Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam
Flood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objectiv...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-06-01
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| Series: | Water Practice and Technology |
| Subjects: | |
| Online Access: | http://wpt.iwaponline.com/content/18/6/1543 |
| _version_ | 1797783056854024192 |
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| author | Van Tich Vu Huu Duy Nguyen Phuong Lan Vu Minh Cuong Ha Van Dong Bui Thi Oanh Nguyen Van Hiep Hoang Thanh Kim Hue Nguyen |
| author_facet | Van Tich Vu Huu Duy Nguyen Phuong Lan Vu Minh Cuong Ha Van Dong Bui Thi Oanh Nguyen Van Hiep Hoang Thanh Kim Hue Nguyen |
| author_sort | Van Tich Vu |
| collection | DOAJ |
| description | Flood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objective of this study was to evaluate the effects of climate and land use change on flood susceptibility in Thua Thien Hue province, Vietnam, using machine learning techniques (support vector machine (SVM) and random forest (RF)) and remote sensing. The machine learning models used a flood inventory including 1,864 flood locations and 11 conditional factors in 2017 and 2021, as the input data. The predictive capacity of the proposed models was assessed using the area under the curve (AUC), the root mean square error (RMSE), and the mean absolute error (MAE). Both proposed models were successful, with AUC values exceeding 0.95 in predicting the effects of climate and land use change on flood susceptibility. The RF model, with AUC = 0.98, outperformed the SVM model (AUC = 0.97). The areas most susceptible to flooding increased between 2017 and 2021 due to increased built-up area.
HIGHLIGHTS
Machine learning algorithms were applied for flood susceptibility modeling.;
The RF model had the highest AUC value (0.98).;
The areas highly flood susceptibility increased between 2017 and 2021.; |
| first_indexed | 2024-03-13T00:20:43Z |
| format | Article |
| id | doaj.art-c421bf93a5dd4800bb26e669a8dcd3e3 |
| institution | Directory Open Access Journal |
| issn | 1751-231X |
| language | English |
| last_indexed | 2024-03-13T00:20:43Z |
| publishDate | 2023-06-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Practice and Technology |
| spelling | doaj.art-c421bf93a5dd4800bb26e669a8dcd3e32023-07-11T16:24:19ZengIWA PublishingWater Practice and Technology1751-231X2023-06-011861543155510.2166/wpt.2023.088088Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal VietnamVan Tich Vu0Huu Duy Nguyen1Phuong Lan Vu2Minh Cuong Ha3Van Dong Bui4Thi Oanh Nguyen5Van Hiep Hoang6Thanh Kim Hue Nguyen7 University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam VNU – University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy str., Hanoi, Vietnam University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam VNU – School of Interdisciplinary Sciences, Vietnam National University, 144 Xuan Thuy str., Hanoi, Vietnam VNU – School of Interdisciplinary Sciences, Vietnam National University, 144 Xuan Thuy str., Hanoi, Vietnam Center for Water Resources Warning and Forecast – National center for water resources Planning and Investigation, Hanoi, Vietnam Flood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objective of this study was to evaluate the effects of climate and land use change on flood susceptibility in Thua Thien Hue province, Vietnam, using machine learning techniques (support vector machine (SVM) and random forest (RF)) and remote sensing. The machine learning models used a flood inventory including 1,864 flood locations and 11 conditional factors in 2017 and 2021, as the input data. The predictive capacity of the proposed models was assessed using the area under the curve (AUC), the root mean square error (RMSE), and the mean absolute error (MAE). Both proposed models were successful, with AUC values exceeding 0.95 in predicting the effects of climate and land use change on flood susceptibility. The RF model, with AUC = 0.98, outperformed the SVM model (AUC = 0.97). The areas most susceptible to flooding increased between 2017 and 2021 due to increased built-up area. HIGHLIGHTS Machine learning algorithms were applied for flood susceptibility modeling.; The RF model had the highest AUC value (0.98).; The areas highly flood susceptibility increased between 2017 and 2021.;http://wpt.iwaponline.com/content/18/6/1543climate changeflood susceptibilityland usemachine learning |
| spellingShingle | Van Tich Vu Huu Duy Nguyen Phuong Lan Vu Minh Cuong Ha Van Dong Bui Thi Oanh Nguyen Van Hiep Hoang Thanh Kim Hue Nguyen Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam Water Practice and Technology climate change flood susceptibility land use machine learning |
| title | Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam |
| title_full | Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam |
| title_fullStr | Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam |
| title_full_unstemmed | Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam |
| title_short | Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam |
| title_sort | predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal vietnam |
| topic | climate change flood susceptibility land use machine learning |
| url | http://wpt.iwaponline.com/content/18/6/1543 |
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