Flood susceptibility modelling using advanced ensemble machine learning models

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

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Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees

Geocarto International, Journal Year: 2021, Volume and Issue: 37(19), P. 5479 - 5496

Published: April 23, 2021

Historical exploration of flash flood events and producing flash-flood susceptibility maps are crucial steps for decision makers in disaster management. In this article, classification regression tree (CART) methodology its ensemble models random forest (RF), boosted trees (BRT) extreme gradient boosting (XGBoost) were implemented to create a map the Bâsca Chiojdului River Basin, one areas Romania that is constantly exposed floods. The torrential including 962 delineated from orthophotomaps field observations. Furthermore, set conditioning forces explain floods was constructed which included aspect, land use cover (LULC), hydrological soil groups lithology, slope, topographic wetness index (TWI), position (TPI), profile curvature, convergence stream power (SPI). All indicated slope as most important factor triggering occurrence. highest area under curve (AUC) achieved by RF model (AUC = 0.956), followed BRT 0.899), XGBoost 0.892) CART 0.868), respectively. results showed central part river basin, covers approximately 30% study area, more susceptible flooding.

Language: Английский

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Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

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

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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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Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India

Sustainable Cities and Society, Journal Year: 2021, Volume and Issue: 68, P. 102773 - 102773

Published: Feb. 15, 2021

Language: Английский

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Urban flooding risk assessment based on GIS- game theory combination weight: A case study of Zhengzhou City

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 77, P. 103080 - 103080

Published: May 29, 2022

Language: Английский

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A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 1741 - 1777

Published: Jan. 1, 2021

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in Kysuca river basin, Slovakia. For this reason, previous were analyzed with 16 conditioning factors. Landslide inventory was divided into training (70% of locations) validating dataset (30% locations). heuristic approach Fuzzy Decision Making Trial Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes (NBC) Extreme Gradient Boosting (XGBoost), respectively. results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% 40.3% total basin area had very high LS corresponding FDEMATEL-ANP, FR, LR, RFC, NBC XGBoost model, analysis revealed RFC most accurate model (overall accuracy 98.3% AUC 97.0%). Besides, FDEMATEL-ANP 93.8% 92.4%) better prediction capability than FR 86.9% 86.1%), LR 90.5% 91.2%), machine learning 76.3% 90.9%) even deep 92.3% 87.1%) models. outweighed models, suggests methods should be tested out before directly applying

Language: Английский

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

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

Published: Sept. 30, 2022

Language: Английский

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Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

Advances in Space Research, Journal Year: 2023, Volume and Issue: 72(2), P. 426 - 443

Published: March 21, 2023

Language: Английский

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Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

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

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