eprintid: 4750
rev_number: 7
eprint_status: archive
userid: 422
dir: disk0/00/00/47/50
datestamp: 2022-08-22 03:57:57
lastmod: 2022-08-22 03:57:57
status_changed: 2022-08-22 03:57:57
type: article
metadata_visibility: show
creators_name: Ha, Minh Cuong
creators_name: Vu, Phuong Lan
creators_name: Nguyen, Huu Duy
creators_name: Hoang, Tich Phuc
creators_name: Dang, Dinh Duc
creators_name: Dinh, Thi Bao Hoa
creators_name: Şerban, Gheorghe
creators_name: Rus, Ioan
creators_name: Brețcan, Petre
title: Machine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province, Central Vietnam Region
ispublished: pub
subjects: Aerospace
subjects: isi
abstract: 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.
date: 2022-05-18
date_type: published
publisher: MDPI
official_url: https://www.mdpi.com/2073-4441/14/10/1617
id_number: https://doi.org/10.3390/w14101617
full_text_status: public
publication: Water
volume: 14
number: 10
refereed: FALSE
issn: 2073-4441
projects: Project QG.21.33
citation:   Ha, Minh Cuong and Vu, Phuong Lan and Nguyen, Huu Duy and Hoang, Tich Phuc and Dang, Dinh Duc and Dinh, Thi Bao Hoa and Şerban, Gheorghe and Rus, Ioan and Brețcan, Petre  (2022) Machine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province, Central Vietnam Region.  Water, 14  (10).    ISSN 2073-4441     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4750/1/water-14-01617.pdf