Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms DOI Open Access
Shavan Askar, Sajjad Zeraat Peyma, Mohanad Mohsen Yousef

et al.

Water, Journal Year: 2022, Volume and Issue: 14(19), P. 3062 - 3062

Published: Sept. 28, 2022

Flooding is one of the most prevalent types natural catastrophes, and it can cause extensive damage to infrastructure environment. The primary method flood risk management susceptibility mapping (FSM), which provides a quantitative assessment region’s vulnerability flooding. objective this study develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), harmony search (HS), with decision table classifier (DTB). proposed were applied in province Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images used monitoring (on 27 July 2019), 160 occurrence locations prepared modeling. For training validation datasets, coupled 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power (SPI), rainfall, normalized difference vegetation (NDVI)). certainty factor (CF) approach was determine spatial association between effective floods, resulting weights employed modeling inputs. According pairwise consistency technique, NDVI altitude are significant factors area under receiver operating characteristic (AUROC) curve evaluate accuracy effectiveness models. DTB-GA model found be accurate (AUC = 0.889), followed DTB-PSO 0.844) DTB-HS 0.812). This research’s hybrid provide reliable estimate risk, maps early-warning control systems.

Language: Английский

Flooding and its relationship with land cover change, population growth, and road density DOI Creative Commons

Mahfuzur Rahman,

Chen Ningsheng,

Golam Iftekhar Mahmud

et al.

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(6), P. 101224 - 101224

Published: May 5, 2021

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These are believed to be associated with land use changes and climate variability. However, identifying the factors that lead flooding is challenging. This study mapped flood susceptibility in northeast region of using Bayesian regularization back propagation (BRBP) neural network, classification regression trees (CART), a statistical model (STM) evidence belief function (EBF), their ensemble models (EMs) for three time periods (2000, 2014, 2017). The accuracy machine learning algorithms (MLAs), STM, EMs were assessed by considering area under curve—receiver operating characteristic (AUC-ROC). Evaluation levels aforementioned revealed EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone other analyzed. Furthermore, this investigated relationships among cover change (LCC), population growth (PG), road density (RD), relative (RCF) areas period between 2000 2017. results showed very high increased 19.72% 2017, while PG rate 51.68% over same period. Pearson correlation coefficient RCF RD was calculated 0.496. findings highlight significant association floods causative factors. could valuable policymakers resource managers they can improvements management reduction damage risks.

Language: Английский

Citations

145
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms DOI Open Access
Asish Saha, Subodh Chandra Pal, Alireza Arabameri

et al.

Water, Journal Year: 2021, Volume and Issue: 13(2), P. 241 - 241

Published: Jan. 19, 2021

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome natural hazard phenomena. With mind, we evaluated prediction performance FS Koiya River basin, Eastern India. The present research work was done through preparation sophisticated inventory map; eight conditioning variables were selected based on topography and hydro-climatological condition, by applying novel ensemble approach hyperpipes (HP) support vector regression (SVR) machine learning (ML) algorithms. HP-SVR also compared with stand-alone ML algorithms HP SVR. In relative importance variables, distance river most dominant factor for occurrences followed rainfall, land use cover (LULC), normalized difference vegetation index (NDVI). validation accuracy assessment maps five popular statistical methods. result evaluation showed that optimal model (AUC = 0.915, sensitivity 0.932, specificity 0.902, 0.928 Kappa 0.835) assessment, 0.885) SVR 0.871).

Language: Английский

Citations

141
Novel ensemble machine learning models in flood susceptibility mapping DOI
Pankaj Prasad, Victor J. Loveson, Bappa Das

et al.

