Unconventional Water Use Allocation in Harbin, China

Abstract

To lessen the strain on Harbin’s water resources and enhance the environment, it is crucial to analyze the key determining factors for the use of unconventional water resources in the city and to allocate unconventional water reasonably among various industries in the planning year. In this study, a back-propagation neural network (BP neural network) model is used to predict the potential for unconventional water resource utilization in the planning year (2025), a gray correlation analysis model is used to evaluate water-using industries, and finally, an unconventional water resource allocation scheme for the study is used to determine the main influencing factors and determine the weights of key indicators. The findings demonstrate a strong correlation between Harbin’s level of investment and construction, economic efficiency, cost, level of water demand, and social factors, as well as a low level of utilization of unconventional water resources throughout the city.