دوره 10، شماره 3 - ( 7-1402 )
جلد 10 شماره 3 صفحات 182-163 |
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Salimi N, Faramarzi M, Tavakoli M, Fathizad H. Using Machine Learning Algorithms for Modeling Groundwater Resources in Arid Rangeland Western Iran. Journal of Spatial Analysis Environmental Hazards 2023; 10 (3) :163-182
URL: http://jsaeh.khu.ac.ir/article-1-3359-fa.html
URL: http://jsaeh.khu.ac.ir/article-1-3359-fa.html
سلیمی نازنین، فرامرزی مرزبان، توکلی محسن، فتحی زاد حسن. استفاده از الگوریتم‌های یادگیری ماشین برای مدل‌سازی منابع آب زیرزمینی در دشت موسیان استان ایلام. تحلیل فضایی مخاطرات محیطی. 1402; 10 (3) :163-182
نازنین سلیمی1 
، مرزبان فرامرزی 2، محسن توکلی1 ، حسن فتحی زاد1
، مرزبان فرامرزی 2، محسن توکلی1 ، حسن فتحی زاد1
1- دانشگاه ایلام
2- دانشگاه ایلام ،faramarzi.marzban@gmail.com
2- دانشگاه ایلام ،
چکیده: (2594 مشاهده)
امروزه میزان برداشت از آبهای زیرزمینی بیش از میزان تغذیه آبهای زیرزمینی است که این عامل باعث افت شدید سطح سفره‌های آب زیرزمینی شده است. مراتع و جنگل‌ها بعنوان اصلی‌ترین مکان‌های تغذیه سفره‌های آب زیرزمینی محسوب می‌شوند، درحالی‌که بیشترین برداشت از این منابع در کاربری کشاورزی انجام می‌شود. هدف اصلی از پژوهش حاضر استفاده از الگوریتم‌های یادگیری ماشین شامل رگرسیون جنگل تصادفی و تابع آنتروپی شانون برای مدل‌سازی منابع آب زیرزمینی در مراتع نیمه‌خشک غرب ایران می‌باشد. برای این هدف، ابتدا لایه‌های اطلاعاتی شامل: درجه شیب، جهت شیب، ارتفاع از سطح دریا، فاصله از گسل، شکل شیب، فاصله از آبراهه، فاصله از جاده، بارندگی، لیتولوژی و کاربری اراضی تهیه شد. پس از تعیین وزن پارامترها با استفاده از تابع آنتروپی شانون و سپس تعیین طبقات آن‌ها، در محیط سامانه‌های اطلاعات جغرافیایی، از ترکیب وزن پارامترها و طبقات آن‌ها نقشه نهایی مناطق دارای پتانسیل منابع آب زیرزمینی مدل‌سازی گردید. بعلاوه، برای اجرای مدل جنگل تصادفی از نرم افزار R 3.5.1 و بسته randomForest استفاده شد. در تحقیق حاضر از اعتبارسنجی ضربدری k-fold برای صحت‌سنجی مدل‌ها استفاده گردید. به منظور ارزیابی کارایی مدل‌های جنگل تصادفی و آنتروپی شانون برای پتانسیل‌یابی منابع آب زیرزمینی، از شاخصهای آماری MAE، RMSE و R2 استفاده شد. نتایج ارزیابی نشان داد که مدل جنگل تصادفی با دقت (RMSE: 3.41, MAE: 2.85 R² = 0.825,) دارای دقت بالاتری نسبت به مدل آنتروپی شانون با دقت (R² = 0.727, RMSE: 4.36, MAE: 3.34) می‌باشد. یافته‌های مدل جنگل تصادفی نشان داد که قسمت زیادی از منطقه مورد مطالعه دارای پتانسیل متوسط (22/26954 هکتار) و مساحت خیلی اندکی (61/205 هکتار) بدون پتانسیل آب زیرزمینی می‌باشد. از طرفی، نتایج مدل آنتروپی شانون نشان داد که قسمت اعظمی از منطقه مورد مطالعه دارای پتانسیل متوسط (05/24633 هکتار) و مساحت خیلی اندکی (12/1502 هکتار) بدون پتانسیل آب زیرزمینی می‌باشد.
فهرست منابع
1. Abd Manap, M., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W.N.A. and Ramli, M.F., 2014. Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7(2), pp.711-724.
2. Al-Abadi, A.M., 2017. Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models. Applied Water Science, 7(2), pp.699-716.
