Prediction model of ecological environmental water demand based on big data analysis

Abstract

The existing prediction model of eco-environmental water demand has the problem of large prediction error. In order to solve the above problems, the prediction model of eco-environmental water demand is constructed based on big data analysis. In order to reduce the prediction error of the ecological environment water demand prediction model, the framework of the ecological environment water demand prediction model is built. On this basis, the principal component analysis method is used to select the auxiliary variables of the model. Based on the selected auxiliary variables, the minimum monthly average flow method is used to analyze the basic water demand of the ecological environment, the leakage water demand and the water surface evaporation ecological environment water demand, so as to analyze based on the results, the water demand of ecological environment is predicted by big data analysis technology, and the prediction of water demand of ecological environment is realized. The experimental results show that compared with the existing ecological environment water demand prediction model, the prediction error of the model is within 19.3, which fully shows that the constructed ecological environment water demand prediction model has better prediction effect and can provide a certain reference value for the actual use of water resources. • The framework of water demand prediction model reduces the prediction error. • Using the 3 σ rule to eliminate the abnormal data. • The minimum monthly average flow method is used to obtain the basic water demand.