Keywords
Water Resource Management
Semiarid Region
Water Quality Index
Multi-Criteria Decision Making
How to Cite
Abstract
Abstract Water management in the Brazilian semi-arid region has been, for decades, a challenge for institutions and decision-makers due to its intrinsic characteristics. The density of human occupation makes the region very vulnerable to drought events and problems related to the quality and need for water use are central issues. For this reason, this study presents an approach to assess the situation of water reservoirs in the semiarid based on the Water Quality Index (WQI) and Multi-Criteria Decision Making (MCDM). The WQI was used to calculate water quality and later applied as a criterion for the MCDM model proposed. The model also considers the need and availability criteria to assess the reservoirs of the two largest drainage basins in Rio Grande do Norte state, Brazil. The MCDM method used was R-TOPSIS since it is more flexible and robust for future analyses in other situations. The results showed the condition of the reservoirs, in order to support decision-makers in the operation of these facilities and enable multiple use of the waters. The combined approach proposed may provide important contributions in the analysis of water reservoirs in order to supply the semiarid region, where water issue is critical.
References
ABBASI, T.; ABBASI, S. A. Water quality indices. Elsevier Science, 2012. https://doi.org/10.1016/B978-0-444-54304-2.00016-6
AIRES, R. F. F.; FERREIRA, L. A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, v. 139, p. 84-97, 2019. https://doi.org/10.1016/j.cie.2019.04.023
AIRES, R. F. F.; FERREIRA, L. A New Multi-Criteria Approach for Sustainable Material Selection Problem. Sustainability, v. 14, n. 18, 11191, 2022. https://doi.org/10.3390/su141811191
AIRES, R. F. F.; FERREIRA, L. The Rank Reversal Problem in Multi-Criteria Decision Making: A Literature Review. Pesquisa Operacional, v. 38, n. 2, p. 331-362, 2018. https://doi.org/10.1590/0101-7438.2018.038.02.0331
AIRES R. F. F.; SALGADO, C. C. R. A Multi-Criteria Approach to Assess the Performance of the Brazilian Unified Health System. International Journal of Environmental Research and Public Health, v. 19, n. 18, 11478, 2022. https://doi.org/10.3390/ijerph191811478
AL-ABADI, A. M. Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models. Applied Water Science, v. 7, n. 2, p. 699–716, 2017. https://doi.org/10.1007/s13201-015-0283-1
ANA - Agência Nacional de Águas. Reservatórios do Semiárido Brasileiro: Hidrologia, Balanço Hídrico e Operação. 2017. Available:https://metadados.snirh.gov.br/geonetwork/srv/api/records/ccc25b76-f711-41ea-a79e-c8d30c287e53 Accessed on: 28 mar. 2019.
ANA - Agência Nacional de Águas. Boletim de Acompanhamento dos Reservatórios do Nordeste do Brasil. 2018. Available: https://www.ana.gov.br/noticias/estudo-reservatorios/ Accessed on: 28 mar. 2019.
AZEVÊDO, E. L.; MEDEIROS, C. R.; GOMES, W. I. A.; AZEVÊDO, D. J. S.; ALVES, R. R. N.; DIAS, T. L. P.; MOLOZZI, J. The use of Risk Incidence and Diversity Indices to evaluate water quality of semi-arid reservoirs. Ecological Indicators, v. 90, p. 90-100, 2018. https://doi.org/10.1016/j.ecolind.2018.02.052
BANIHABIB, M. E.; SHABESTARI, M. H. Fuzzy Hybrid MCDM Model for Ranking the Agricultural Water Demand Management Strategies in Arid Areas. Water Resources Management, v. 31, n. 1, p. 495–513, 2017. https://doi.org/10.1007/s11269-016-1544-y
BANSAL, S.; GANESAN, G. Advanced Evaluation Methodology for Water Quality Assessment Using Artificial Neural Network Approach. Water Resources Management, v. 33, n. 9, p. 3127–3141, 2019. https://doi.org/10.1007/s11269-019-02289-6
BEHZADIAN, M.; OTAGHSARA, S. K.; YAZDANI, M.; IGNATIUS, J. A state-of-the-art survey of TOPSIS applications. Expert Systems with Applications, v. 39, n. 17, p. 13051-13069, 2012. https://doi.org/10.1016/j.eswa.2012.05.056
BOZDAĞ, A. Combining AHP with GIS for assessment of irrigation water quality in Çumra irrigation district (Konya), Central Anatolia, Turkey. Environmental Earth Sciences, v. 73, n. 12, p. 8217–8236, 2015. https://doi.org/10.1007/s12665-014-3972-4
BROWN, R. M.; MCLELLAND, N. J.; DEININGER, R. A.; TOZER, R. G. A Water Quality Index – Do We Dare? Water & Sewage Works, v. 117, n. 10, p. 339-343, 1970.
CAMPANELLA, G.; RIBEIRO, R. A. A framework for dynamic multiple-criteria decision making. Decision Support Systems, v. 52, n. 1, p. 52–60, 2011. https://doi.org/10.1016/j.dss.2011.05.003
DANIELSON, M.; EKENBERG, L. An improvement to swing techniques for elicitation in MCDM methods. Knowledge-Based System, v. 168, p. 70–79, 2019. https://doi.org/10.1016/j.knosys.2019.01.001
DENG, X.; SONG, C.; LIU, K.; KE, L.; ZHANG, W.; MA, R.; ZHU, J.; WU, Q. Remote sensing estimation of catchment-scale reservoir water impoundment in the upper Yellow River and implications for river discharge alteration. Journal of Hydrology, v. 585, p. 124791, 2020. https://doi.org/10.1016/j.jhydrol.2020.124791
EDWARDS, W.; BARRON, F. H. SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement. Organizational Behavior and Human Decision Processes, v. 60, n. 3, p. 306–325, 1994. https://doi.org/10.1006/obhd.1994.1087
HAAK, L.; PAGILLA, K. The Water-Economy Nexus: a Composite Index Approach to Evaluate Urban Water Vulnerability. Water Resources Management, v. 34, n. 1, p. 409–423, 2020. https://doi.org/10.1007/s11269-019-02464-9
HWANG, C. L.; YOON, K. Multiple attributes decision-making methods and applications. New York: Springer, 1981.
