Document Type : Research

Authors

1 BSc. in Mapping Engineering, Jundishapur University of Technology, Dezful, Iran

2 Assistant Professor, Department of Geography, Payame Noor University, Tehran, Iran

3 MA in Hydrology and Director of Saghez Water Resources Affairs, Saghez, Iran

4 Meteorology Expert, Kurdistan Province Meteorological Office, Sanandaj, Iran

Abstract

Such issues as increase of population, industrial development, illegal harvesting, increase in cultivated areas, and drought have increased the use of underground water resources in recent years and the level of underground water is decreasing  every day. Therefore, the identification of underground water resources, its optimal use and its management will result in sustainable use of the precious resources. Weighted overlap analysis using multi-criteria decision-making techniques (MCDM), fuzzy hierarchical analytical process (FAHP), GIS, and remote sensing knowledge were used to find the potential of underground water resources in Saqqez city watershed. The data of 740 wells and 14 geological, meteorological, topographical, hydrological and environmental parameters were prepared and processed by GIS to produce a potential map of underground water resources. By using the system performance curve (ROC) for verifying, the potential map of underground water resources was estimated. By analyzing the distribution of existing open wells, the value of AUC = 83.7% shows that the model has successfully predicted groundwater. Statistical analysis showed that 8.74% and 18.48% of the total area of Saghez city are in very good and good potential area, and 31.48% and 34.25% are in the medium and poor potential area, respectively, and 7.05% are in the area with very poor potential. The findings will help policy makers and managers in planning and sustainable managing of groundwater resources.

Keywords

Main Subjects

  1. حجازی ­زاده، ز.، خسروی، آ.، حسینی، س ا.، رحیمی، ع.، و کربلائی، علیرضا. (1400). پتانسیل‌سنجی مناطق کویری، بیابانی و سواحل مکران به منظور کسب انرژی از خورشید با استفاده از منطق فازی و مدل تحلیل سلسله­ مراتبی. فصلنامه تحقیقات کاربردی علوم جغرافیایی، 21(63)، 18-1.
  2. حسینی، س. ا.، احمدی، ح.، و هوشیار، م. (1400). واکاوی سامانه بارشی‌ مولد سیلاب تیرماه 1394 شهرستان سقز در استان کردستان. فصلنامه مدیریت جامع حوزه ­های آبخیز، 1(1)، 47-65.
  3. رنجبری، م ر.، واقعی، ر.، و بیگدلی، ب. (1397). پتانسیل­ یابی منابع آب زیرزمینی با استفاده از تصاویر ماهواره‌ای و GIS و تلفیق لایه‌های اطلاعاتی به روش تحلیل سلسله مراتبی AHP. هفتمین کنفرانس ملی مدیریت منابع آب ایران، دانشگاه یزد.
  4. جلالی، س.، صمدی، م.، صمدی قشلاقچایی، م.، و کرنژادی آ.(1395). بررسی شاخص های مورفومتری در حوزه آبخیز چهل چای استان گلستان با استفاده از GIS. نشریه علمی ترویجی مهندسی نقشه برداری و اطلاعات مکانی، 7(4)، 37-48.
  5. علیزاده، ا. (1389). اصول هیدرولوژی کاربری (چاپ سی‌ویکم). مشهد: انتشارات دانشگاه امام رضا (ع).
  6. کردوانی، پ.، موغلی، م.، و فرخی، ح. (1391). ارزیابی سد خاکی لاور بر آب‌های زیرزمینی دشت لاور فین (بندرعباس)، فصلنامه جغرافیایی طبیعی، 5(15)، 1-9.
  7. گودرزی، م.، احمدی، ح.، و حسینی، س ا. (1396). بررسی ارتباط شاخص ­های پیوند از دور با مؤلفه­های بارشی و دمایی (مطالعه موردی: ایستگاه همدید کرج). فصلنامه اکوهیدرولوژی، 4(3)، 651-641.
  8. ماه گلی، آ.، چیت‌سازان م.، و میرزایی، ی. (1390). پتانسیل­یابی آب زیرزمینی در سازندهای سخت با استفاده GIS و سنجش از دور (مطالعه موردی: شمال حسینه). همایش ژئوماتیک، سازمان نقشه برداری کشور، تهران.

