Prediction of Water Demand in Yangtze River Delta Urban Agglomeration

Xiaowei Feng

《World Scientific Research Journal》 8卷6期 (2022/06) Pp. 280-284

https://doi.org/10.6911/WSRJ.202206_8(6).0037

摘要

The prediction of urban water demand can provide reference for the planning and management of water resources, and it is of great significance to alleviate the pressure of regional water resources. By comparing the prediction accuracy of random forest, support vector machine and BP neural network model, this paper seeks the most suitable model for forecasting water demand of Yangtze River Delta urban agglomeration. The results show that the average relative errors of SVM in the fitting and forecasting stages are 2.64% and 2.20% respectively, and the overall error is 2.57%. Compared with the other two models, SVM has the best overall performance and can be used to predict the water demand of the Yangtze River Delta urban agglomeration.

關鍵字

Yangtze river delta urban agglomeration ； Water demand prediction ； Random forest ； Support vector machine ； Back propagation

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