Interrelation of the Estimation of Social and Economic Development of the Region and Quality of Water Resources
DOI:
https://doi.org/10.12974/2311-8741.2019.07.11Keywords:
Social and economic development, Regional economic system, Water resources, Estimation of the quality of water, Hydroeconomic system, Machine Learning.Abstract
The paper validates the practicability of studying the problem of the quality of water at a regional level taking into account the mutual interaction between soci?-economic and hydroeconomic systems of the territories. The research revealed the limitations of the existing methods of estimation of the quality of water in the light of creation of informational and analytical basis of making managerial decisions. Accordingly, the authors suggest using the analytical tools of fuzzy logic, cluster analysis and neural networks to build an integral estimation of degree of water pollution and to classify water bodies according to the degree of their pollution and similarity of conditions and parameters of pollution. On the basis of the data concerning the quality of water resources in the river basin neural network was built, allowing to determine clusters of regions according to the quality of water resources. As input variables we used the information on the volume of drawing and dropping of water for each region, the amount of wastewater, which is polluted. The suggested methods allow greatly increase the opportunities to make managerial decisions with the help of interrelated estimation of the quality of water resources and the indicators of social and economic development of regional economic systems.
References
Kosolapov AE, Kosolapova NA, Matveeva LG, Chernova OA. Assessment of water resources use efficiency based on the GRP water intensity indicator. Regional Statistics 2018; 8 (2): 154-169. https://doi.org/10.15196/RS080201
Hsien C, Low JSC, Chung SY, Tan DZL. Quality-based water and wastewater classification for waste-to-resource matching. Resour Conserv Recycl 2019; 151: 104477. https://doi.org/10.1016/j.resconrec.2019.104477
Government of Newfoundland and Labrador, 2018. Drinking Water Quality Index. URL http://www.mae.gov.nl.ca/- waterres/quality/drinkingwater/dwqihtml (accessed Nov 1, 2019).
Tyagi S, Sharma B, Singh P, Dobhal R, Water quality assessment in terms of water quality index. Am. J. Water Resour 2013; 1: 34-38.
Raut S, GS A, Dharnaik A. Determination of wastewater quality index of municipal wastewater treatment plant using determination of wastewater quality index of municipal wastewater treatment plant using fuzzy rule base. Eur. J. Adv. Eng. Technol 2017; 4(10): 733-738.
Vijayan G, Saravanane R, Sundararajan T. Wastewater quality index - a tool for categorization of the wastewater and its influence on the performance of sequencing batch reactor. Int. J Environ Eng Manag 2016; 7: 69-88.
Sargaonkar A, Deshpande V. Development of an overall index of pollution for surface water based on a general classification scheme in Indian context. Environ. Monit. Assess 2003; 89: 43-67. https://doi.org/10.1023/A:1025886025137
Tripathi M, Singal SK. Allocation of weights using factor analysis for development of a novel water quality index. Ecotoxicol Environ Saf. 2019; 183: 109510. https://doi.org/10.1016/j.ecoenv.2019.109510
Ahmed AN, Othman FB, Afan HA, Ibrahim RK, Fai CM, Hossain MS et al. Machine learning methods for better water quality prediction. J Hydrol 2019; 578: 124084. https://doi.org/10.1016/j.jhydrol.2019.124084
Maier HR, Dandy GC. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environ. Model. Softw 2000; 15(1): 101-124. https://doi.org/10.1016/S1364-8152(99)00007-9
Fu ZH, Zhao HJ, Wang H, Lu WT, Wang J, Guo HC. Integrated planning for regional development planning and water resources management under uncertainty: A case study of Xining, China. J Hydrol 2017; 554: 623-634. https://doi.org/10.1016/j.jhydrol.2017.08.022
Vieira JMP, Lijklema L. Development and application of a model for regional water quality management. Water Res 1989; 23(6): 767-777. https://doi.org/10.1016/0043-1354(89)90212-1
Ho JY, Afan HA, El-Shafie AH, Koting SB, Mohd NS, Jaafar WZB et al. Towards a time and cost effective approach to water quality index class prediction. J Hydrol 2019; 575: 148- 165. https://doi.org/10.1016/j.jhydrol.2019.05.016
Bu H, Meng W, Zhang Y, Wan J. Relationships between land use patterns and water quality in the Taizi River basin China. Ecol. Indic., 2014; 41: 187-197. https://doi.org/10.1016/j.ecolind.2014.02.003
Guo X, Zhang X, Yue H. Evaluation of hierarchically weighted principal component analysis for water quality management at Jiaozuo mine Int. Biodeterior. Biodegrad 2018; 128: 182-185. https://doi.org/10.1016/j.ibiod.2017.11.012
Kim SE, Seo IW, Choi SY. Assessment of water quality variation of a monitoring network using exploratory factor analysis and empirical orthogonal function. Environ. Model. Softw 2017; 94: 21-35. https://doi.org/10.1016/j.envsoft.2017.03.035
Singh VB, Tripathi JN. Identification of critical water quality parameters derived from principal component analysis: case study from NOIDA area in India Am. J. Water Res 2016; 4: 121-129.
Carrasco G, Molina JL, Patino-Alonso MC, Castillo MDC, Vicente-Galindo MP, Galindo-Villardón MP. Water quality evaluation through a multivariate statistical HJ-Biplot approach. J Hydrol 2019; 577: 123993. https://doi.org/10.1016/j.jhydrol.2019.123993
Shuhong C, Shijun Z, Dianfan Z. Water quality monitoring method based on feedback self correcting dense connected convolution network. Neurocomputing 2019; 349: 301-313. https://doi.org/10.1016/j.neucom.2019.03.023
Dobrolezha EV. Resource support of development of the region's economy: evaluation, management, efficiency. Rostov State Economic University. Ltd 2011.