EMULATION PERFORMANCE EVALUATION OF URBAN RUNOFF MODEL BY NEURAL NETWORK AND DEEP LEARNING

Abstract

 In recent years, urban floods have frequently occurred, and improving the accuracy of urban runoff prediction is an urgent issue. Urban runoff process is complicated, and it is difficult to construct a runoff model with high accuracy. The machine learning model can adjust the model parameters automatically if there are input data and output data, so it is possible to construct the model easily. So, in this paper, we aim to evaluate how much the urban runoff model can be emulated by the machine learning model, and the virtual rainfall and the virtual runoff （with the known true value already published by the authors）obtained from it. The artificial neural network model and deep learning model were constructed for quantity, and the reproducibility in learning flood and verification flood was compared and verified. We also evaluated the emulation performance when changing hyper parameters.