Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 873, P. 162285 - 162285

Published: Feb. 17, 2023

Language: Английский

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Flood susceptibility mapping contributes to disaster risk reduction: A case study in Sindh, Pakistan

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 108, P. 104503 - 104503

Published: April 23, 2024

Floods are a widespread and damaging natural phenomenon that causes harm to human lives, resources, property has agricultural, eco-environmental, economic impacts. Therefore, it is crucial perform flood susceptibility mapping (FSM) identify susceptible zones mitigate reduce damage. This study assessed the damage caused by 2022 flash in Sindh identified flood-susceptible based on frequency ratio (FR) analytical hierarchy process (AHP) models. Flood inventory maps were generated, containing 150 sampling points, which manually selected from Landsat imagery. The points split into 70% for training 30% validating results. Furthermore, fourteen conditioning factors considered analysis developing FSM. final FSM categorized five zones, representing levels very low high. results areas under high Ghotki (FR 4.42% AHP 5.66%), Dadu 21.40% 21.29%), Sanghar 6.81% 6.78%). Ultimately, accuracy was evaluated using receiver operating characteristics area curve method, resulting 82%, 83%), 91%, 90%), 96%, 95%). enhances scientific understanding of impacts across diverse regions emphasizes importance accurate informed decision-making. findings provide valuable insights supportive policymakers, agricultural planners, stakeholders engaged risk management adverse consequences floods.

Language: Английский

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Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping

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

Published: Jan. 14, 2025

Language: Английский

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Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

Weather and Climate Extremes, Journal Year: 2023, Volume and Issue: 41, P. 100595 - 100595

Published: July 29, 2023

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), artificial bee colony (ABC) algorithms). Initially, integration optical (Landsat-8) radar (Sentinel-1) satellite images were used monitor flooded areas during July 2022 monsoon Kazerun region, Iran. A total 1358 occurrence points considered from monitored areas; 70% (952 points) 30% (406 evaluating models, respectively. Based on thirteen spatial criteria influencing floods, four models ((XGBoost, XGBoost-ABC, XGBoost-BFO, XGBoost-CS)) generate map (FSM). According results, XGBoost-CS (area under curve (AUC) = 0.96), XGBoost-BFO (AUC 0.953), XGBoost-ABC 0.941), XGBoost 0.939) greater accuracy modeling, The results indicated coupled with metaheuristic (XGBoost-ABC, XGBoost-CS) exhibited higher than standalone model.

Language: Английский

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Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Natural Hazards Research, Journal Year: 2024, Volume and Issue: 4(1), P. 32 - 45

Published: Jan. 4, 2024

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, country's efforts in planning managing flood risks remain insufficient, especially vulnerable Mohana-Khutiya River. Therefore, this study focused on River utilizes Maximum Entropy (MaxEnt) model comprehensively map susceptibility fill crucial gaps risk assessments. This employed a combination 10 geospatial environmental layers field-based past inventory implement MaxEnt machine learning modeling. The available data were divided into two sets, 75% allocated construction remaining 25% validation. demonstrated that proximity river had significant impact (33.1%) occurrence flood. Surprisingly, amount annual precipitation throughout year exhibited no detectable contribution event site. About 4.9% area came under high susceptible zone followed by 12.75 % moderate 82.34% low-risk zone. excellent performance an Area Under Curve (AUC) value 0.935 low standard deviation 0.018, indicating accurate predictions consistent precision. These results highlight model's reliability significance developing disaster management policy local government Future research should refine including more variables, validating against observed events, exploring integration other modeling approaches.

Language: Английский

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Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(12), P. 18701 - 18722

Published: Feb. 13, 2024

Language: Английский

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Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling

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

Published: March 4, 2024

This study focuses on determining the optimal distance metric in K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four metrics KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized flood susceptibility mapping (FSM) Estahban, Iran. A database comprising 509 occurrence points extracted from satellite images 12 factors influencing floods was created analysis. The particle swarm optimization (PSO) employed hyperparameter feature selection, considering eight influential as inputs. results revealed that KNN-Manhattan exhibited superior accuracy (root mean squared error (RMSE) = 0.169, absolute (MAE) 0.051, coefficient determination (R2) 0.884, area under curve (AUC) 0.94) compared with other algorithms identifying flood-prone areas. KNN-Minkowski followed closely, an RMSE 0.175, MAE 0.056, R2 0.876, AUC 0.939. KNN-Euclidean achieved 0.183, 0.061, 0.842, 0.929, whereas KNN-Chebyshev 0.198, 0.075, 0.924.

Language: Английский

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A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

Journal of Flood Risk Management, Journal Year: 2024, Volume and Issue: 17(2)

Published: March 7, 2024

Abstract Identifying flood‐prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory 13 predictors were used to implement five boosting algorithms: gradient machine (GBM), extreme boosting, categorical logit boost, light (LGBM) map susceptibility in Rathnapura while evaluating trained model's generalizing ability assessing the feature importance mapping (FSM). The model performance was evaluated using F1‐score, kappa index, area under curve (AUC) method. findings revealed that all models effective identifying overall trends LightGBM had superior results (F1‐score = 0.907, Kappa value 0.813 AUC 0.970), securing top scores across metrics compared other (for testing dataset). Based on evaluation, most of finer (AUC min 0.737) moderate predictions beyond training region. According results, regions lower altitudes topographic roughness values, rainfall, proximity rivers are more susceptible flooding. This framework can be adapted rapid FSM data‐deficient regions.

Language: Английский

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Assessment of flood susceptibility in Cachar district of Assam, India using GIS-based multi-criteria decision-making and analytical hierarchy process

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

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Spatial analysis of flood susceptibility in Coastal area of Pakistan using machine learning models and SAR imagery

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(5)

Published: Feb. 18, 2025

Language: Английский

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