IGARN - Instituto de Gestão das Águas do Estado do Rio Grande do Norte. Bacias Hidrográficas. 2023. Available:http://adcon.rn.gov.br/ACERVO/IGARN/DOC/DOC000000000029746.HTML Accessed on: 12 dec. 2023.
LI, P.; WU, J.; QIAN, H. Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China. Environmental Monitoring and Assessment, v. 184, n. 8, p. 4841-4854, 2012. https://doi.org/10.1007/s10661-011-2306-1
MLADENOVIĆ-RANISAVLJEVIĆ, I. I.; TAKIĆ, L.; NIKOLIĆ, D. Water Quality Assessment Based on Combined Multi-Criteria Decision-Making Method with Index Method. Water Resources Management, v. 32, p. 2261-2276, 2018. https://doi.org/10.1007/s11269-018-1927-3
MUSTAJOKI, J.; HAMALAINEN, R. P.; SALO, A. Decision Support by SMART/SWING: Incorporating Imprecision in the SMART e SWING Methods. Decision Sciences, v. 36, n. 2, p 317–339, 2005. https://doi.org/10.1111/j.1540-5414.2005.00075.x
MUSTAJOKI, J.; HAMALAINEN, R. P.; LINDSTEDT, M. R. K. Using Intervals for Global Sensitivity and Worst-Case Analyses in Multiattribute Value Trees. European Journal of Operational Research, v. 174, n. 1, p. 278–292, 2006. https://doi.org/10.1016/j.ejor.2005.02.070
PAA - Programa Água Azul. Qualidade das águas dos principais corpos d’água interiores norte-rio-grandenses com vistas ao consumo humano e preservação ambiental. 2017. Available:http://programaaguaazul.ct.ufrn.br/relatorios/aguas_superficiais/ Accessed on: 28 mar. 2019.
ROY, R.; MAJUMDER, M.; BARMAN, R. N. Assessment of Water Quality by RSM and ANP: A Case Study in Tripura, India. Asian Journal of Water, Environment and Pollution, v. 14, n. 1, p. 51–58, 2017. https://doi.org/10.3233/AJW-170006
SAADATPOUR, M. An Adaptive Surrogate Assisted CE-QUAL-W2 Model Embedded in Hybrid NSGA-II AMOSA Algorithm for Reservoir Water Quality and Quantity Management. Water Resources Management, v. 34, n. 4, p. 1437–1451, 2020. https://doi.org/10.1007/s11269-020-02510-x
ŞENER, Ş.; ŞENER, E.; DAVRAZ, A. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Science of The Total Environment, v. 584-585, p. 131–144, 2017. https://doi.org/10.1016/j.scitotenv.2017.01.102
SINGH, A. P.; SRINIVAS, R.; KUMAR, S.; CHAKRABARTI, S. Water quality assessment of a river basin under fuzzy multi-criteria framework. International Journal of Water, v. 9, n. 3, p. 226–247, 2015. https://doi.org/10.1504/IJW.2015.070364
SUTADIAN, A. D.; MUTTIL, N.; YILMAZ, A. G.; PERERA, B. J. C. Using the Analytic Hierarchy Process to identify parameter weights for developing a water quality index. Ecological Indicators, v. 75, p. 220-233, 2017. https://doi.org/10.1016/j.ecolind.2016.12.043
WALKER, D.; JAKOVLJEVIĆ, D.; SAVIĆ, D.; RADOVANOVIĆ, M. Multi-criterion water quality analysis of the Danube River in Serbia: A visualisation approach. Water Research, v. 79, p. 158–172, 2015. https://doi.org/10.1016/j.watres.2015.03.020
WEF – World Economic Forum. The Global Risks Report 2018. 2018. Available:http://www3:weforum:org=docs=WEFGRR18Report:pdf Accessed on: 20 mar. 2018.
WEST, A. O.; NOLAN, J. M.; SCOTT, J. T. Optical water quality and human perceptions: a synthesis. Wiley Interdisciplinary Reviews: Water, v. 3, n. 2, p. 167–180, 2016. https://doi.org/10.1002/wat2.1127
WILLS, M.; IRVINE, K. N. Application of the national sanitation foundation water quality index in Cazenovia Creek. Middle States Geographer, p. 95–104, 1996.
YAN, W.; LI, J.; LIU, M.; BAI, X.; SHAO, H. Data-based multiple criteria decision-making model and visualized monitoring of urban drinking water quality. Soft Computing, v. 21, p. 6031-6041, 2017. https://doi.org/10.1007/s00500-017-2809-y
YASEEN, Z. M.; RAMAL, M. M.; DIOP, L.; JAAFAR, O.; DEMIR, V.; KISI, O. Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation. Water Resources Management, v. 32, n. 7, p. 2227–2245, 2018. https://doi.org/10.1007/s11269-018-1915-7
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2023 Renan Felinto de Farias Aires, Camila Cristina Rodrigues Salgado