 

  1. Adeyeye, O. A., Ikpokonte, E. A., & Arabi, S. A. (2019). GIS-based groundwater potential mapping within Dengi area, North Central Nigeria. The Egyptian Journal of Remote Sensing and Space Science22(2), 175-181.
  2. Al-Ruzouq, R., Shanableh, A., Yilmaz, A. G., Idris, A., Mukherjee, S., Khalil, M. A., & Gibril, M. B. A. (2019). Dam site suitability mapping and analysis using an integrated GIS and machine learning approach. Water11(9), 1880.
  3. Al-Saady, Y. I., Al-Suhail, Q. A., Al-Tawash, B. S., & Othman, A. A. (2016). Drainage network extraction and morphometric analysis using remote sensing and GIS mapping techniques (Lesser Zab River Basin, Iraq and Iran). Environmental Earth Sciences75(18), 1-23.
  4. Arefin, R. (2020). Groundwater potential zone identification using an analytic hierarchy process in Dhaka City, Bangladesh. Environmental Earth Sciences, 79(11), 1-16.
  5. Arulbalaji, P., Padmalal, D., & Sreelash, K. (2019). GIS and AHP techniques based delineation of groundwater potential zones: A case study from southern Western Ghats, India. Scientific Reports9(1), 1-17.
  6. Balezentiene, L., Streimikiene, D., & Balezentis, T. (2013). Fuzzy decision support methodology for sustainable energy crop selection. Renewable and Sustainable Energy Reviews17, 83-93.
  7. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems17(3), 233-247.
  8. Çelik, R. (2019). Evaluation of groundwater potential by GIS-based multicriteria decision making as a spatial prediction tool: Case study in the Tigris River Batman-Hasankeyf Sub-Basin, Turkey. Water11(12), 2630.
  9. Chen, V. Y., Lien, H. P., Liu, C. H., Liou, J. J., Tzeng, G. H., & Yang, L. S. (2011). Fuzzy MCDM approach for selecting the best environment-watershed plan. Applied Soft Computing11(1), 265-275.
  10. Chen, W. P., & Lee, C. H. (2003). Estimating ground-water recharge from streamflow records. Environmental Geology44(3), 257-265.
  11. Chen, W., Zhao, X., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., & Ahmad, B. B. (2020). Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. Journal of Hydrology583, 124602.
  12. Chowdhury, A., Jha, M. K., & Chowdary, V. M. (2010). Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district, West Bengal, using RS, GIS and MCDM techniques. Environmental Earth Sciences59(6), 1209-1222.
  13. Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., & Chacón, J. (2012). Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain). Natural Hazards and Earth System Sciences12(2), 327-340.
  14. Dar, T., Rai, N., & Bhat, A. (2021). Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP). Geology, Ecology, and Landscapes, 5(4), 292-307.
  15. Das, S. (2017). Delineation of groundwater potential zone in hard rock terrain in Gangajalghati block, Bankura district, India using remote sensing and GIS techniques. Modeling Earth Systems and Environment3(4), 1589-1599.
  16. Das, S. (2019). Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India. Groundwater for Sustainable Development8, 617-629.
  17. Das, S., & Pardeshi, S. D. (2018). Morphometric analysis of Vaitarna and Ulhas river basins, Maharashtra, India: using geospatial techniques. Applied Water Science8(6), 1-11.
  18. Deng, F., Deng, Z., Lv, D., Wang, D., Duan, H., & Xing, Z. (2016). Application of remote sensing and GIS analysis in groundwater potential estimation in west Liaoning Province, China. Journal of Engineering Research4(3), 1-17.
  19. Eini, M., Kaboli, H. S., Rashidian, M., & Hedayat, H. (2020). Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction50, 101687.
  20. Eliades, M., Bruggeman, A., Lubczynski, M. W., Christou, A., Camera, C., & Djuma, H. (2018). The water balance components of Mediterranean pine trees on a steep mountain slope during two hydrologically contrasting years. Journal of Hydrology562, 712-724.