Geocarto International, Journal Year: 2021, Volume and Issue: 37(16), P. 4571 - 4593

Published: Feb. 19, 2021

The research aims to propose the new ensemble models by combining machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with base classifier adabag (AB) for flood susceptibility mapping (FSM). proposed were implemented in central west coast of India, which is vulnerable events. For inventory mapping, a total 210 localities identified. Twelve effective factors selected using boruta algorithm FSM. area under receiver operating characteristics (AUROC) curve other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), absolute (MAE)) employed estimate compare success rate approaches. validation results individual terms AUC value AB (92.74%) >RF (91.50%) >BRT (90.75%) >LB (89.07%) >NSC (88.97%) >KNN (83.88%), whereas showed that AB-RF (94%) was highest prediction efficiency followed by, AB-KNN (93.33%), AB-NSC (93.02%), AB-LB (92.83%), AB-BRT (92.64%). outcomes established more appropriate increase accuracy different single models. Therefore, this study can be useful proper planning management hazard alike geographic environment.

Language: Английский

Citations

106
A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India DOI

Dipankar Ruidas,

Rabin Chakrabortty, Abu Reza Md. Towfiqul Islam

et al.

Environmental Earth Sciences, Journal Year: 2022, Volume and Issue: 81(5)

Published: Feb. 21, 2022

Language: Английский

Citations

93
Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis DOI
Romulus Costache,

Tran Trung Tin,

Alireza Arabameri

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127747 - 127747

Published: March 24, 2022

Language: Английский

Citations

74
Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling DOI Creative Commons
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2023, Volume and Issue: 14(1)

Published: May 4, 2023

This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those rainfall-runoff model, different training dataset sizes utilized performance assessment. Ten independent factors assessed. An inventory map approximately 850 sites is based on several post-flood surveys. randomly split between (70%) testing (30%). AUC-ROC 97.9%, 99.5%, 99.5% CatBoost, RF, respectively. FSMs developed by methods show good agreement terms an extension flood inundation using model. models' showed 10–13% total area be highly susceptible flooding, consistent RRI's map. that downstream areas (both urbanized agricultural) under high very levels susceptibility. Additionally, input datasets tested determine least number data points having acceptable reliability. demonstrate can realistically predict FSMs, regardless samples.

Language: Английский

Citations

52
Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms DOI
Mostafa Riazi, Khabat Khosravi, Kaka Shahedi

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 871, P. 162066 - 162066

Published: Feb. 10, 2023

Language: Английский

Citations

50
A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment DOI Creative Commons
A. Habibi, M. R. Delavar,

Mohammad Sadegh Sadeghian

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 122, P. 103401 - 103401

Published: July 14, 2023

Flash floods are among the world most destructive natural disasters, and developing optimum hybrid Machine Learning (ML) models for flash flood susceptibility (FFS) modeling remains a challenge. This study proposed novel intelligence algorithms based on of several ensemble ML (i.e., Bagged Flexible Discriminant Analysis (BAFDA), Extreme Gradient Boosting (XBG), Rotation Forest (ROF) Boosted Generalized Additive Model (BGAM)) wrapper-based factor optimization Recursive Feature Elimination (RFE) Boruta) to improve accuracy FFS mapping at Neka-Haraz watershed in Iran. In addition, Random Search (RS) method is meta-optimization developed hyper-parameters. considers 20 conditioning factors (CgFs) 380 non-flood locations create geospatial database. The performance each model was evaluated by area under receiver operating characteristic (ROC) curve (AUC) validation methods, such as efficiency. demonstrated good performance, with BGAM-Boruta achieving highest (AUC = 0.953, Efficiency 0.910), followed ROF-Boruta 0.952), ROF-RFE 0.951), BAFDA-Boruta 0.950), BGAM-RFE ROF 0.949), BGAM 0.948), BAFDA-RFE 0.943), XGB-Boruta BAFDA 0.939), XGB-RFE 0.938) XGB 0.911). model, regional coverage about 46% high very areas. Moreover, revealed that distance river, slope, rainfall, altitude, road CgFs significant this region.

Language: Английский

Citations

43
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
Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping DOI Creative Commons
Seyed Vahid Razavi-Termeh, Ali Pourzangbar, Abolghasem Sadeghi‐Niaraki

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104357 - 104357

Published: Jan. 14, 2025

Language: Английский

Citations

2