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

и другие.

Water, Год журнала: 2022, Номер 14(10), С. 1617 - 1617

Опубликована: Май 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.

Язык: Английский

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam DOI Creative Commons
Huu Duy Nguyen

Journal of Water and Climate Change, Год журнала: 2022, Номер 14(1), С. 200 - 222

Опубликована: Дек. 19, 2022

Abstract The objective of this study was the development an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf (GWO), Differential Evolution (DE) to construct flood susceptibility maps in Ha Tinh province Vietnam. database includes 13 conditioning factors 1,843 locations, which were split by a ratio 70/30 between those used build validate model, respectively. Various statistical indices, root mean square error (RMSE), area under curve (AUC), absolute (MAE), accuracy, R1 score, applied models. results show that all proposed models performed well, with AUC value more than 0.95. Of models, ANFIS-GBO most accurate, 0.96. Analysis shows approximately 32–38% is located high very zone. successful performance over large-scale can help local authorities decision-makers develop policies strategies reduce threats related flooding future.

Язык: Английский

Процитировано

28
Flood susceptibility mapping with ensemble machine learning: a case of Eastern Mediterranean basin, Türkiye DOI
Hüseyin Baran Özdemir, Müsteyde Baduna Koçyiğit, Diyar Akay

и другие.

Stochastic Environmental Research and Risk Assessment, Год журнала: 2023, Номер 37(11), С. 4273 - 4290

Опубликована: Июль 1, 2023

Язык: Английский

Процитировано

14
A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping DOI
Maelaynayn El baida,

Mohamed Hosni,

Farid Boushaba

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(15), С. 5823 - 5864

Опубликована: Авг. 3, 2024

Язык: Английский

Процитировано

5
Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India DOI Creative Commons
Pankaj Prasad, Sourav Mandal,

Sahil Naik

и другие.

Applied Computing and Geosciences, Год журнала: 2024, Номер 24, С. 100189 - 100189

Опубликована: Сен. 4, 2024

Язык: Английский

Процитировано

5
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

и другие.

Water, Год журнала: 2022, Номер 14(10), С. 1617 - 1617

Опубликована: Май 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.

Язык: Английский

Процитировано

19