A prediction method of regional water resources carrying capacity based on artificial neural network

Earth sciences research journal

article

Zhen Zhang¹  Shi Chaoyang² 

[1]School of Intelligent Engineering, Zhengzhou University of Aeronautics

[2]Information Network Center, Zhengzhou Yellow River Nursing Vocational College

关键词: Artificial neural network;    BP neural network;    Regional water resources;    Water resources carrying capacity;    Carrying capacity prediction;   

DOI  :  10.15446/esrj.v25n2.81615

学科分类：社会科学、人文和艺术（综合）

来源: Universidad Nacional de Colombia * Departamento de Geociencias

【 摘 要 】

To better predict the water resources carrying capacity and guide the social and economic activities, a prediction method of regional water resources carrying capacity is proposed based on an artificial neural network. Zhaozhou County is selected as the research area of water resources carrying capacity prediction, and its natural geographical characteristics, social economy, and water resources situation are explored. According to the regional water resources quantity and utilization characteristics and evaluation emphasis, the evaluation index system of water resources carrying capacity is constructed to evaluate the importance and correlation of water resource carrying capacity. The pressure degree of water resources carrying capacity is divided into five grades. According to the evaluation standard of bearing capacity, the artificial intelligence BP neural network model is constructed. Based on the main impact factors of water resources carrying capacity in this area, the water resources carrying capacity grade is obtained by weight calculation and convergence iteration by using neural network model and influence factor data to realize the prediction of water resources carrying capacity. The research results show that the network model can meet the demand for precision. The prediction results have a high degree of fit with the actual data, indicating that human intelligence can obtain accurate prediction results in water resources carrying capacity prediction.

【 授权许可】

CC BY