Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia DOI Creative Commons
Ahmed M. Al‐Areeq, Sani I. Abba, Mohamed A. Yassin

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(21), P. 5515 - 5515

Published: Nov. 2, 2022

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims demonstrate predictive ability four ensemble algorithms for assessing flood risk. Bagging (BE), logistic model tree (LT), kernel support vector machine (k-SVM), k-nearest neighbour (KNN) used in this zoning Jeddah City, Saudi Arabia. The 141 locations have been identified research area based on interpretation aerial photos, historical data, Google Earth, field surveys. For purpose, 14 continuous factors different categorical examine their effect flooding area. dependency analysis (DA) was analyse strength predictors. comprises two input variables combination (C1 C2) features sensitivity selection. under-the-receiver operating characteristic curve (AUC) root mean square error (RMSE) were utilised determine accuracy a good forecast. validation findings showed that BE-C1 performed best terms precision, accuracy, AUC, specificity, as well lowest (RMSE). performance skills overall models proved reliable with range AUC (89–97%). can also be beneficial flash forecasts warning activity developed by disaster

Language: Английский

Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development DOI Creative Commons
Youssef Bammou, Brahim Benzougagh, Abdessalam Ouallali

et al.

Journal of African Earth Sciences, Journal Year: 2024, Volume and Issue: 213, P. 105229 - 105229

Published: March 11, 2024

Gully erosion is a widespread environmental danger, threatening global socio-economic stability and sustainable development. This study comprehensively applied seven machine learning (ML) models including SVM, KNN, RF, XGBoost, ANN, DT, LR, evaluated gully susceptibility in the Tensift catchment predict it within Haouz plain, Morocco. To ensure reliability of findings, employed robust combination inventory, sentinel images, Digital Surface Model. Eighteen predictors, encompassing topographical, geomorphological, environmental, hydrological factors, were selected after multicollinearity analyses. The revealed that approximately 28.18% at very high risk erosion. Furthermore, 15.13% 31.28% are categorized as low respectively. These findings extend to where 7.84% surface area highly risking erosion, while 18.25% 55.18% characterized areas. gauge performance ML models, an array metrics specificity, precision, sensitivity, accuracy employed. highlights XGBoost KNN most promising achieving AUC ROC values 0.96 0.93 test phase. remaining namely RF (AUC = 0.89), LR 0.80), SVM 0.81), DT 0.86), ANN 0.78), also displayed commendable performance. novelty this research its innovative approach combat through cutting edge offering practical solutions for watershed conservation, management, prevention land degradation. insights invaluable addressing challenges posed by region, beyond geographical boundaries can be used defining appropriate mitigation strategies local national scale.

Language: Английский

Citations

24
Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin DOI Creative Commons
Yogesh Bhattarai, Sunil Duwal, Sanjib Sharma

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Feb. 9, 2024

Floods pose devastating effects on the resiliency of human and natural systems. flood risk management challenges are typically complicated in transboundary river basin due to conflicting objectives between multiple countries, lack systematic approaches data monitoring sharing, limited collaboration developing a unified system for hazard prediction communication. An open-source, low-cost modeling framework that integrates open-source models can help improve our understanding susceptibility inform design equitable strategies. This study datasets machine -learning techniques quantify across data-scare basin. The analysis focuses Gandak River Basin, spanning China, Nepal, India, where damaging recurring floods serious concern. is assessed using four widely used learning techniques: Long-Short-Term-Memory, Random Forest, Artificial Neural Network, Support Vector Machine. Our results exhibit improved performance Network Machine predicting maps, revealing higher vulnerability southern plains. demonstrates remote sensing prediction, mapping, environment.

Language: Английский

Citations

18
Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm DOI Creative Commons
Himan Shahabi, Ataollah Shirzadi,

Somayeh Ronoud

et al.

