Published

2021-07-19

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

Un método de predicción de la capacidad de carga de los recursos hídricos regionales basado en una red neuronal artificial

DOI:

https://doi.org/10.15446/esrj.v25n2.81615

Keywords:

Artificial neural network, BP neural network, Regional water resources, Water resources carrying capacity, Carrying capacity prediction (en)
Red neuronal artificial, Red neuronal BP, Recursos hídricos regionales, Capacidad de carga de recursos hídricos, Predicción de la capacidad de carga (es)

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Authors

  • Chaoyang Shi Information Network Center, Zhengzhou Yellow River Nursing Vocational College, Zhengzhou, 450066, China
  • Zhen Zhang School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450066, China

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.

Para predecir la capacidad de carga de los recursos hídricos con mayor precisión y orientar mejor las actividades sociales y económicas, se propone un método de predicción de la capacidad de carga de los recursos hídricos regionales basado en una red neuronal artificial. El condado de Zhaozhou se selecciona como el área de investigación de la predicción de la capacidad de carga de los recursos hídricos, y se exploran sus características geográficas naturales, la economía social y la situación de los recursos hídricos. De acuerdo con las características regionales de cantidad y utilización de los recursos hídricos y el énfasis de la evaluación, el sistema de índice de evaluación de la capacidad de carga de los recursos hídricos se construye para evaluar la importancia y el grado de correlación de la capacidad de carga de los recursos hídricos, y el grado de presión de la capacidad de carga de los recursos hídricos se divide en cinco grados. De acuerdo con el estándar de evaluación de la capacidad de carga, se construye el modelo de red neuronal BP de inteligencia artificial. Con base en los principales factores de impacto de la capacidad de carga de los recursos hídricos en esta área, el grado de capacidad de carga de los recursos hídricos se obtiene mediante el cálculo del peso y la iteración de convergencia utilizando el modelo de red neuronal y los datos de factores de influencia, para realizar la predicción de la capacidad de carga de los recursos hídricos. Los resultados de la investigación muestran que el modelo de red puede satisfacer la demanda de precisión y los resultados de la predicción tienen un alto grado de ajuste con los datos reales, lo que indica que la inteligencia humana puede obtener resultados de predicción precisos en el proceso de predicción de la capacidad de carga de los recursos hídricos.

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How to Cite

APA

Shi, C. . and Zhang, Z. . (2021). A prediction method of regional water resources carrying capacity based on artificial neural network. Earth Sciences Research Journal, 25(2), 169–177. https://doi.org/10.15446/esrj.v25n2.81615

ACM

[1]
Shi, C. and Zhang, Z. 2021. A prediction method of regional water resources carrying capacity based on artificial neural network. Earth Sciences Research Journal. 25, 2 (Jul. 2021), 169–177. DOI:https://doi.org/10.15446/esrj.v25n2.81615.

ACS

(1)
Shi, C. .; Zhang, Z. . A prediction method of regional water resources carrying capacity based on artificial neural network. Earth sci. res. j. 2021, 25, 169-177.

ABNT

SHI, C. .; ZHANG, Z. . A prediction method of regional water resources carrying capacity based on artificial neural network. Earth Sciences Research Journal, [S. l.], v. 25, n. 2, p. 169–177, 2021. DOI: 10.15446/esrj.v25n2.81615. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/81615. Acesso em: 28 mar. 2025.

Chicago

Shi, Chaoyang, and Zhen Zhang. 2021. “A prediction method of regional water resources carrying capacity based on artificial neural network”. Earth Sciences Research Journal 25 (2):169-77. https://doi.org/10.15446/esrj.v25n2.81615.

Harvard

Shi, C. . and Zhang, Z. . (2021) “A prediction method of regional water resources carrying capacity based on artificial neural network”, Earth Sciences Research Journal, 25(2), pp. 169–177. doi: 10.15446/esrj.v25n2.81615.

IEEE

[1]
C. . Shi and Z. . Zhang, “A prediction method of regional water resources carrying capacity based on artificial neural network”, Earth sci. res. j., vol. 25, no. 2, pp. 169–177, Jul. 2021.

MLA

Shi, C. ., and Z. . Zhang. “A prediction method of regional water resources carrying capacity based on artificial neural network”. Earth Sciences Research Journal, vol. 25, no. 2, July 2021, pp. 169-77, doi:10.15446/esrj.v25n2.81615.

Turabian

Shi, Chaoyang, and Zhen Zhang. “A prediction method of regional water resources carrying capacity based on artificial neural network”. Earth Sciences Research Journal 25, no. 2 (July 19, 2021): 169–177. Accessed March 28, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/81615.

Vancouver

1.
Shi C, Zhang Z. A prediction method of regional water resources carrying capacity based on artificial neural network. Earth sci. res. j. [Internet]. 2021 Jul. 19 [cited 2025 Mar. 28];25(2):169-77. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/81615

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8. Qiuxiang Jiang, Weipeng Guo, Zilong Wang, Yunxing Wu, Youzhu Zhao, Meiyun Tao, Yuxiang Sun. (2024). Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China. Journal of Hydrology, 634, p.131084. https://doi.org/10.1016/j.jhydrol.2024.131084.

9. Gang He, Shuhang Zhao, Xiaodong Wang. (2024). Evaluation of water resources security in Anhui Province based on GA-BP model. Environmental Science and Pollution Research, 31(20), p.29246. https://doi.org/10.1007/s11356-024-32937-3.

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