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تعیین پتانسیل آب زیرزمینی منطقه تربت‌جام با استفاده از روش بیشینه آنتروپی و تحلیل سلسله مراتبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مرتع و آبخیزداری، دانشکده کشاورزی، مجتمع آموزش عالی شیروان، شیروان، ایران

2 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

چکیده

با افزایش جمعیت، اهمیت منابع آب زیرزمینی به­‌عنوان یکی از مهمترین منابع تأمین‌کننده آب شرب در مناطق خشک بیش ‌از پیش آشکار می‌شود. در این پژوهش، به‌منظور تعیین مناطق دارای پتانسیل آب زیرزمینی شهرستان تربت‌جام و اولویت‌­بندی عوامل موثر از روش­‌های تحلیل سلسله مراتبی و روش بیشینه آنتروپی با استفاده از مدل MaxEnt و عوامل فاصله و تراکم گسل، سنگ‌­شناسی، شیب، جهت شیب، فاصله از آبراهه و تراکم زهکشی، طبقات ارتفاعی، کاربری اراضی، انحنای دامنه، شاخص رطوبت توپوگرافیک و شاخص موقعیت توپوگرافیک استفاده شده است. همچنین، برای ارزیابی این دو روش، از منحنی تشخیص عملکرد نسبی (ROC) استفاده شد. از مجموع 220 چشمه موجود، به‌صورت تصادفی 30 درصد به­‌عنوان داده­‌های اعتبارسنجی و 70 درصد به­‌عنوان داده‌­های آزمون (روش بیشینه آنتروپی) طبقه‌­بندی شدند. بر اساس روش بیشینه آنتروپی، نتایج نشان داد که 29.6 درصد حوزه آبخیز دارای پتانسیل آب زیرزمینی بالایی است. بر اساس نمودار جکنایف، لایه­‌های ارتفاع (DEM)، شیب، فاصله از گسل و سنگ­‌شناسی به‌ترتیب مهمترین عوامل تاثیرگذار بر پتانسیل آب زیرزمینی بودند. سطح زیر منحنی (AUC) در روش بیشینه آنتروپی، نشان­‌دهنده دقت 91 درصد (عالی) در مرحله آموزش و 80 درصد (خیلی خوب) در مرحله اعتبارسنجی برای تعیین مناطق دارای پتانسیل آب زیرزمینی بود. بر اساس روش تحلیل سلسله مراتبی، 34.4 درصد حوضه دارای پتانسیل آب زیرزمینی می­‌باشد و لایه‌­های شیب، سنگ‌­شناسی، ارتفاع و فاصله از گسل به‌ترتیب مهمترین عوامل تاثیرگذار بودند و دقت این روش 73 درصد برآورد شد. نتایج نشان داد که به‌کارگیری روش­‌های تحلیل سلسله مراتبی و روش بیشینه آنتروپی، ضمن صرفه‌­جویی در زمان و هزینه، قابلیت مناسبی در پیش‌بینی پتانسیل آب زیرزمینی دارند و روش بیشینه آنتروپی برتری بیشتری نسبت به روش تحلیل سلسله مراتبی دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Delineation of groundwater potential zones in Torbate Jam district using maximum entropy and AHP methods

نویسندگان [English]

  • Mehdi Teimouri 1
  • Omid Asadi Nalivan 2

1 Department of Range and watershed management, Faculty of Agriculture, Higher Education Complex of Shirvan, Shirvan, Iran

2 Gorgan University of Agricultural Sciences and Natural Resources

چکیده [English]

