基于深度学习的旧金山湾水质预测

传统的水质预测模型计算复杂,且在大对流情况下会引发误差,对于大数据时代下智能化的水质预测问题并不适用。本文针对旧金山湾地表水质研究区的数据资料,利用数据分析、统计检验、深度学习时序模型等技术方法对该研究区的水质进行研究,根据主成分信息构建了长短时记忆(LSTM)循环神经网络模型,对研究区的5个地表水质采样站点进行了水质预测。结果表明:长短时记忆循环神经网络模型通过门控制循环和记忆单元结构,有效控制传入模型的输入特征,从而降低模型的复杂度;双层长短时记忆循环神经网络模型较单层长短时记忆循环神经网络模型的预测精度平均提高5.3%。利用LSTM模型可以对旧金山湾地表水质进行有效评价。

Water Quality Prediction of San Francisco Bay Based on Deep Learning

The traditional water quality prediction model is complicated in calculation and will cause errors in the case of large convection, so it is not applicable to intelligent water quality prediction in the era of big data. Based on the data of San Francisco Bay surface water quality research area, the authors studied the water quality of the research area by using data analysis, statistical testing, deep learning time series models,and other technical methods. Based on the principal component information, a long short term memory(LSTM) circulation neural network model was constructed, and further,the water quality of 5 surface water sampling sites was predicted. The results show that the long short term memory cyclic neural network model can effectively control the loop and memory unit structure through gates and the input characteristics of the incoming model, thereby reducing the complexity of the model. Moreover, the prediction accuracy of the two-layer long and short-term memory cyclic neural network model is 5.3 % higher than that of the single-layer long and short-term memory cyclic neural network model.