Sensitivity analysis and prediction of water supply and demand in Shenzhen based on an ELRF algorithm and a self-adaptive regression coupling model

Abstract

Given that sensitive feature recognition plays an important role in the prediction and analysis of water supply and demand, how to conduct effective sensitive feature recognition has become a critical problem. The current algorithms and recognition models are easily affected by multicollinearity between features. Moreover, these algorithms include only a single learning machine, which exposes large limitations in the process of sensitive feature recognition. In this study, an ensemble learning random forest (ELRF) algorithm, including multiple learning machines, was proposed to recognize sensitive features. A self-adaptive regression coupling model was developed to predict water supply and demand in Shenzhen in the next ten years. Results validate that the ELRF algorithm can effectively recognize sensitive features compared with decision tree and regular random forest algorithms. The model used in this study shows a strong self-adaptive ability in the modeling process of multiple regression. The water demand in Shenzhen will reach 2.2 billion m3 in 2025 and 2.35 billion m3 in 2030, which will exceeded the water supply ability of Shenzhen. Furthermore, three scenarios are designed in terms of water supply security and economic operation, and a comparative analysis is performed to obtain an optimal scenario.