Affiliations: [a] School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China
| [b] School of Electronic Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China
Correspondence:
[*]
Corresponding author. Mengxiao Zhang, School of Information Engineering, North China University of Water Resources and Electric Power, 450045 Zhengzhou, China. E-mail: [email protected].
Abstract: Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir.
Keywords: LSTM, Blockchain-LSTM, water level prediction, compensation strategy
