Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City

Authors

  • Mustafa Ahmed Othman Abo Mhara Department of Electronics Commerce, Faculty of Economics and Political Science, Bani Walid University, LIBYA

DOI:

https://doi.org/10.31033/ijemr.10.6.16

Keywords:

Flood, Forecasting, Deep Learning, CNN, Spatial-Temporal Feature, Geographical Feature

Abstract

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.

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Published

2020-12-31

How to Cite

Mustafa Ahmed Othman Abo Mhara. (2020). Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City. International Journal of Engineering and Management Research, 10(6), 118–129. https://doi.org/10.31033/ijemr.10.6.16