Flood susceptibility mapping using advanced hybrid machine learning and CyGNSS: a case study of Nghe An province, Vietnam

• Autorzy: Nguyen Huu Duy , Vu Phương Lan , Ha Minh Cuong , Dinh Thi Bao Hoa , Nguyen Thuy Hang , Hoang Tich Phuc , Doan Quang Cuong , Pham Van Manh , Dang Dinh Kha

Identyfikatory

DOI

10.1007/s11600-022-00940-2

• Języki publikacji: EN

Abstrakty

EN

Flooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map food susceptibility with high accuracy, to support decision-makers and planners in designing more effective food management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build food susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 food locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R² ), were used to assess the performance of the model. The results for all the models were good, with an AUC value of+0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map food susceptibility with high accuracy.

Słowa kluczowe

EN

flood susceptibility; machine learning; CyGNSS; Nghe An; Vietnam

PL

podatność na powódź; nauczanie maszynowe; CyGNSS; Nghe An; Wietnam

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2785--2803
• Opis fizyczny: Bibliogr. 88 poz.

Twórcy

autor

Nguyen Huu Duy

• Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

autor

Vu Phương Lan

• Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

• https://orcid.org/0000-0002-5212-9688

autor

Ha Minh Cuong

• School of Aerospace Engineering (SAE), VNU University of Engineering and Technology (UET), Vietnam National University (VNU), Hanoi, Vietnam

autor

Dinh Thi Bao Hoa

• Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

autor

Nguyen Thuy Hang

• VNU Vietnam Japan University (VJU), Vietnam National University (VNU), Hanoi, Vietnam

autor

Hoang Tich Phuc

• Center for Interdisciplinary Integrated Technology Field Monitoring (FIMO), VNU University of Engineering and Technology (UET), Vietnam National University (VNU), Hanoi, Vietnam

autor

Doan Quang Cuong

• Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

autor

Pham Van Manh

• Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

autor

Dang Dinh Kha

• Faculty of Hydrology, Meteorology and Oceanography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).