With the increase in population, the importance of groundwater resources as one of the most important sources of drinking water in the arid regions becomes apparent. In this research, in order to determine the areas with groundwater potential in the city of Torbate Jam and prioritizing the effective factors, hierarchical analysis methods and maximum entropy method using MaxEnt model and the factors of distance from fault and fault density, lithology, slope, slope direction, distance from the waterway and drainage density, elevation, land use, slope curvature, topographic humidity index and topographic position indicator was used. Also, for assessing these two methods, the Receiver Operating Characteristic (ROC) was used. From 220 sources, 30% were randomly assigned as validation data and 70% were categorized as test data in maximum entropy method. Results showed that 29.6% of the watershed had high groundwater potential according to the maximum entropy method. Based on Jack-Knife Diagram, DEM, slope, distance from fault and lithology were the most important factors affecting groundwater potential, respectively. The Area Under the Curve (AUC) in the maximum entropy method indicated a precision of 91% (excellent) at the training period and 80% (very good) in the validation period to determine areas with potential for groundwater. Based on AHP method, 34.4% of the area has groundwater potential, and the slope layers, lithology, elevation and distance from the faults were the most important factors, respectively and accuracy of this method was 73%. The results showed that applying AHP and maximum entropy methods, while saving time and cost, have a good ability to predict the potential of groundwater and the maximum entropy method has more superiority than the hierarchical analysis method.

کلیدواژه‌ها [English]

