TY  - JOUR
ID  - SisLab4750
UR  - https://www.mdpi.com/2073-4441/14/10/1617
IS  - 10
A1  - Ha, Minh Cuong
A1  - Vu, Phuong Lan
A1  - Nguyen, Huu Duy
A1  - Hoang, Tich Phuc
A1  - Dang, Dinh Duc
A1  - Dinh, Thi Bao Hoa
A1  - ?erban, Gheorghe
A1  - Rus, Ioan
A1  - Bre?can, Petre
Y1  - 2022/05/18/
N2  - Floods are the most frequent natural hazard globally and incidences have been increasing in recent years as a result of human activity and global warming, making significant impacts on people’s livelihoods and wider socio-economic activities. In terms of the management of the environment and water resources, precise identification is required of areas susceptible to flooding to support planners in implementing effective prevention strategies. The objective of this study is to develop a novel hybrid approach based on Bald Eagle Search (BES), Support Vector Machine (SVM), Random Forest (RF), Bagging (BA) and Multi-Layer Perceptron (MLP) to generate a flood susceptibility map in Thua Thien Hue province, Vietnam. In total, 1621 flood points and 14 predictor variables were used in this study. These data were divided into 60 for model training, 20 for model validation and 20 for testing. In addition, various statistical indices were used to evaluate the performance of the model, such as Root Mean Square Error (RMSE), Receiver Operation Characteristics (ROC), and Mean Absolute Error (MAE). The results show that BES, for the first time, successfully improved the performance of individual models in building a flood susceptibility map in Thua Thien Hue, Vietnam, namely SVM, RF, BA and MLP, with high accuracy (AUC > 0.9). Among the models proposed, BA-BES was most effective with AUC = 0.998, followed by RF-BES (AUC = 0.998), MLP-BES (AUC = 0.998), and SVM-BES (AUC = 0.99). The findings of this research can support the decisions of local and regional authorities in Vietnam and other countries regarding the construction of appropriate strategies to reduce damage to property and human life, particularly in the context of climate change.
PB  - MDPI
JF  - Water
VL  - 14
SN  - 2073-4441
TI  - Machine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province, Central Vietnam Region
AV  - public
ER  -