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

Abstract

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.

Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China

“…The index evaluation system methods include artificial neural networks, comprehensive index analysis, principal component analysis, and fuzzy comprehensive evaluation (FCE) methods. The artificial neural networks method can approximate any nonlinear function in theory, and can usually predict multivariable nonlinear systems with satisfactory results: for example, Shi and Zhang [45] used it to predict water consumption in Zhaozhou County; however, the artificial neural network prediction is based on relatively independent systems, making it challenging to consider the coupling between systems [46]. The principal component analysis [47,48] is a statistical analysis method for reducing dimensionality that can replace the original information of many variables with a few independent comprehensive indexes.…”

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

“…The establishment of a global environmental monitoring system called ''early warning'' was introduced in the environmental field (Yuan, 1987). Nevertheless, most related studies were concentrated on the forecasting of floods (Plate, 2008), early warning of water shortage and water resource carrying status (Yu et al, 2020;Shi and Zhang, 2021), and prediction of water quality and pollutant (Ding et al, 2017;Jin et al, 2019;Imani et al, 2021), whereas very few paid attention to the comprehensive early warning of the water environmental system risk. Although the WECC does not have a uniform definition, it is a broad concept related to the environmental properties of water, and it focuses on the mechanisms of interaction in human (socioeconomic)-water environmental systems (Zhou et al, 2019).…”