Water Level Prediction of Firewater System Based on Improved Hybrid LSTM Algorithm

Abstract

Aiming at the incomplete data and difficult prediction in the prediction of firewater system water level, a data filling method is proposed based on the reinforcement learning approach and a deep learning (DL) prediction method is studied based on the long-term short-term memory (LSTM) model in this paper. Firstly, a reinforcement learning-based K-nearest neighbor (KNN) algorithm is designed for the data incompleteness that occurs in the firewater level. The parameters of the KNN algorithm are optimized through the reinforcement learning to enhance the accuracy of data filling. Secondly, to compensate for the shortcomings of the traditional firewater pool level prediction methods, a DL prediction method based on the LSTM network approach is proposed by using the complete data. Through the LSTM three gate functions, the previous information is rounded to avoid causing gradient explosion, rounding the method is more stable and accurate. Finally, the simulation results show that the proposed method in this paper can effectively predict incomplete water level information.

Keywords

• Firewater system
• LSTM
• deep learning
• K-nearest neighbors
• reinforcement learning