, areas with high or very vulnerable flooding. These show mapping provide insights priority planning management, especially limited hydrological data.

Language: Английский

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Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

Stochastic Environmental Research and Risk Assessment, Journal Year: 2020, Volume and Issue: 34(12), P. 2277 - 2300

Published: Sept. 4, 2020

Language: Английский

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Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 591, P. 125552 - 125552

Published: Sept. 20, 2020

Language: Английский

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Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models

Water, Journal Year: 2020, Volume and Issue: 12(7), P. 1995 - 1995

Published: July 14, 2020

Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil susceptibility hazard assessment utmost importance enhancing mitigation policies laws. This paper proposes novel machine learning (ML) the mapping water soil. weighted subspace random forest (WSRF), Gaussian process with radial basis function kernel (Gaussprradial), naive Bayes (NB) ML methods were used in prediction susceptibility. Data included 227 samples non-erosion locations through field surveys advance spatial distribution using predictive factors. In this study, 19 effective factors considered. critical selected simulated annealing feature selection (SAFS). aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from stream, drainage density, fault normalized difference vegetation index (NDVI), hydrologic group, texture, lithology. dataset cells (70% training 30% testing) randomly prepared assess robustness different models. functional relevance between was computed evaluated metrics, including accuracy, kappa coefficient, probability detection (POD). accuracies WSRF, Gaussprradial, NB 0.91, 0.88, 0.85, respectively, testing data; 0.82, 0.76, 0.71, coefficient; 0.94, POD. However, models, especially had an acceptable performance regarding producing maps. Maps produced most robust can be useful tool management, watershed conservation, reduction loss.

Language: Английский

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Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 289, P. 112449 - 112449

Published: April 1, 2021

Language: Английский

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Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Geoscience Frontiers, Journal Year: 2022, Volume and Issue: 13(5), P. 101425 - 101425

Published: June 17, 2022

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard mitigation strategy includes assessing individual hazards as well their interactions. However, with the rapid development artificial intelligence technology, techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, study proposes mapping framework using classical deep algorithm Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations Google Earth images, extensive field surveys, topography, hydrology, environmental data sets train validate proposed CNN method. Next, method assessed in comparison conventional logistic regression k-nearest neighbor methods several objective criteria, i.e., coefficient determination, overall accuracy, mean absolute error root square error. Experimental results show that outperforms algorithms predicting probability floods, flows landslides. Finally, maps three are combined create map. It can be observed from map 62.43% area prone hazards, while 37.57% harmless. hazard-prone areas, 16.14%, 4.94% 30.66% susceptible landslides, respectively. terms concurrent 0.28%, 7.11% 3.13% joint occurrence floods flow, respectively, whereas, 0.18% subject all hazards. The benefit engineers, disaster managers local government officials involved sustainable land mitigation.

Language: Английский

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Flood susceptible prediction through the use of geospatial variables and machine learning methods

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 617, P. 129121 - 129121

Published: Jan. 13, 2023

Language: Английский

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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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