Geoscience Frontiers, Journal Year: 2020, Volume and Issue: 12(3), P. 101100 - 101100

Published: Nov. 24, 2020

Flash floods are responsible for loss of life and considerable property damage in many countries. Flood susceptibility maps contribute to flood risk reduction areas that prone this hazard if appropriately used by land-use planners emergency managers. The main objective study is prepare an accurate map the Haraz watershed Iran using a novel modeling approach (DBPGA) based on Deep Belief Network (DBN) with Back Propagation (BP) algorithm optimized Genetic Algorithm (GA). For task, database comprising ten conditioning factors 194 locations was created One-R Attribute Evaluation (ORAE) technique. Various well-known machine learning optimization algorithms were as benchmarks compare prediction accuracy proposed model. Statistical metrics include sensitivity, specificity accuracy, root mean square error (RMSE), area under receiver operatic characteristic curve (AUC) assess validity result shows model has highest goodness-of-fit (AUC = 0.989) 0.985), validation dataset it outperforms benchmark models including LR (0.885), LMT (0.934), BLR (0.936), ADT (0.976), NBT (0.974), REPTree (0.811), ANFIS-BAT (0.944), ANFIS-CA (0.921), ANFIS-IWO (0.939), ANFIS-ICA (0.947), ANFIS-FA (0.917). We conclude DBPGA excellent alternative tool predicting flash other regions floods.

Language: Английский

Citations

133
Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks DOI Creative Commons
Mohammad Ahmadlou, A’kif Al-Fugara, Abdel Rahman Al‐Shabeeb

et al.

Journal of Flood Risk Management, Journal Year: 2020, Volume and Issue: 14(1)

Published: Dec. 18, 2020

Abstract Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, combination data‐driven techniques with remote sensing (RS) geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining multilayer perceptron (MLP) autoencoder models to produce maps two areas located in Iran India. For cases, nine, twelve factors were considered as predictor variables mapping, respectively. The prediction capability proposed was compared that traditional MLP through area under receiver operating characteristic (AUROC) criterion. AUROC curve autoencoder‐MLP were, respectively, 75 90, 74 93% training phase 60 91, 81 97% testing phase, India results suggested outperformed and, therefore, can be powerful other studies

Language: Английский

Citations

114
Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning DOI
Romulus Costache, Mihnea Cristian Popa, Dieu Tien Bui

et al.

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 585, P. 124808 - 124808

Published: March 9, 2020

Language: Английский

Citations

113
Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment DOI
Romulus Costache, Quoc Bao Pham, Mohammadtaghi Avand

et al.

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

Published: April 20, 2020

Language: Английский

Citations

111
Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India DOI Open Access
Shruti Sachdeva, Bijendra Kumar

Stochastic Environmental Research and Risk Assessment, Journal Year: 2020, Volume and Issue: 35(2), P. 287 - 306

Published: Oct. 6, 2020

Language: Английский

Citations

81
Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study DOI
Amit Kumar Saha, Sonam Agrawal

Nanotechnology for Environmental Engineering, Journal Year: 2020, Volume and Issue: 5(2)

Published: May 10, 2020

Language: Английский

Citations

73
Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh DOI
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato

et al.

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 28(26), P. 34450 - 34471

Published: March 2, 2021

Language: Английский

Citations

72
Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping DOI Open Access
Romulus Costache, Phuong Thao Thi Ngo, Dieu Tien Bui

et al.

Water, Journal Year: 2020, Volume and Issue: 12(6), P. 1549 - 1549

Published: May 29, 2020

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In regard, geospatial database the flood with 178 locations 10 predictors prepared used AHP FR were processing coding into numeric format, whereas DNN, which is powerful state-of-the-art probabilistic machine leaning, employed build an inference model. The reliability models verified help Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), several statistical measures. result shows that two ensemble models, DNN-AHP DNN-FR, are capable predicting future areas accuracy higher than 92%; therefore, they tool studies.

Language: Английский

Citations

71