  • AHP
  • MaxEnt
  • Receiver Operating Characteristic
  • Validation
  • Water resources
مراجع
  1. Adiat, K.A., N.M. Nawawi and K. Abdullah. 2012. Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool: a case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology, 440: 75-89.
  2. Agarwal, R. and P.K. Garg. 2016. Remote sensing and GIS based groundwater potential and recharge zones mapping using multi criteria decision analysis making technique. Water Resources Management, 30: 243-260.
  3. Al-Abadi, A., A. Al-Temmeme and A. Al-Ghanimy. 2016. A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Water Resources Management, 2(3): 265-283.
  4. Arabameri, A.R., M. Sohrabi, K. Rezaei, M. Yamani and K. Shirani. 2018. Simulation of Najaf-Abad Watershed groundwater using data driven ensemble model EBF-Index of entropy. Journal of Water and Soil Conservation, 25(2): 25-48 (in Persian).
  5. Arulbalaji, P., D. Padmalal and K. Sreelash. 2019. GIS and AHP techniques-based delineation of groundwater potential zones: a case study from Southern Western Ghats, India. Scientific Reports, 9: 20-42.
  6. Deepa, S., S. Venkateswaran, R. Ayyandurai, R. Kannan and V. Prabhu. 2016. Groundwater recharges potential zones mapping in upper Manimuktha Sub-basin Vellar River India using GIS and remote sensing techniques. Modeling Earth Systems and Environment, 2: 137-149.
  7. Deng, F., Z. Deng, D. Lv, D. Wang, H. Duan and Z. Xing. 2016. Application of remote sensing and GIS analysis in groundwater potential estimation in west Liaoning Province, China. Journal of Engineering Research, 4(3): 1-17.
  8. Ghorbani Nejad, S., F. Falah, M. Daneshfar, A. Haghizadeh and O. Rahmati. 2017. Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International, 32(2): 167–187.
  9. Golkarian, A. and O. Rahmati. 2018. Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran. Environmental Earth Sciences, 77: 369-389.
  10. Golkarian, A., S.A. Naghibi, B. Kalantar and B. Pradhan. 2018. Groundwater potential mapping using C5.0, random forest and multivariate adaptive regression spline models in GIS. Environmental Monitoring and Assessment, 190: 149-168.
  11. Guo-Liang, D., Z. Shuang, I. Javed and Y. Xin. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang Watershed, Gansu Province, China. Journal of Mountain Science, 14(2): 249-268.
  12. Haghizade, A., D. Moghaddam and H. Pourghasemi. 2017. GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran). Journal of Earth System Science, 126: 109-135.
  13. Jothibasu, A. and S. Anbazhagan. 2016. Modeling groundwater probability index in Ponnaiyar River Basin of South India using Analytic Hierarchy Process, Model. Earth Systems and Environment, 2: 109-132.
  14. Kazemi, R., S. Shadfar and R. Bayat. 2016. Investigation of the effective elements in water resource exploration of the hard formations, case study: Lar Catchment. Journal of Watershed Engineering and Management, 7(4): 389-401 (in Persian).
  15. Kordestani, M.D., S.A. Naghibi, H. Hashemi, K. Ahmadi, B. Kalantar and B. Pradhan. 2018. Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology, 27: 1–14.
  16. Lee, S., Y. Hyun and M. Lee. 2019. Groundwater potential mapping using data mining models of big data analysis in Goyang-Si, South Korea. Sustainability, 11: 16-28.
  17. Manap, M.A., N.A. Sulaiman, M.F. Ramli, B. Pradhan and N. Surip. 2013. A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arabian Journal of Geosciences, 6: 1621–1637.
  18. Mokarram, M., G. Roshan and S. Negahban. 2015. Landform classification using topography position index, case study: salt dome of Korsia-Darab Plain, Iran. Modeling Earth Systems and Environment, 1: 40-59.
  19. Mousavi, S.M., A. Golkarian, S.A. Naghibi, B. Kalantar and B. Pradhan. 2017. GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. AIMS Geosciences, 3: 91–115.
  20. Naghibi, S.A., H.R. Pourghasemi and K. Abbaspour. 2018. A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using RS and GIS. Theoretical and Applied Climatology, 131(3–4): 967–984.
  21. Naghibi, S.A., H.R. Pourghasemi, Z.S. Pourtaghie and A. Rezaei. 2014. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan Watershed, Iran. Earth System Sciences, 8(1): 171-186.
  22. Phillips, S.J., R.P. Anderson and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modeling, 190: 231–259.
  23. Rahimi, M. and K. Solaimani. 2017. Remote sensing and GIS-based assessment groundwater potential zones mapping using multi-criteria decision-making technique. Iran Watershed Management Science and Engineering, 10(35): 27-38 (in Persian).
  24. Rahmati, O., A. Nazari Samani, M. Mahdavi, H.R. Pourghasemi and H. Zeinivand. 2015. Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8: 7059–7071.
  25. Rahmati, O., H.R. Pourghasemi and A.M. Melesse. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping, a case study at Mehran region, Iran. Catena, 137: 360–372.
  26. Razandi, Y., H.R. Pourghasemi, N. Samani Neisani and O. Rahmati. 2015. Application of analytical hierarchy process, frequency ratio and certainty factor models for groundwater potential mapping using GIS. Earth Science Informatics, 8(4): 867-883.
  27. Razavi, V., M. Mesgari and K. Kazemi. 2017. Evaluation and comparison of frequency ratio, statistic index and entropy methods for groundwater potential mapping using GIS, case study: Jahrom Township. Journal of Ecohydrology, 4(3): 725-736 (in Persian).
  28. Saaty, T.L. 1980. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill, New York, 287 pages.
  29. Sahoo, S., S.B. Munusamy, A. Dhar, A. Kar and P. Ram. 2017. Appraising the accuracy of multi-class frequency ratio and weights of evidence method for delineation of regional groundwater potential zones in canal command system. Water Resources Management, 31: 4399–4413.
  30. Saraf, A. and P.R. Chaudhary. 2005. Integrated remote sensing and GIS for groundwater exploration and identification of artificial recharges sites. International Journal of Remote Sensing, 19(10): 1825-1841.
  31. Sener, E., S. Sener and A. Davraz. 2018. Groundwater potential mapping by combining fuzzy analytic hierarchy process and GIS in Beysehir Lake Basin, Turkey. Arabian Journal of Geosciences, 11: 1–21.
  32. Tehrany, M.S., B. Pradhan and M.N. Jebur. 2013. Spatial prediction of flood susceptible areas using rule based Decision Tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504: 69-79.
  33. Thilagavathi, N., T. Subramani, M. Suresh and D. Karunanidhi. 2015. Mapping of groundwater potential zones in Salem Chalk Hills, Tamil Nadu, India, using Remote Sensing and GIS techniques. Environment Monitoring Assessment, 187(164): 1-17.
  34. Yang, X.Q., S.P.S. Kushwaha, S. Saran, J. Xu and P.S. Roy. 2013. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Journal of Ecological Engineering, 51: 83–87.
  35. Yeh, H.F., Y.S. Cheng, H. Lin and C. Lee. 2016. Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustainable Environment Research, 26: 33–43.
  36. Zabihi, M., H.R. Pourghasemi and M. Behzadfar. 2015. Groundwater potential mapping using Shannon's entropy and random forest models in the Bojnourd Township. Journal of Ecohydrology, 2(2): 221-232 (in Persian).
مهندسی و مدیریت آبخیز
دوره 13، شماره 2
تیر 1400
صفحه 339-354
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هم رسانی
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