توسعۀ شاخص کیفی برای ارزیابی آب‏ زیرزمینی و پیش ‏بینی تغییرات آن با مدل شبکۀ بیزین (مطالعۀ موردی: دشت زنجان)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی منابع آب، گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران

2 استاد گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران

3 دانشیار گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران

4 کارشناس پژوهشی پژوهشکدۀ مطالعات و تحقیقات منابع آب، مؤسسۀ تحقیقات آب، تهران

چکیده

‌آب‌های ‏زیرزمینی یکی از منابع مهم تأمین آب به‌خصوص در مناطق خشک و کم‌بارش به ‏شمار می‏رود. از این‏رو، تعیین کیفیت و پیش‏بینی آن امری ضروری است. مطالعۀ حاضر به ارزیابی کیفیت منابع آب ‏زیرزمینی و پیش‏بینی آن در آبخوان زنجان می‏پردازد. شاخص GWQI در پژوهش‌های پیشین، وزن‌دهی ساده بر پایۀ دیدگاه‌های کارشناسی بوده است. از این‌رو، در شاخص جدید (C-GWQI) برای تعیین وزن‏ها، از روش آنتروپی شانون و از تصمیم‏گیری چند‏معیارۀ COPRAS، به منظور توسعۀ این شاخص استفاده شد. با تعریف دو محدودۀ کیفی مجاز و مطلوب برای مصارف شرب طبق استاندارد WHO، کیفیت آبخوان در سه محدودۀ مطلوب، مجاز و غیرمجاز برای طبقه‏بندی آب شرب استفاده شد. نتایج نشان داد در همۀ دوره‏های زمانی سطح کیفیت آب ‏زیرزمینی در محدودۀ شهری پایین‏تر از سایر نقاط است. با این حال، در بیشتر چاه‏های بررسی‌شده، کیفیت آب برای شرب ارزیابی مطلوب شد. شاخص توسعه داده‌شده با استفاده از مدل شبکۀ بیزین تحت ۸ راهبرد ساختاری ارزیابی و پیش‏بینی شد و از بین راهبردهای مختلف، با توجه به معیارهای میانگین مطلق خطای نسبی (MARE) و ضریب همبستگی (R) راهبرد برتر انتخاب شد. راهبرد برتر کیفیت آب‏ زیرزمینی در مرحلۀ آموزش و آزمون به‌ترتیب دارای مقادیر ۹۳۲/۱ و ۹۹۲/۰ درصد از نظر شاخص MARE ارزیابی شد. پارامترهای پیش‏بینی‌کنندۀ راهبرد منتخب شامل آب‏ برگشتی، تخلیه، بارش، دما و کیفیت این ماه توانستند با دقت زیادی کیفیت ماه بعد را پیش‏بینی کنند. نتایج مطالعۀ حاضر می‏تواند به مدیران برای حفظ و مدیریت بهتر آبخوان کمک کند.

کلیدواژه‌ها


عنوان مقاله [English]

Development Bayesian Model for Forecasting Groundwater Quality Index (Case Study: Zanjan Plain)

نویسندگان [English]

  • Saeed Mozaffari 1
  • Mohammad Ebrahim Banihabib 2
  • Saman Javadi 3
  • Hamid Kardan Moghaddam 4
1 MSc Student, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Iran
2 Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Iran
3 Associate Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Iran
4 Department of Water Resources Research, Water Research Institute, Tehran, Iran
چکیده [English]

Determining and forecasting groundwater quality can be a primary step for managing aquifer sustainability. This study investigates and forecasts groundwater quality in Zanjan Aquifer. In the previous studies, the GWQI index is a simple weighting based on expert opinions. Thus, in the developing a new index (C-GWQI), for weighting, the Shannon entropy method and the COPRAS multi-criteria decision-making technique were used. In this research, COPRAS Multi Criteria Decision Making Technique was utilized to develop the new index (C-GWQI). By defining two permissible and desirable points of drinking water according to the WHO standard, aquifer quality was classified into three ranges including, desirable, permissible and non-permissible for drinking water. The results showed that in all periods of time, groundwater quality is lower in urban areas than in other areas. However, in most of the wells surveyed, the water quality was evaluated in desirable range for drinking. The developed index was forecasted using the Bayesian network model under eight structural strategies and the best-case strategy was selected according to mean absolute relative error (MARE) and correlation coefficient (R). The best strategy was forecasted next month's groundwater quality with MARE of training and test respectively of 1.932% and 0.992%. This strategy was able to forecast the following month with good accuracy with predictor parameters such as return water, discharge, precipitation, temperature, and quality of this month. The results of this study can assist managers to better conserve and manage the aquifer.

