Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city

Abstract

With the advancement of water conservancy informatization based on Internet of Things (IoT), the hydrological data are increasingly enriched. As a result, more and more algorithms and methods relying on deep learning are introduced in the flood forecasting. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecasting model based on Convolution Neural Network (CNN) with two-dimension (2D) convolutional operation. At first, we imported the rainfall spatial–temporal features by gridding the Xixian basin. After that, we processed the data from Digital Elevation Model (DEM) as the geographical feature and employed the historical streamflow process of Xixian basin as the trend feature. Next, extensive experiments were implemented to determine the optimal hyper-parameters of the proposed CNN flood process forecasting model. Numerical results show that our proposed model demonstrated a better accuracy for predicting the flood peak and arrival occasion, with a 24-hour and 36-hour lead time respectively.