Evaluating the potential of groundwater resources using a combination of data mining methods:(Case study: Hormozgan province, Sarkhon plain)

Subject Areas : Farm water management with the aim of improving irrigation management indicators

fateme riahi ¹ , hasan vagharfard ² , peyman daneshkar araste ³ , hamid kardan moghadam ⁴

1 - PhD student in Watershed Management -Water; Faculty of Agriculture and Natural Resources; Hormozgan University
2 - Associate Professor of Range and Watershed Management Group; Faculty of Agriculture and Natural Resources; Hormozgan University
3 - Associate Professor, Department of Water Science and Engineering; Imam Khomeini International University of Qazvin
4 - Assistant Professor, Water Research Institute of the Ministry of Energy, Tehran, Iran.

Received: 2020-01-12 Accepted : 2020-05-19 Published : 2020-05-21

Keywords: Groundwater simulation, Random forest method, GAM method, GLM method,

Abstract :

The porpose of this study is the ground water potential mapping.using four methods, stochastic forest, GLM, Domain, and GAM. in addition, four methods for combining these methods for potential mapping were also evaluated. for this purpose, eleven criteria include the slope , profile curvature ,(Topographic Curvature), total curvature, index (spi), index (spi), index (twi), land use, soil and demographic elevation model were used according to the experience of experts and researchers. in order to validate the 76 of wells with high discharge were, it has been used for simulation (70 %) and validation (30%) before modeling the linear test on the criteria, there is no linear relationship between variables. according to, the results of the evaluation using the ROC curve showed that all four used methods have excellent accuracy and AUC over 90%. then, the results of four methods were combined with mean averaging method. The final potential showed that 32.89% of the lands have good potential for exploiting groundwater resources.The results of the importance factors also showed that the slope, height, and power index were the most important factors. The results of this research can serve as information bases for planners and local authorities to evaluate, plan, manage, sustainably use and synthesize groundwater resources in the future.

References:

ابراهیم­خانی، سو، افضلی، م؛ و شکوهی، ع. 1390. پیش­بینی و بررسی عوامل تصادفات جاده­ای با استفاده از الگوریتم­های داده­کاوی. فصل­نامه دانش اجتماعی زنجان، 1(1): 127-111.

رحیمی، د. 1390. پتانسیل‌یابی منابع آب زیرزمینی (مطالعه موردی دشت شهرکرد). جغرافیا و برنامه‌ریزیمحیطی، 4:127.

رنجبرمنش، ن. انتظاری، م؛ و رامشت، م.ح. 1392. ِ بحران ناشی از افت سطح آب زیرزمینی در اثر فعالیت تکتونیکی دردشت ماهی دشت. دو فصلنامه‌ی ژئومورفولوژی کاربردی ایران، 1(2):1-18.

ناطقی نیا، س.، مصطفی‌زادهفرد، ب؛ و موسوی، ف. 1387. تأثیر شیب و پوشش گیاهی بر ذخیره سطحی و رواناب در خاک‌های درز و شکاف دار تحت آبیاری بارانی. آبیاری و زهکشی ایران: 2 19.

سوری، س.، بهاروند، س. و امیری، وهاب. 1396. پتانسیل‌یابی منابع آب زیرزمینی با استفاده از روش سلسله مراتبی فازی (مطالعه موردی: دشت رومشگان). فصلنامه زمین‌شناسیمحیط‌زیست، 11(40):11-26

طرح احیا و تعادل بخشی دشت سرخون، سازمان آب منطقه­ای هرمزگان: 1394

سیف، ع.، کارگر، ا. 1390. پتانسیل‌یابی منابع آب زیرزمینی با استفاده از روش تحلیل سلسله مراتبی و سیستم جغرافیایی، مطالعه موردی: حوضه آبریز سیرجان. جغرافیای طبیعی: 75

حقی زاده، ع.، میرزا پور، ح. 1395. پتانسیل‌یابیآب‌های زیرزمینی حوزه آبخیز مادیان رود لرستان با استفاده تجزیه‌وتحلیل شاخص همپوشانی وزنی (WIOA). هیدروژئولوژی:. 83-98

عرب عامری، ع.، رضایی، خ.، یمانی، م. و شیرانی، ک. 1397. مدل‌سازی و تهیه نقشه پتانسیل آب‌های زیرزمینی با استفاده از روش ترکیبی داده محور تابع شواهد قطعی- شاخص آنتروپی (مطالعه موردی: حوزه نجف‌آباد). پژوهش‌های حفاظت آب‌وخاک: 48-25

یمانی، م.، احمدآبادی، ع. و زارع، غ. 1391. به­کارگیری الگوریتم ماشین­های بردار در پهنه­بندی خطر وقوع زمین‌لغزش (مطالعه موردی: حوضه آبریز درکه). جغرافیا و مخاطرات محیطی،3: 142-125.

