Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City
DOI:
https://doi.org/10.31033/ijemr.10.6.16Keywords:
Flood, Forecasting, Deep Learning, CNN, Spatial-Temporal Feature, Geographical FeatureAbstract
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.