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http://dx.doi.org/10.6109/jkiice.2020.24.2.198

Design of Artificial Intelligence Water Level Prediction System for Prediction of River Flood  

Park, Se-Hyun (Department of Electronic Engineering, Andong National University)
Kim, Hyun-Jae (Major in Electronic Engineering, Department of Information and Communication, Andong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 198-203 More about this Journal
Abstract
In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.
Keywords
River flood forecasting; AI; LSTM; Nodejs;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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