خاشعی سیوکی ع، قهرمان، بیژن و کوچک زاده، م. 1391. ارزیابی پتانسیل استحصال آب ازآبخوان با استفاده از روش فرآیند تحلیل سلسله مراتبی فازی (مثال. موردی: دشتنیشابور) مجله پژوهش آب ایران. 5(9): 170-181

Al-Abadi, A. M., Al-Temmeme, A. A., & Al-Ghanimy, M. A. (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. Sustainable Water Resources Management, 2(3), 265-283.‏

Alley, W. M., Reilly, T. E., & Franke, O. L. (1999). Sustainability of ground-water resources (Vol. 1186). US Department of the Interior, US Geological Survey.‏

Carpenter, G., Gillison, A. N., & Winter, J. (1993). DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity & Conservation, 2(6), 667-680.‏

Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2018). Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bulletin of Engineering Geology and the Environment, 77(2), 611-629.‏

Constantin, M., Bednarik, M., Jurchescu, M. C., & Vlaicu, M. (2011). Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental earth sciences, 63(2), 397-406.‏

Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., ... & Althuwaynee, O. F. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural hazards, 65(1), 135-165.‏

Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., ... & Li, J. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.‏

Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental geology, 41(6), 720-730.‏

Agarwal, E., Agarwal, R., Garg, R. D., & Garg, P. K. (2013). Delineation of groundwater potential zone: An AHP/ANP approach. Journal of earth system science, 122(3), 887-898.‏

Gayen, A., & Pourghasemi, H. R. (2019). Spatial Modeling of Gully Erosion: A New Ensemble of CART and GLM Data-Mining Algorithms. In Spatial Modeling in GIS and R for Earth and Environmental Sciences (pp. 653-669). Elsevier.‏

Glenn, C. R., Whittier, R. B., Dailer, M. L., Dulaiova, H., El-Kadi, A. I., Fackrell, J., ... & Waters, C. A. (2012). Lahaina groundwater tracer study–Lahaina, Maui, Hawai ‘i. Final Interim Report. Prepared for the State of Hawai ‘i Department of Health, the US Environmental Protection Agency, and the US Army Engineer Research and Development Center.‏

Guikema, S. D., & Quiring, S. M. (2012). Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data. Reliability Engineering & System Safety, 99, 178-182.‏

Hijmans, R. J., & Graham, C. H. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global change biology, 12(12), 2272-2281.‏

Jha, M. K., Kamii, Y., & Chikamori, K. (2009). Cost-effective approaches for sustainable groundwater management in alluvial aquifer systems. Water resources management, 23(2), 219.‏

Jothibasu, A., & Anbazhagan, S. (2016). Modeling groundwater probability index in Ponnaiyar River basin of South India using analytic hierarchy process. Modeling Earth Systems and Environment, 2(3), 109.‏

Le Page, M., Berjamy, B., Fakir, Y., Bourgin, F., Jarlan, L., Abourida, A., ... & Simonneaux, V. (2012). An integrated DSS for groundwater management based on remote sensing. The case of a semi-arid aquifer in Morocco. Water resources management, 26(11), 3209-3230.‏

Lee, S., Song, K. Y., Kim, Y., & Park, I. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeology Journal, 20(8), 1511-1527.‏

Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30.‏

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 Informatics, 8(1), 171-186.‏

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 Informatics, 8(1), 171-186.‏

Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283-300.‏

Oney, O. (2019). The increase of the performance of ultrafine coal flotation by using emulsified kerosene and the prediction of the flotation parameters by random forest and genetic algorithm. Archives of Mining Sciences, 64(1), 119-130.‏

Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180-197.‏

Patriche, C. V., Căpăţână, V., & Stoica, D. L. (2006). Aspects regarding soil erosion spatial modeling using the USLE/RUSLE within GIS. Geographia Technica, 2, 87-97.‏

Pourghasemi, H. R., & Beheshtirad, M. (2015). Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto International, 30(6), 662-685.‏

Pourtaghi, Z. S., & Pourghasemi, H. R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662.‏

Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (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.‏

Razandi, Y., Pourghasemi, H. R., Neisani, N. S., & Rahmati, O. (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.‏

Sharma, L. P., Patel, N., Ghose, M. K., & Debnath, P. (2012). Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India. Arabian Journal of Geosciences, 5(3), 421-431.‏

Shekhar, S., & Pandey, A. C. (2015). Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto International, 30(4), 402-421.‏

Singh, P., Gupta, A., & Singh, M. (2014). Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, 17(2), 111-121.‏

Thapa, R., Gupta, S., Guin, S., & Kaur, H. (2017). Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal. Applied Water Science, 7(7), 4117-4131.‏

Yesilnacar, E. K. (2005). The application of computational intelligence to landslide susceptibility mapping in Turkey. University of Melbourne, Department, 200.

Youssef, A. M., Pradhan, B., Jebur, M. N., & El-Harbi, H. M. (2015). Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environmental Earth Sciences, 73(7), 3745-3761.‏

Moghaddam, H. K., Moghaddam, H. K., Kivi, Z. R., Bahreinimotlagh, M., & Alizadeh, M. J. (2019). Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater for Sustainable Development, 9, 100237.‏