Application Of Machine Learning In The Prediction Of Intelligent Water Demand

In order to solve the problem of large error between actual water use and water demand prediction results in the existing urban water demand prediction,a method of urban water demand prediction is proposed in this paper.Based on the collected water consumption data of a community,this method uses the relevant algorithm of machine learning to establish the prediction model of urban water demand,which effectively reduces the error between the prediction result of urban water demand and the actual water consumption.Firstly,the implementation principle and application scenario of classical prediction algorithm in machine learning are studied to find out the algorithm suitable for urban water demand prediction.Through the research,the Autoregressive Integrated Moving Average mode,the Seasonal Autoregressive Integrated Moving Average mode,the Convolutional Neural Networks model and the Long Short-Term Memory model in neural network learning are preliminarily selected for water demand prediction.Finally,the Seasonal Autoregressive Integrated Moving Average mode and the Long Short-Term Memory model with good prediction results are selected.Based on the water consumption data of communities,Research on community water demand forecast is carried out respectively.Secondly,the water demand prediction model of urban residents is constructed.In this paper,convolutional neural network model(CNN)and short and long term memory network model(LSTM)are used to construct the CNN-LSTM water demand prediction model based on attention mechanism,so as to realize the prediction of urban water demand.The prediction results show that: compared with the LSTM model and the seasonal differential autoregressive sliding model,the mean square error between the prediction result and the actual water demand is the smallest when CNN-LSTM model is used to forecast the urban water demand.After the attention mechanism is added into the CNN-LSTM model,the CNN-LSTM water demand prediction model based on the attention mechanism is constructed,and the error between the prediction results of the model and the real value is smaller.Thus,the validity of the CNNLSTM water demand prediction model based on attention mechanism is verified.Finally,according to the established CNN-LSTM water demand prediction model based on attention mechanism,the development framework of Spring Cloud and Vue is adopted to complete the development of the intelligent water demand prediction system,and the relevant pages are displayed to verify the availability of the system.