  21. Elvis, B. W. W., Arsène, M., Théophile, N. M., Bruno, K. M. E., & Olivier, O. A. (2022). Integration of shannon entropy (SE), frequency ratio (FR) and analytical hierarchy process (AHP) in GIS for suitable groundwater potential zones targeting in the Yoyo river basin, Méiganga area, Adamawa Cameroon. Journal of Hydrology: Regional Studies, 39, 100997.
  22. Feizizadeh, B., Roodposhti, M. S., Jankowski, P., & Blaschke, T. (2014). A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers & Geosciences73, 208-221.
  23. Gorsevski, P. V., & Jankowski, P. (2010). An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Computers & Geosciences36(8), 1005-1020.
  24. Halder, S., Roy, M. B., & Roy, P. K. (2020). Fuzzy logic algorithm based analytic hierarchy process for delineation of groundwater potential zones in complex topography. Arabian Journal of Geosciences, 13(13), 1-22.
  25. Haque, M., Siddika, S., Sresto, M. A., Saroar, M., & Shabab, K. R. (2021). Geo-spatial analysis for flash flood susceptibility mapping in the North-East Haor (Wetland) Region in Bangladesh. Earth Systems and Environment5(2), 365-384.
  26. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Chen, W. (2018). Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment625, 575-588.
  27. Hsieh, T. Y., Lu, S. T., & Tzeng, G. H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management22(7), 573-584.
  28. Huajie, D., Zhengdong, D., & Feifan, D. (2016). Classification of groundwater potential in Chaoyang area based on QUEST algorithm. In 2016 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 890-893). New York: IEEE.
  29. Jaafarzadeh, M. S., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R., & Rouhani, H. (2021). Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Scientific Reports, 11(1), 1-18.
  30. Kahraman, C., & Kaya, İ. (2010). Investment analyses using fuzzy probability concept. Technological and Economic Development of Economy16(1), 43-57.
  31. Kaliraj, S., Chandrasekar, N., & Magesh, N. S. (2014). Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arabian Journal of Geosciences7(4), 1385-1401.
  32. Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., & Nasseri, M. (2019). A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of Hydrology572, 17-31.
  33. Kayastha, P., Dhital, M. R., & De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers & Geosciences52, 398-408.
  34. Li, S. P., & Will, B. F. (2005). A fuzzy logic system for visual evaluation. Environment and Planning B: Planning and Design32(2), 293-304.
  35. Machiwal, D., Jha, M. K., & Mal, B. C. (2011). Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management25(5), 1359-1386.
  36. Malczewski, J., & Rinner, C. (2015). Multicriteria decision analysis in geographic information science. New York, (Vol. 1). Cham: Springer.
  37. Mallast, U., Gloaguen, R., Geyer, S., Rödiger, T., & Siebert, C. (2011). Semi-automatic extraction of lineaments from remote sensing data and the derivation of groundwater flow-paths. Hydrology & Earth System Sciences Discussions15(8), 2665–2678.
  38. Mallick, J., Singh, C. K., Al‐Wadi, H., Ahmed, M., Rahman, A., Shashtri, S., & Mukherjee, S. (2015). Geospatial and geostatistical approach for groundwater potential zone delineation. Hydrological Processes29(3), 395-418.
  39. Manap, M. A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W. N. A., & Ramli, M. F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences7(2), 711-724.
  40. Moghaddam, D. D., Rezaei, M., Pourghasemi, H. R., Pourtaghie, Z. S., & Pradhan, B. (2015). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences,8(2), 913-929.
  41. Murthy, K. S. R., & Mamo, A. G. (2009). Multi‐criteria decision evaluation in groundwater zones identification in Moyale‐Teltele subbasin, South Ethiopia. International Journal of Remote Sensing30(11), 2729-2740.
