Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam  

作　　者：Huong Thi Thanh Ngo Nguyen Duc Dam Quynh-Anh Thi Bui Nadhir Al-Ansari Romulus Costache Hang Ha Quynh Duy Bui Sy Hung Mai Indra Prakash Binh Thai Pham 

机构地区：[1]University of Transport Technology,Hanoi,100000,Vietnam [2]Department of Civil,Environmental and Natural Resources Engineering,Lulea University of Technology,Lulea,97187,Sweden [3]Department of Civil Engineering,Transilvania University of Brasov,Brasov,500152,Romania [4]Danube Delta National Institute for Research and Development,Tulcea,820112,Romania [5]Departement of Geodesy and Geomatics,National University of Civil Engineering,Hanoi,100000,Vietnam [6]Faculty of Hydraulic Engineering,National University of Civil Engineering,Hanoi,100000,Vietnam [7]DDG(R)Geological Survey of India,Gandhinagar,382010,India

出　　处：《Computer Modeling in Engineering & Sciences》2023年第6期2219-2241,共23页工程与科学中的计算机建模（英文）

基　　金：funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED)under Grant No.105.08-2019.03.

摘　　要：Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

关 键 词：Flash flood deep learning neural network(DL) machine learning(ML) receiver operating characteristic curve(ROC) VIETNAM 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]