Flood Velocity Prediction Using Deep Learning Approach

Abstract

Floods are one of the most serious natural disasters that can cause huge societal and economic losses. Extensive research has been conducted on topics like flood monitoring, prediction, and loss estimation. In these research fields, flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes. Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time. Deep 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 methods. This study explores the potential of deep learning models in predicting flood velocity. More 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 conditions. Geographic 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 model. Our 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 time. Meanwhile, we discuss the limitations for the improvement in future work.