Ensemble machine learning paradigms in hydrology: A review

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 598, P. 126266 - 126266

Published: April 1, 2021

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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Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 592, P. 125815 - 125815

Published: Nov. 30, 2020

Language: Английский

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Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances

Hydrology, Journal Year: 2023, Volume and Issue: 10(7), P. 141 - 141

Published: June 30, 2023

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions people worldwide. Due to its ability accurately anticipate successfully mitigate the effects floods, flood modeling is an important approach in control. This study provides a thorough summary modeling’s current condition, problems, probable future directions. The includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing GIS, artificial intelligence machine learning, multiple-criteria decision analysis. Additionally, it covers heuristic metaheuristic techniques employed evaluation examines advantages disadvantages various models, evaluates how well they are able predict course impacts floods. constraints data, unpredictable nature model, complexity model some difficulties that must overcome. In study’s conclusion, prospects for development advancement field discussed, including use advanced technologies integrated models. To improve risk management lessen society, report emphasizes necessity ongoing research modeling.

Language: Английский

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Hydrogeochemical Evaluation of Groundwater Aquifers and Associated Health Hazard Risk Mapping Using Ensemble Data Driven Model in a Water Scares Plateau Region of Eastern India

Exposure and Health, Journal Year: 2022, Volume and Issue: 15(1), P. 113 - 131

Published: April 23, 2022

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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Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms

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

Published: Feb. 10, 2023

Language: Английский

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A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale

Environmental Processes, Journal Year: 2024, Volume and Issue: 11(1)

Published: Feb. 13, 2024

Abstract This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial Evaluation Laboratory (DEMATEL) methods for assessing flood risk in Kosynthos River basin northeastern Greece. The study aims to address challenges arising from data limitations provide decision-makers with effective management strategies. integration of DEMATEL is crucial, providing a robust framework considers interdependencies among factors, particularly regions where conventional numerical modeling faces difficulties. preferred over other due its proficiency handling qualitative ability account interactions studied factors. proposed method based on two developed causality diagrams. first diagram crucial hazard absence data. second offers multidimensional analysis, considering criteria. Notably, referring vulnerability can adapt local (or national) conditions, ill-defined nature vulnerability. Given identifies highly hazardous vulnerable areas, not only provides essential insights but also supports formulating approaches mitigate impacts communities infrastructure. Validation includes sensitivity analysis comparison historical Effective weights derived enhance precision Flood Hazard Index (FHI) Vulnerability (FVI).

Language: Английский

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