  42. Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S., & Rezaei, A. (2015). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics8(1), 171-186.
  43. Ogato, G. S., Bantider, A., Abebe, K., & Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. Journal of Hydrology: Regional Studies27, 100659.
  44. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences37(9), 1264-1276.
  45. Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems11(5), 635-652.
  46. Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications39(3), 2822-2841.
  47. Pathak, D. (2017). Delineation of groundwater potential zone in the Indo-gangetic plain through GIS analysis. Journal of Institute of Science and Technology22(1), 104-109.
  48. Patra, S., Mishra, P., & Mahapatra, S. C. (2018). Delineation of groundwater potential zone for sustainable development: A case study from Ganga Alluvial Plain covering Hooghly district of India using remote sensing, geographic information system and analytic hierarchy process. Journal of Cleaner Production172, 2485-2502.
  49. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences51, 350-365.
  50. Radulović, M., Brdar, S., Mesaroš, M., Lukić, T., Savić, S., Basarin, B., & Pavić, D. (2022). Assessment of Groundwater Potential Zones Using GIS and Fuzzy AHP Techniques—A Case Study of the Titel Municipality (Northern Serbia).  International Journal of Geo-Information11(4), 257.
  51. Rahaman, S. A., Ajeez, S. A., Aruchamy, S., & Jegankumar, R. (2015). Prioritization of sub watershed based on morphometric characteristics using fuzzy analytical hierarchy process and geographical information system–A study of Kallar Watershed, Tamil Nadu. Aquatic Procedia4, 1322-1330.
  52. Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2015). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences8(9), 7059-7071.
  53. Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences7(2), 725-742.
  54. Saha, S. (2017). Groundwater potential mapping using analytical hierarchical process: a study on Md. Bazar Block of Birbhum District, West Bengal. Spatial Information Research, 25(4), 615-626.
  55. Sajil Kumar, P. J., Elango, L., & Schneider, M. (2022). GIS and AHP Based Groundwater Potential Zones Delineation in Chennai River Basin (CRB), India. Sustainability14(3), 1830.
  56. Senanayake, I. P., Dissanayake, D. M. D. O. K., Mayadunna, B. B., & Weerasekera, W. L. (2016). An approach to delineate groundwater recharge potential sites in Ambalantota, Sri Lanka using GIS techniques. Geoscience Frontiers7(1), 115-124.
  57. Silwal, C. B., & Pathak, D. (2018). Review on practices and state of the art methods on delineation of ground water potential using GIS and remote sensing. Bulletin of the Department of Geology, 20, 7-20.
  58. Singh, L. K., Jha, M. K., & Chowdary, V. M. (2017). Multi-criteria analysis and GIS modeling for identifying prospective water harvesting and artificial recharge sites for sustainable water supply. Journal of Cleaner Production142, 1436-1456.
  59. Suganthi, S., Elango, L., & Subramanian, S. K. (2013). Groundwater potential zonation by Remote Sensing and GIS techniques and its relation to the Groundwater level in the Coastal part of the Arani and Koratalai River Basin, Southern India. Earth Sciences Research Journal17(2), 87-95.
  60. Trabelsi, F., Lee, S., Khlifi, S., & Arfaoui, A. (2018). Frequency ratio model for mapping groundwater potential zones using GIS and remote sensing; Medjerda Watershed Tunisia. Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia.
  61. Wang, W. D., Xie, C. M., & Du, X. G. (2009). Landslides susceptibility mapping in Guizhou province based on fuzzy theory. Mining Science and Technology (China)19(3), 399-404.
  62. Yeh, H. F., Cheng, Y. S., Lin, H. I., & Lee, C. H. (2016). Mapping groundwater recharge potential zone using a GIS approach in Hualian River, Taiwan. Sustainable Environment Research26(1), 33-43.
CAPTCHA Image