Machine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province, Central Vietnam Region DOI Open Access
Minh Cường Hà, Phuong Vu, Huu Du Nguyen

et al.

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1617 - 1617

Published: May 18, 2022

Floods are the most frequent natural hazard globally and incidences have been increasing in recent years as a result of human activity global warming, making significant impacts on people’s livelihoods wider socio-economic activities. In terms management environment water resources, precise identification is required areas susceptible to flooding support planners implementing effective prevention strategies. The objective this study develop novel hybrid approach based Bald Eagle Search (BES), Support Vector Machine (SVM), Random Forest (RF), Bagging (BA) Multi-Layer Perceptron (MLP) generate flood susceptibility map Thua Thien Hue province, Vietnam. total, 1621 points 14 predictor variables were used study. These data divided into 60% for model training, 20% validation testing. addition, various statistical indices evaluate performance model, such Root Mean Square Error (RMSE), Receiver Operation Characteristics (ROC), Absolute (MAE). results show that BES, first time, successfully improved individual models building Hue, Vietnam, namely SVM, RF, BA MLP, with high accuracy (AUC > 0.9). Among proposed, BA-BES was AUC = 0.998, followed by RF-BES 0.998), MLP-BES SVM-BES 0.99). findings research can decisions local regional authorities Vietnam other countries regarding construction appropriate strategies reduce damage property life, particularly context climate change.

Language: Английский

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
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

17
Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method DOI
Ziyuan Luo, Jian Tian, Jian Zeng

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 106, P. 104435 - 104435

Published: March 28, 2024

Language: Английский

Citations

17
Threats of climate change and land use patterns enhance the susceptibility of future floods in India DOI
Subodh Chandra Pal, Indrajit Chowdhuri, Biswajit Das

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114317 - 114317

Published: Dec. 24, 2021

Language: Английский

Citations

76
Towards flood risk mapping based on multi-tiered decision making in a densely urbanized metropolitan city of Istanbul DOI
Ömer Ekmekcioğlu, Kerim Koç, Mehmet Özger

et al.

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 80, P. 103759 - 103759

Published: Feb. 5, 2022

Language: Английский

Citations

61
Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards DOI
Ömer Ekmekcioğlu, Kerim Koç

CATENA, Journal Year: 2022, Volume and Issue: 216, P. 106379 - 106379

Published: May 19, 2022

Language: Английский

Citations

44
Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations DOI Open Access
Rabin Chakrabortty, Subodh Chandra Pal,

Dipankar Ruidas

et al.

Water, Journal Year: 2023, Volume and Issue: 15(3), P. 558 - 558

Published: Jan. 31, 2023

Flood, a distinctive natural calamity, has occurred more frequently in the last few decades all over world, which is often an unexpected and inevitable hazard, but losses damages can be managed controlled by adopting effective measures. In recent times, flood hazard susceptibility mapping become prime concern minimizing worst impact of this global threat; nonlinear relationship between several causative factors dynamicity risk levels makes it complicated confronted with substantial challenges to reliable assessment. Therefore, we have considered SVM, RF, ANN—three ML algorithms GIS platform—to delineate zones subtropical Kangsabati river basin, West Bengal, India; experienced frequent events because intense rainfall throughout monsoon season. our study, adopted are efficient solving non-linear problems assessment; multi-collinearity analysis Pearson’s correlation coefficient techniques been used identify collinearity issues among fifteen factors. research, predicted results evaluated through six prominent statistical (“AUC-ROC, specificity, sensitivity, PPV, NPV, F-score”) one graphical (Taylor diagram) technique shows that ANN most modeling approach followed RF SVM models. The values AUC model for training validation datasets 0.901 0.891, respectively. derived result states about 7.54% 10.41% areas accordingly lie under high extremely danger zones. Thus, study help decision-makers constructing proper strategy at regional national mitigate particular region. This type information may helpful various authorities implement outcome spheres decision making. Apart from this, future researchers also able conduct their research byconsidering methodology

Language: Английский

Citations

29
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 DOI Creative Commons
Seyed Vahid Razavi-Termeh,

Myoung-Bae Seo,

Abolghasem Sadeghi‐Niaraki

et al.

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

Citations

29
Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal DOI Creative Commons
Menuka Maharjan, Sachin Timilsina, Santosh Ayer

et al.

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

Citations

14
Enhancing Flood Risk Analysis in Harris County: Integrating Flood Susceptibility and Social Vulnerability Mapping DOI
Hemal Dey, Wanyun Shao, Md. Munjurul Haque

et al.

Journal of Geovisualization and Spatial Analysis, Journal Year: 2024, Volume and Issue: 8(1)

Published: May 22, 2024

Language: Английский

Citations

10