Flood Velocity Prediction Using Deep Learning Approach

作     者：LUO Shaohua DING Linfang TEKLE Gebretsadik Mulubirhan BRULAND Oddbjørn FAN Hongchao LUO Shaohua;DING Linfang;TEKLE Gebretsadik Mulubirhan;BRULAND Oddbjørn;FAN Hongchao

作者机构：Department of Civil and Environmental EngineeringNorwegian University of Science and TechnologyTrondheim 7034Norway 

出 版 物：《Journal of Geodesy and Geoinformation Science》 (测绘学报（英文版）)

年 卷 期：2024年第7卷第1期

页      面：59-73页

核心收录：

学科分类：081802[工学-地球探测与信息技术] 08[工学] 081105[工学-导航、制导与控制] 0818[工学-地质资源与地质工程] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 

主　　题：flood velocity prediction geographic data MLP deep learning 

摘      要：Floods are one of the most serious natural disasters that can cause huge societal and economic *** research has been conducted on topics like flood monitoring,prediction,and loss *** these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the *** methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long *** learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional *** study explores the potential of deep learning models in predicting flood *** specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain *** data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP *** experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing ***,we discuss the limitations for the improvement in future work.