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, Journal Year: 2022, Volume and Issue: 14(1), P. 200 - 222

Published: Dec. 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.

Language: Английский

Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms DOI
Farhana Parvin, Sk Ajim Ali, Beata Całka

et al.

Theoretical and Applied Climatology, Journal Year: 2022, Volume and Issue: 149(1-2), P. 639 - 659

Published: May 4, 2022

Language: Английский

Citations

35
Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India DOI Creative Commons
Chiranjit Singha, Kishore Chandra Swain, Modeste Meliho

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(24), P. 6229 - 6229

Published: Dec. 8, 2022

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, decision tree (ML) models. A total 400 flood nonflood locations acted as target variables hazard zoning map. All operative in this study tested using variance inflation factor (VIF) values (<5.0) Boruta feature ranking (<10 ranks) for FHZ maps. The model along with RF GBM had sound maps area. area under receiver operating characteristics (AUROC) curve statistical matrices such accuracy, precision, recall, F1 score, gain lift applied to assess performance. 70%:30% sample ratio training validation standalone models concerning AUROC value showed results all ML models, (97%), SVM (91%), NB (96%), DT (88%), (97%). also suitability RF, GBM, developing

Language: Английский

Citations

35
National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh DOI
Zakaria Shams Siam, Rubyat Tasnuva Hasan,

Soumik Sarker Anik

et al.

Geocarto International, Journal Year: 2022, Volume and Issue: 37(26), P. 12119 - 12148

Published: April 6, 2022

Assessing flood risk is challenging due to complex interactions among susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel assessment framework by utilizing hybridized deep neural network (DNN) fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as case region, where limited studies examined at national scale. The results exhibited that DNN AHP models can produce the most accurate map while comparing 15 different About 20.45% of are zones moderate, high, very high severity. northeastern well areas adjacent Ganges–Brahmaputra–Meghna rivers, have damage potential, significant number people were affected during 2020 event. developed in this would help policymakers formulate comprehensive management system.

Language: Английский

Citations

30
GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam DOI
Huu Duy Nguyen

Earth Science Informatics, Journal Year: 2022, Volume and Issue: 15(4), P. 2369 - 2386

Published: June 24, 2022

Language: Английский

Citations

29
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, Journal Year: 2022, Volume and Issue: 14(1), P. 200 - 222

Published: Dec. 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.

Language: Английский

Citations

29