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International Journal of Neural Network, 2022, 3(2); doi: 10.38007/NN.2022.030208.

Evaluation Model of Regional Water Resources Sustainable Utilization Based on Neural Network


Shiyao Liu

Corresponding Author:
Shiyao Liu

College of Architecture, Xi'an University of Architecture and Technology Huaqing College, Xi'an, 710043, Shaanxi, China


With the growth of population and the rapid development of science and technology and economy, how to sustainably utilize limited WR to support the rapidly increasing population, ensure human water demand, and ensure the sound development of society, economy and ecological environment has long been become one of the hotpots in WR research. Therefore, this paper studies the evaluation model (EM) of regional water resources (WR) sustainable utilization based on neural network. In this paper, city A is taken as an example to study the model of sustainable utilization of WR. Based on deep learning theory, an EM of sustainable utilization of WR—probabilistic neural network (PNN) model is constructed. The research results show that the evaluation results of the PNN model are accurate and reasonable.


Neural Network, Regional Water Resources, Sustainable Utilization, Evaluation Model

Cite This Paper

Shiyao Liu. Evaluation Model of Regional Water Resources Sustainable Utilization Based on Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 2: 60-67. https://doi.org/10.38007/NN.2022.030208.


[1] Halmaghi E E, Moteanu D. Considerations on Sustainable Water Resources Management. International conference KNOWLEDGE-BASED ORGANIZATION, 2019, 25(1):236-240. https://doi.org/10.2478/kbo-2019-0038

[2] Al-Faraj F, Scholz M. Technical Support Framework for Sustainable Management of Transboundary Water Resources. Environmental engineering and management journal, 2019, 18(3):707-718. https://doi.org/10.30638/eemj.2019.064

[3] Tuninetti M, Tamea S, Dalin C. Water Debt Indicator Reveals Where Agricultural Water Use Exceeds Sustainable Levels. Water Resources Research, 2019, 55(3):2464-2477. https://doi.org/10.1029/2018WR023146

[4] Rivera R J, Failde R M, Martin J D. Therapeutic characteristics of Galician mineral and thermal waters (NW-Spain) ascribed to their local/regional geological setting. Sustainable Water Resources Management, 2019, 5(1):83-99. https://doi.org/10.1007/s40899-017-0112-9

[5] Bancheva-Preslavska H, Bezlova D. Communication Criteria for Conservation and Sustainable Use of Bulgarian Wetlands of International Importance. Journal of Environmental Protection and Ecology, 2018, 19(4):1873-1880.

[6] Mrowiec M, Ociepa E, Malmur R, et al. Sustainable water management in cities under climate changes. Problemy Ekorozwoju, 2018, 13(1):133-138.

[7] Anandakumar, Kumar A, Kale R V, et al. A Hybrid-Wavelet Artificial Neural Network Model for Monthly Water Table Depth Prediction. Current science, 2019, 117(9):1475-1481. https://doi.org/10.18520/cs/v117/i9/1475-1481

[8] Amrit K, Pandey R P, Mishra S K. Assessment of meteorological drought characteristics over Central India. Sustainable Water Resources Management, 2018, 4(4):999-1010. https://doi.org/10.1007/s40899-017-0205-5

[9] Kumar R. Sustainable Water Supply Plan-2040 for Lucknow City. Journal of Indian Water Works Association, 2018, 50(4):263-268.

[10] C Sánchez-Sánchez, Izzo D. Real-time optimal control via Deep Neural Networks: study on landing problems. Journal of Guidance, Control, and Dynamics, 2018, 41(5):1122-1135. https://doi.org/10.2514/1.G002357

[11] Wang X M, Jian-Wu Q I, Xiao-Hong L I, et al. Dynamic Evaluation of Ecological Security in Tianshui City Based on Grey Correlation Model. Resource Development & Market, 2018.

[12] Ruybal C J, Hogue T S, Mccray J E. Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatio-Temporal Regression Kriging. Water Resources Research, 2019, 55(4):2820-2837. https://doi.org/10.1029/2018WR023437

[13] Ghazavi R., Samie M., Vali AB., Pakparvar M. Evaluation of the effect of land use change on runoff using supervised classified satellite data. Global Nest Journal, 2019, 21(2):245-252.

[14] Augusto M R, Campos B, Carvalho V, et al. Modeling of H2S Dispersion in Brazil with Aermod: Case Study of Water Resource Recovery Facility In South of Brazil. Revista Brasileira de Meteorologia, 2019, 34(4):497-504. https://doi.org/10.1590/0102-7786344063