سامانه مدیریت نشریات علمی دانشگاه قم

پورعلی دوگاهه, منیژه, شعاعی, سید محمد. (1404). ارزیابی کیفی منابع آب رودخانه دربند در شرایط تغییر اقلیم با روش تصمیم‌گیری چندمعیاره TODIM. پژوهش‌های فلسفی -کلامی, 1(2), 37-48. doi: 10.22091/wrcc.2025.12792.1012
منیژه پورعلی دوگاهه; سید محمد شعاعی. "ارزیابی کیفی منابع آب رودخانه دربند در شرایط تغییر اقلیم با روش تصمیم‌گیری چندمعیاره TODIM". پژوهش‌های فلسفی -کلامی, 1, 2, 1404, 37-48. doi: 10.22091/wrcc.2025.12792.1012
پورعلی دوگاهه, منیژه, شعاعی, سید محمد. (1404). 'ارزیابی کیفی منابع آب رودخانه دربند در شرایط تغییر اقلیم با روش تصمیم‌گیری چندمعیاره TODIM', پژوهش‌های فلسفی -کلامی, 1(2), pp. 37-48. doi: 10.22091/wrcc.2025.12792.1012
پورعلی دوگاهه, منیژه, شعاعی, سید محمد. ارزیابی کیفی منابع آب رودخانه دربند در شرایط تغییر اقلیم با روش تصمیم‌گیری چندمعیاره TODIM. پژوهش‌های فلسفی -کلامی, 1404; 1(2): 37-48. doi: 10.22091/wrcc.2025.12792.1012

ارزیابی کیفی منابع آب رودخانه دربند در شرایط تغییر اقلیم با روش تصمیم‌گیری چندمعیاره TODIM

