K-Similarity降噪的LSTM神经网络水质多因子预测模型

Water Quality Multi-Factor Prediction Model Using LSTM Neural Network Based on K-Similarity Noise Reduction

针对水质预测问题,以地表水水质监测因子作为研究对象,提出了一种基于长短期记忆(LSTM)神经网络的水质多因子预测模型,同时利用提出的K-Similarity降噪法对模型的输入数据进行降噪,提高模型预测性能.通过与BP神经网络、RNN和传统的LSTM神经网络预测模型进行对比实验,证明了所提出的方法均方误差最小,预测结果更准确.

In view of the water quality prediction problem, taking the surface water quality monitoring factors as the research object, a Long Short-Term Memory(LSTM) neural network based model is proposed for water quality multifactor prediction. At the same time, the proposed K-Similarity method is used to denoise the input data of the model to improve the prediction performance of the model. Compared with BP neural network, RNN, and traditional LSTM neural network prediction model, the experiment shows that the proposed method has the least square error and the prediction result is more accurate.