کلیدواژه‌ها [English]

  • Bayesian network
  • COPRAS
  • Groundwater quality
  • Zanjan Aquifer
[1]. Germolec DR, Yang RS, Ac kermann MF, Rosenthal GJ, Boorman GA, Blair P, et al. Toxicology studies of a chemical mixture of 25 groundwater contaminants: II. Immunosuppression in B6C3F1 mice. Toxicological Sciences. 1989;13(3):377-87.
[2]. Huiqun M, Ling L, editors. Water quality assessment using artificial neural network. 2008 International Conference on Computer Science and Software Engineering; 2008: IEEE.
[3]. Horton RK. An index number system for rating water quality. Journal of Water Pollution Control Federation. 1965;37(3):300-6.
[4]. Miller W, Joung H, Mahannah C, Garret J. Identification of Water Quality Differences in Nevada Through Index Application 1. Journal of Environmental Quality. 1986;15(3):265-72.
[5]. Ramakrishnaiah C, Sadashivaiah C, Ranganna G. Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka State, India. Journal of Chemistry. 2009;6(2):523-30.
[6]. Vasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Ganthi RR, Chidambaram S, Anandhan P, et al. Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India. Environmental monitoring and assessment. 2010;171(1-4):595-609.
[7]. Logeshkumaran A, Magesh N, Godson PS, Chandrasekar N. Hydro-geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India. Applied Water Science. 2015;5(4):335-43.
[8]. Barba-Romero S, Pomerol JC. Multicriterion Decision in Management: principles and practice. Operations Research Management Science, Massachusetts. 2000.
[9]. Zavadskas EK, Turskis Z. Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy. 2011;17(2):397-427.
[10].            Cooper W. Seiford. LM and Tone, K.(2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Boston: Kluwer Academic Publishers; 2007.
[11].            Üstün AK. Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis. Natural Hazards. 2016;80(3):1603-23.
[12].            Üstün AK, Barbarosoğlu G. Performance evaluation of Turkish disaster relief management system in 1999 earthquakes using data envelopment analysis. Natural Hazards. 2015;75(2):1977-96.
[13].            Banai-Kashani R. A new method for site suitability analysis: The analytic hierarchy process. Environmental management. 1989;13(6):685-93.
[14].            Jha MK, Chowdary V, Chowdhury A. Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeology journal. 2010;18(7):1713-28.
[15].            Do HT, Lo S-L, Thi LAP. Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP). Environmental monitoring and assessment. 2013;185(1):909-16.
[16].            Jeihouni M, Toomanian A, Shahabi M, Alavipanah S. Groundwater quality assessment for drinking purposes using GIS modelling (case study: city of Tabriz). The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014;40(2):163.
[17].            Kavurmaci M, Üstün AK. Assessment of groundwater quality using DEA and AHP: a case study in the Sereflikochisar region in Turkey. Environmental monitoring and assessment. 2016;188(4):258.
[18].            Minh HVT, Avtar R, Kumar P, Tran DQ, Ty TV, Behera HC, et al. Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam. Geosciences. 2019;9(8):330.
[19].            Chatterjee P, Athawale VM, Chakraborty S. Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials & Design. 2011;32(2):851-60.
 
[20].            Das MC, Sarkar B, Ray S. A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology. Socio-Economic Planning Sciences. 2012;46(3):230-41.
[21].            Maiti S, Das A, Shah R, Gupta G. Application of automatic relevance determination model for groundwater quality index prediction by combining hydro-geochemical and geo-electrical data. Modeling Earth Systems and Environment. 2017;3(4):1371-82.
[22].            Nezhad MF, Abbasi M, Markarian S. A novel method for modeling effluent quality index using Bayesian belief network. International Journal of Environmental Science and Technology. 2019;16(11):7071-80.
[23].            Ammar K, McKee M, Kaluarachchi J. Bayesian method for groundwater quality monitoring network analysis. Journal of Water Resources Planning and Management. 2009;137(1):51-61.
[24].            Hantush MM, Chaudhary A. Bayesian framework for water quality model uncertainty estimation and risk management. Journal of Hydrologic Engineering. 2013;19(9):04014015.
[25].            Venkatramanan S, Chung S, Ramkumar T, Rajesh R, Gnanachandrasamy G. Assessment of groundwater quality using GIS and CCME WQI techniques: a case study of Thiruthuraipoondi city in Cauvery deltaic region, Tamil Nadu, India. Desalination and Water Treatment. 2016;57(26):12058-73.
[26].            Abbasnia A, Yousefi N, Mahvi AH, Nabizadeh R, Radfard M, Yousefi M, et al. Evaluation of groundwater quality using water quality index and its suitability for assessing water for drinking and irrigation purposes: Case study of Sistan and Baluchistan province (Iran). Human and Ecological Risk Assessment: An International Journal. 2019;25(4):988-1005.
[27].            World Health Organization (WHO. Information Products: Water, Sanitation and Health. World Health Organization (WHO); 2004.
[28].            Kaklauskas A, Zavadskas EK, Raslanas S, Ginevicius R, Komka A, Malinauskas P. Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and buildings. 2006;38(5):454-62.
[29].            Zavadskas E, Kaklauskas A, editors. Determination of an efficient contractor by using the new method of multicriteria assessment. International Symposium for “The Organization and Management of Construction” Shaping Theory and Practice; 1996.
[30].            Pearl J. Probabilistic Reasoning in Intelligent Systems. Representation & Reasoning. Morgan Kaufmann Publishers San Mateo; 1988.
[31].            Aguilera P, Fernández A, Fernández R, Rumí R, Salmerón A. Bayesian networks in environmental modelling. Environmental Modelling & Software. 2011;26(12):1376-88.
[32].            Uusitalo L. Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling. 2007;203(3-4):312-8.
[33].            Farmani R, Henriksen HJ, Savic D. An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination. Environmental Modelling & Software. 2009;24(3):303-10.
[34].            Reza Nemati A, Nemati B, Reza Pirestani M. Qualitative monitoring of Saveh plain's Groundwater based on water quality index (WQI). European Online Journal of Natural and Social Sciences: Proceedings. 2014;3(3 (s)):pp. 236-41.