منابع آب و تغییر اقلیم
دوره 1، شماره 2، تیر 1404، صفحه 37-48    اصل مقاله (1.44 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22091/wrcc.2025.12792.1012
نویسندگان
منیژه پورعلی دوگاهه* 1؛ سید محمد شعاعی2
1دانشجوی دکتری، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه زنجان، زنجان، ایران
2گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه زنجان، زنجان، ایران.
چکیده
رودخانه‌ها به‌عنوان منابع حیاتی تأمین آب، نقش اساسی و غیرقابل انکاری در زندگی انسان‌ها ایفا می‌کنند. در سال‌های اخیر، بسیاری از رودخانه‌ها به‌تدریج به مکانی برای تخلیه فاضلاب‌های شهری، آلاینده‌های صنعتی و پساب‌های کشاورزی تبدیل شده‌اند. این تغییر نگرش در استفاده از منابع آبی، موجب بروز مشکلات جدی در کیفیت آب رودخانه‌ها شده است و به‌شدت بر زیست بوم‌های آبی تأثیر گذاشته است. هدف از انجام پژوهش حاضر، ارزیابی کیفیت آب رودخانه دربند با استفاده از شاخص کیفیت آب می‌باشد و در مرحله بعد، وزن چهار عامل بررسی شده و در شاخص کیفیت آب با استفاده از روش تصمیم‌گیری چند‌معیاره TODIM در این زمینه رتبه‌بندی
می‌شود. بدین‌منظور، ابتدا داده‌های ماهانه بارش، دما، TDS، EC، PH، SO4 طی دوره آماری 2005-1988 با استفاده از شاخص کیفیت آب، تحلیل می‌شوند. به‌منظور بررسی تاثیر تغییرات اقلیمی بر کیفیت آب رودخانه، ابتدا متغیرهای اقلیمی مربوطه برای دوره‌های آینده با کمک مدل GFDL-ESM2G برای شبیه‌سازی بارش و از مدل‌ MIROC ESM-CHEM برای شبیه‌سازی دما تحت سناریو‌های انتشار RCP 2.6، RCP 4.5 و RCP 8.5 شبیه‌سازی خواهند شد و سپس بین پارامترهای مؤثر بر دبی و پارامترهای کیفیت آب روابط رگرسیونی برقرار می‌‌شود و با توجه به پیش‌بینی انجام‌‌شده برای دبی در دوره 2047-2030، شرایط کیفیت آب رودخانه تحت سه سناریو محاسبه می‌شود و در نهایت با استفاده از TODIM پارامترها رتبه‌بندی می‌شوند. نتایج بررسی‌ها نشان می‌دهند که شاخص‌های کیفی آب در گزینه‌های مختلف، تفاوت‌هایی را نشان می‌دهند و می‌توان با بهره‌گیری از این نتایج، تأثیر پارامترهای کیفی را بر بهبود کیفیت آب مورد ارزیابی قرار داد.
کلیدواژه‌ها
شاخص کیفیت آب رودخانه؛ روش تصمیم‌گیری چندمعیاره TODIM؛ روش وزن‌دهی آنتروپی؛ تغییر اقلیم
عنوان مقاله [English]
Qualitative assessment of Darband River water resources under climate change conditions using the TODIM multi-criteria decision-making method
نویسندگان [English]
Manizheh Pourali Dougaheh1؛ Seyyed Mohammad Shoaei2
1Ph.D. Student, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran
2Department of Water and Hydraulic Structure Engineering, Faculty of Engineering, University of Zanjan, Iran.
چکیده [English]
Abstract
Objective: Rivers constitute vital freshwater resources and play an indispensable role in sustaining human life and aquatic ecosystems. In recent decades, however, many rivers have progressively become recipients of municipal wastewater, industrial effluents, and agricultural runoff. This shift in water resource utilization has led to severe deterioration of river water quality and has exerted significant pressure on aquatic ecosystems. The primary objective of the present study is to assess the water quality of the Darband River using the Water Quality Index (WQI). Subsequently, the relative importance of four key factors is evaluated and ranked within the WQI framework using the TODIM multi-criteria decision-making (MCDM) method.
Method: For this purpose, monthly data on precipitation, temperature, total dissolved solids (TDS), electrical conductivity (EC), pH, and sulfate (SO₄) over the statistical period of 1988–2005 were analyzed to determine the current status of river water quality. To investigate the potential impacts of climate change on river water quality, future climatic variables were projected using the GFDL-ESM2G model for precipitation and the MIROC-ESM-CHEM model for temperature under three representative concentration pathways (RCP 2.6, RCP 4.5, and RCP 8.5). Regression relationships were then established between the climatic and hydrological variables influencing river discharge and the key water quality parameters. Based on the projected river discharge for the period 2030–2047, water quality conditions were estimated under each emission scenario, and the corresponding parameters were subsequently ranked using the TODIM method.
Results: The results indicate noticeable variations in water quality indices across different scenarios, highlighting the differential influence of individual water quality parameters.
Conclusion: These findings provide a robust basis for evaluating the relative contribution of key parameters to water quality improvement and offer valuable insights for river basin management and climate-adaptive water quality planning.
 
 
 
English Extended Abstract
Introduction
Rivers, as vital sources of water supply, play a fundamental and undeniable role in human life. These water resources are not only essential for agricultural purposes, such as irrigating farmlands and providing food for human populations, but also for drinking and industrial uses. In recent years, many rivers have become primary destinations for discharging urban wastewater, industrial pollutants, and agricultural runoff. This shift in perspective regarding the use of water resources has led to serious problems in river water quality and has significantly impacted aquatic ecosystems. This study aims to assess the water quality of the Darband River using a water quality index. Subsequently, the weights of four factors will be examined and ranked in the water quality index using the TODIM multi-criteria decision-making method.
 
