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.

References

Cheng, K., Fu, Q., Meng, J., Li, T. X., & Pei, W. (2018). Analysis of the spatial variation and identification of factors affecting the water resources carrying capacity based on the cloud model. Water Resources Management, European Water Resources Association (EWRA), 32(8), 1-15. https://doi.org/10.1007/s11269-018-1957-x

Chenini, I., Msaddek, M. H., & Dlala, M. (2019). Hydrogeological characterization and aquifer recharge mapping for groundwater resources management using multicriteria analysis and numerical modeling: A case study from Tunisia. Journal of African Earth Sciences, 154(6), 59-69. https://doi.org/10.1016/j.jafrearsci.2019.02.031

Dos Santos, F. F., Pimenta, P. F., Lunardi, C., Draghetti, L., Carro, L., Kaeli, & D., Rech, P. (2019). Analyzing and increasing the reliability of convolutional neural networks on GPUs. IEEE Transactions on Reliability, 68(2), 663-677. DOI: 10.1109/TR.2018.2878387

Guo, L. (2020). Design of prediction system for sustainable carrying capacity of regional water resources based on big data. Modern Electronic Technology, 043(009), 117-121.

Hu, M. J., Li, Z. J., Ding, Z. S., Zhou, N., & Shen, Y. (2019). The spatio-temporal heterogeneity and driving mechanism of China's water transfer from agriculture to non-agriculture. Geographical Research, 38(06), 1542-1554. DOI: 10.11821/dlyj020171262

Jia, J. H., & Long, X. J. (2018). Research on prediction model of water resources carrying capacity. Water Resources and Hydropower Engineering, 49(10), 24-30.

Jiang, Y. T., & Li, P. (2018). Short-term load forecasting based on improved particle swarm neural network. Electrical Engineering, 19(02), 87-91. DOI: 10.1109/CEECT50755.2020.9298636

Kamimura, R. (2018). Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks. Neurocomputing, 323(1), 12-36. https://doi.org/10.1016/j.neucom.2018.09.036

Li, S. H., & Wang, L. (2018). Neural network renormalization group. Physical review letters, 121(26), 260601.1-260601.7. DOI: 10.1103/PhysRevLett.121.260601

Liu, Z. M., Zhou, Z. Z., & Wang, Y. Q. (2019). Forecast and analysis of regional water resources carrying capacity based on grey prediction model. Journal of Yangtze River Scientific Research Institute, 36(9), 34-39.

Li, W. K., Wang, W. L., & Li, L. (2018). Optimization of water resources utilization by multi-objective moth-flame algorithm. Water Resources Management, 32(10), 3303-3316. https://doi.org/10.1007/s11269-018-1992-7

Menhas, R., Mahmood, S., Tanchangya, P., & Safdar, M. N. (2019). Sustainable development under belt and road initiative: A case study of China-Pakistan economic corridor's socio-economic impact on Pakistan. Sustainability, 11(21), 1-24.

Par, S. S., & Kar, S. (2019). A hybridized forecasting method based on weight adjustment of neural network using generalized type-2 fuzzy set. International Journal of Fuzzy Systems, 21(1), 308-320. https://doi.org/10.1007/s40815-018-0534-z

Pelusi, D., Mascella, R., Tallini L., Nayak, J., Naik, B., & Abraham, A. (2018). Neural network and fuzzy system for the tuning of gravitational search algorithm parameters. Expert Systems with Applications, 102(7), 234-244. https://doi.org/10.1016/j.eswa.2018.02.026

Todo, Y., Tang, Z., Todo, H., Ji, J., & Yamashita, K. (2019). Neurons with Multiplicative Interactions of Nonlinear Synapses. International Journal of Neural Systems, 29(08), 115-133. DOI:10.1142/S0129065719500126

Wang, L., Xiong, Y. N., Li, S. W., & Zeng, Y. R. (2019). New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowledge-Based Systems, 176(7), 77-96. https://doi.org/10.1016/j.knosys.2019.03.028

Wang, L., Zhao, H. Y., & Sha, C. L. (2018). Dynamical stability in a delayed neural network with reaction–diffusion and coupling. Nonlinear Dynamics, 92(3), 1197-1215. https://doi.org/10.1007/s11071-018-4118-0

Wen, S., Xiao, S., Yang, Y., Yan, Z., Zeng, Z., & Huang, T. (2019). Adjusting learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(6), 1084-1094. DOI: 10.1109/TCAD.2018.2834436

Xu, X., Zhang, Y., & Chen, Y. (2020). Projecting China's future water footprint under the shared socio-economic pathways. Journal of Environmental Management, 260(4), 110102.1-110102.13. DOI:10.1016/j.jenvman.2020.110102

Zhu, L., Li, X., Bai, Y., Yi, T., & Yao, L. (2019). Evaluation of water resources carrying capacity and its obstruction factor analysis: A case study of Hubei province, China. Water, 11(12), 2573.1-2573.14. DOI:10.3390/w11122573

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: 29 mar. 2024.

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 29, 2024. 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 2024 Mar. 29];25(2):169-77. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/81615

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2. Shuo Zhu, Yan Liu, Miaochao Chen. (2022). Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network. Journal of Mathematics, 2022, p.1. https://doi.org/10.1155/2022/1332829.

3. Ying Zhang, Xiaomeng Song, Xiaojun Wang, Zhifeng Jin, Feng Chen. (2023). Multi-Level Fuzzy Comprehensive Evaluation for Water Resources Carrying Capacity in Xuzhou City, China. Sustainability, 15(14), p.11369. https://doi.org/10.3390/su151411369.

4. Yaqing Li, Jing Zhang, Yongyu Song. (2022). Comprehensive comparison and assessment of three models evaluating water resource carrying capacity in Beijing, China. Ecological Indicators, 143, p.109305. https://doi.org/10.1016/j.ecolind.2022.109305.

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