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

Flood susceptibility modelling using advanced ensemble machine learning models DOI Creative Commons
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato

et al.

Geoscience Frontiers, Journal Year: 2020, Volume and Issue: 12(3), P. 101075 - 101075

Published: Oct. 5, 2020

Floods are one of nature's most destructive disasters because the immense damage to land, buildings, and human fatalities. It is difficult forecast areas that vulnerable flash flooding due dynamic complex nature floods. Therefore, earlier identification flood susceptible sites can be performed using advanced machine learning models for managing disasters. In this study, we applied assessed two new hybrid ensemble models, namely Dagging Random Subspace (RS) coupled with Artificial Neural Network (ANN), Forest (RF), Support Vector Machine (SVM) which other three state-of-the-art modelling susceptibility maps at Teesta River basin, northern region Bangladesh. The application these includes twelve influencing factors 413 current former points, were transferred in a GIS environment. information gain ratio, multicollinearity diagnostics tests employed determine association between occurrences influential factors. For validation comparison ability predict statistical appraisal measures such as Freidman, Wilcoxon signed-rank, t-paired Receiver Operating Characteristic Curve (ROC) employed. value Area Under (AUC) ROC was above 0.80 all models. modelling, model performs superior, followed by RF, ANN, SVM, RS, then several benchmark approach solution-oriented outcomes outlined paper will assist state local authorities well policy makers reducing flood-related threats also implementation effective mitigation strategies mitigate future damage.

Language: Английский

Citations

427
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 DOI Creative Commons
Sk Ajim Ali, Farhana Parvin, Quoc Bao Pham

et al.

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

Citations

264
Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms DOI Creative Commons
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(21), P. 3568 - 3568

Published: Oct. 31, 2020

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone flash a crucial step in hazard management. In present study, Kalvan watershed Markazi Province, Iran, was chosen evaluate susceptibility modeling. Thus, detect flood-prone zones this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel (PRF), regularized (RRF), extremely randomized trees (ERT). Fifteen climatic geo-environmental variables used as inputs models. The results showed that ERT optimal model with an area under curve (AUC) value 0.82. rest models’ AUC values, i.e., RRF, PRF, RF, BRT, 0.80, 0.79, 0.78, 0.75, respectively. model, areal coverage very high moderate susceptible 582.56 km2 (28.33%), portion associated low zones. It concluded topographical hydrological parameters, e.g., altitude, slope, rainfall, river’s distance, effective parameters. will play vital role planning implementation mitigation strategies region.

Language: Английский

Citations

196
Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms DOI
Swapan Talukdar,

Bonosri Ghose,

Shahfahad

et al.

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

Published: Sept. 4, 2020

Language: Английский

Citations

184
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

94
A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India DOI
Rajib Mitra, Jayanta Das

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(6), P. 16036 - 16067

Published: Sept. 30, 2022

Language: Английский

Citations

77
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

75
Applications of Stacking/Blending ensemble learning approaches for evaluating flash flood susceptibility DOI Creative Commons
Jing Yao, Xiaoxiang Zhang, Weicong Luo

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102932 - 102932

Published: July 30, 2022

Flash floods are a type of catastrophic disasters which cause significant losses life and property worldwide. In recent years, machine learning techniques have become powerful tools for evaluating flash flood susceptibility. This research applies stacking blending ensemble approaches to assess the potential in Jiangxi, China. Four base models – linear regression, K-nearest neighbours, support vector machine, random forest adopted build two models. All evaluated by three metrics (accuracy, true positive rate, area under receiver operating characteristic curve) compared with Bayesian approach. The results suggest that approach is superior all other models, has then been selected evaluate vulnerability catchments Jiangxi. derived maps susceptibility over half province, terms either or number catchments, prone floods, particular north, northeast south. These empirical findings can help develop plans disaster prevention control, as well improving public knowledge hazards.

Language: Английский

Citations

71
An improved MCDM combined with GIS for risk assessment of multi-hazards in Hong Kong DOI
Hai‐Min Lyu, Zhen‐Yu Yin

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 91, P. 104427 - 104427

Published: Jan. 28, 2023

Language: Английский

Citations

64