Method
In this section, the research process is described. Initially, after collecting baseline data and information for the study area, the performance of climate models is evaluated based on the Fifth Assessment Report of the IPCC. Subsequently, the relevant climatic variables for future periods will be simulated. Following this, the water quality status in both present and future conditions will be examined using the Water Quality Index, and finally, the influential qualitative parameters impacting water quality will be ranked, and their weights will be adjusted.
In this study, the relevant climatic variables were simulated by using the GFDL-ESM2G model for precipitation and the MIROC ESM-CHEM model for temperature in the baseline period (1988–2005) and near future (2030–2047) under three scenarios: RCP2.6, RCP4.5, and RCP8.5. Then, water quality in the baseline and future periods was assessed using the Iran Water Quality Index (WQIIR), the World Health Organization Water Quality Index (WQIWHO), and the Schuler classification. Finally, using the TODIM multi-criteria decision-making method, effective parameters were ranked based on water quality indices.
The entropy-Shannon weighting method was employed for weighting the criteria in this study. The entropy-Shannon method represents the dispersion of values in decision-making; the greater the degree of dispersion, the higher the level of deviation. The study area for this research is the Darband River Basin.
 
Results
This section presents the results of changes in water quality during the baseline and future periods, as well as weighting methods and the ranking of options, which represent water quality indices for the study area.
The relationships between water quality parameters and climatic factors such as temperature and precipitation were examined during the baseline period. The findings indicate an inverse relationship between rainfall and EC, TDS, and SO4. This suggests that an increase in precipitation leads to a reduction in EC and dissolved solids in surface waters. By substituting the projected values of minimum and maximum temperatures and precipitation into the derived relationships, the future values of each water quality parameter can be estimated.
Based on the regression relationships obtained for each parameter, their values were calculated under baseline conditions and different scenarios to assess the water quality of the Darband River using the WQIIR, WQIWHO, and Schuler classification indices. The results indicate that the water quality under baseline conditions is in a good state, and forecasts suggest that, for all three climate change scenarios, the water quality will remain in good condition in both indices. Since the coefficients and relative weights differ between the two indices, the magnitude of the indices also differs, with WQIWHO having higher values.
A decision matrix was formulated based on two criteria: minimizing WQIWHO and maximizing WQIIR, along with four options—TDS, EC, pH, and SO4—that each represent the relative weight of water quality parameters. To determine the weight of the criteria, the entropy of each criterion, its degree of deviation, and finally, its weight were calculated. The findings indicate that changes in options have a greater impact on maximizing WQIIR compared to minimizing WQIWHO.
The results also show that reducing EC has a greater impact on the criteria due to its higher relative weight compared to other parameters, and its reduction has a more significant effect on improving water quality.
 
Conclusion
The findings regarding climate change in the Darband River Basin indicate that maximum temperature under future scenarios will experience a significant increase compared to baseline conditions. Similarly, precipitation levels are also projected to show an upward trend in the future relative to the baseline period. Consequently, water quality parameters will also change. The results reveal that the river's water quality was in a favorable condition during the baseline period, and it is expected to maintain good quality in the future.
In the final stage, using the TODIM multi-criteria decision-making method, four selected alternatives were ranked under climate change conditions. The results indicate that water quality indices vary across different alternatives, and these findings can be used to determine the impact of each quality parameter on improving water quality. However, it is recommended that, for a more comprehensive assessment, the impact of other pollutants on this river should also be studied to develop a specific index for it.
کلیدواژه‌ها [English]
River Water Quality Index, TODIM Multi-Criteria Decision-Making Method, Entropy Weighting Method, Climate Change
مراجع

[1] Tiyasha T, Tung TM, Yaseen ZM. A survey on river water quality modelling using artificial intelligence models: 2000-2020. Journal of Hydrology. 2020 Jun; 585: 124670. https://doi.org/10.1016/j.jhydrol.2020.124670.

[2] Perveen Sh, Ul-Haque A. Drinking water quality monitoring, assessment and management in Pakistan: A review. Heliyon. 2023 Mar; 9(3): e13872. https://doi.org/10.1016/j.heliyon.2023.e13872.

[3] Jinturkar AM, Deshmukh SS, Agarkar SV, Chavhan GR. Determination of water quality index by fuzzy logic approach: A case of groundwater in an Indian town. Water Science & Technology. 2010; 61(8): 1987-1994. https://doi.org/10.2166/wst.2010.095.

[4] Park SS, Lee YS. A water quality modeling study of the Nakdong River, Korea. Ecological Modelling. 2002 Jun; 152(1): 65-75. https://doi.org/10.1016/S0304-3800(01)00489-6.

[5] Singh AP, Dhadse K, Ahalawat J. Managing water quality of a river using an integrated geographically weighted regression technique with fuzzy decision-making model. Environmental Monitoring and Assessment. 2019 May; 191 (378). https://doi.org/10.1007/s10661-019-7487-z.

[6] Chapman D. Water quality assessments: A guide to the use of biota, sediments and water in environmental monitoring. Published by UNESCO/WHO/UNEP; 1996.

[7] Ouyang T, Zhu Z, Kuang Y. River water quality and pollution sources in the Pearl River Delta, China. Journal of Environmental Monitoring. 2005 Jun; 7: 664-669. https://doi.org/10.1039/B504475H.

[8] Effendi H, Wardianto Y. Water quality status of Ciambulawung River, Banten Province, based on pollution index and NSF-WQI. Procedia Environmental Sciences. 2015; 24: 228-237. https://doi.org/10.1016/j.proenv.2015.03.30.

[9] Nourbakhsh Z, Mehrdadi N, Moharamnejad N, Hassani AH, Yousefi H. Evaluating the suitability of different parameters for qualitative analysis of groundwater based on analytical hierarchy process. Desalination and Water Treatment. 2016 Jun; 57 (28): 13175-13182. https://doi.org/10.1080/19443994.2015.1056837.

[10] Ahmadianfar I, Jamei M, Chu X. A novel hybrid wavelet-locally weighted linear regression (W-LWLR) model for electrical conductivity (EC) prediction in surface water. Journal of Contaminant Hydrology. 2020 Jun; 232: 103641. https://doi.org/10.1016/j.jconhyd.2020.103641.

[11] Khalili R, Montaseri H, Mottaghi H, Jalili M. Evaluation of water quality in the Chalus River using the statistical analysis and water quality index (WQI). Water and Soil Management and Modeling. 2021 Aug; 1(3): 38-52. https://doi.org/10.22098/MMWS.2021.9300.1031 [In Persian].

[12] Mishra AP, Singh S, Jani M, Singh KA, Pande ChB, Varade AM. Assessment of water quality index using Analytic Hierarchy Process (AHP) and GIS: a case study of a struggling Asan River. International Journal of Environmental Analytical Chemistry. 2022 Jan; 104(5): 1159-1171. https://doi.org/10.1080/03067319.2022.2032015.

[13] Aldrees A, Javed MF, Taha ATB, Mohamed AM, Jasiński M, Gono M. Evolutionary and ensemble machine learning predictive models for evaluation of water quality. Journal of Hydrology: Regional Studies. 2023 Apr; 46: 101331. https://doi.org/10.1016/j.ejrh.2023.101331.

[14] Patel DD, Mehta DJ, Azamathulla HM, Shaikh MM, Jha Sh, and Rathnayake U. Application of the weighted arithmetic water quality index in assessing groundwater quality: A case study of the South Gujarat region. Water. 2023 Oct; 15(19): 3512. https://doi.org/10.3390/w15193512.

[15] Das A. Water pollution and water quality assessment and application of criterion impact loss (CILOS), geographical information system (GIS), artificial neural network (ANN) and decision-learning technique in river water quality management: An experiment on the Mahanadi catchment, Odisha, India. Desalination and Water Treatment. 2025 Jan; 321: 100969. https://doi.org/10.1016/j.dwt.2024.100969.

[16] Das A. Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha. HydroResearch. 2025 Jan; 8: 244-264. https://doi.org/10.1016/j.hydres.2024.12.002.

[17] Shannon CE. A Mathematical Theory of Communication. The Bell System Technical Journal. 1948 Jul; 27 (3), 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

[18] Gomes LFAM, Lima MMPP. TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences. 1992 Jan; 16, 113-127.

[19] Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica. 1979 Mar; 47 (2): 263-291. https://doi.org/10.2307/1914185.

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