3. Al-Abadi, A.M., Al-Temmeme, A.A. and 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), pp.265-283.
4. Allouche, O., Tsoar, A. and Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of applied ecology, 43(6), pp.1223-1232.
5. Breiman, L. and Cutler, A., 2004. Random Forests, URL: http://www. stat. berkeley. edu/users/breiman. RandomForests/cc_papers. htm.
6. Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.
7. Cutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J., 2007. Random forests for classification in ecology. Ecology, 88(11), pp.2783-2792.
8. De Martonne, E. M. (1926). L'indice d'aridité. Bulletin de l'Association de géographes français, 3(9), 3-5.
9. Friedman, J., Hastie, T. and Tibshirani, R., 2010. Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), p.1.
10. Ganapuram, S., Kumar, G.V., Krishna, I.M., Kahya, E. and Demirel, M.C., 2009. Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS. Advances in Engineering Software, 40(7), pp.506-518.
11. Halder, S., Roy, M. B., & Roy, P. K. (2020). Analysis of groundwater level trend and groundwater drought using Standard Groundwater Level Index: A case study of an eastern river basin of West Bengal, India. SN Applied Sciences, 2(3), 1-24.
12. Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748.
13. Hou, E., Wang, J. and Chen, W., 2018. A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models. Geocarto international, 33(7), pp.754-769.
14. Jothibasu, A. and 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.
15. Khoshtinat, S., Aminnejad, B., Hassanzadeh, Y. and Ahmadi, H., 2019. Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater. Journal of Hydroinformatics, 21(5), pp.745-760.
16. Malekian, A. and Azarnivand, A., 2016. Application of integrated Shannon’s entropy and VIKOR techniques in prioritization of flood risk in the Shemshak watershed, Iran. Water Resources Management, 30(1), pp.409-425.
17. Massey, D.S. and Denton, N.A., 1988. The dimensions of residential segregation. Social forces, 67(2), pp.281-315.
18. Moghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S. and Pradhan, B., 2015. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences, 8(2), pp.913-929.
19. Molden, D. (2013). Water for food water for life: A comprehensive assessment of water management in agriculture: Routledge.
20. Naghibi, S. A., Ahmadi, K. and Daneshi, A. 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9), 2761-2775.
21. Naghibi, S.A., Pourghasemi, H.R. and Dixon, B., 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188(1), p.44.
22. Naghibi, S.A., Pourghasemi, H.R., Pourtaghi, Z.S. and 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), pp.171-186.
23. Nampak, H., Pradhan, B. and Abd Manap, M., 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, pp.283-300.
24. Nhu, V.H., Rahmati, O., Falah, F., Shojaei, S., Al-Ansari, N., Shahabi, H., Shirzadi, A., Górski, K., Nguyen, H. and Ahmad, B.B., 2020. Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models. Water, 12(4), p.985.
25. Oh, H.J., Kim, Y.S., Choi, J.K., Park, E. and Lee, S., 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4), pp.158-172.
26. Ozdemir, A., 2011. GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411(3-4), pp.290-308.
27. Pourghasemi, H.R., Mohammady, M. and Pradhan, B., 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97, pp.71-84.
28. Pourtaghi, Z.S. and Pourghasemi, H.R., 2014. GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), pp.643-662.
29. Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosciences, 1(1), 120-129.
30. Rahmati, O., Pourghasemi, H.R. and 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, pp.360-372.
31. Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M. and Rigol-Sanchez, J.P., 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, pp.93-104.
32. Shannon, C.E., 2001. A mathematical theory of communication. ACM SIGMOBILE mobile computing and communications review, 5(1), pp.3-55.
33. Tien Bui, D., Le, K.T.T., Nguyen, V.C., Le, H.D. and Revhaug, I., 2016. Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sensing, 8(4), p.347.
34. Tweed, S.O., Leblanc, M., Webb, J.A. and Lubczynski, M.W., 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchmentAbd Manap, M., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W.N.A. and Ramli, M.F. 2014. Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7(2): 711-724.
35. Al-Abadi, A.M. 2017. Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models. Applied Water Science, 7(2): 699-716.
36. Al-Abadi, A.M., Al-Temmeme, A.A. and 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.
37. Allouche, O., Tsoar, A. and Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of applied ecology, 43(6): 1223-1232.
38. Breiman, L. and Cutler, A. 2004. Random Forests, URL: http://www. stat. berkeley. edu/users/breiman. RandomForests/cc_papers. htm.
39. Breiman, L. 2001. Random forests. Machine learning, 45(1): 5-32.
40. Cutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792.
41. De Martonne, E. M. 1926. L'indice d'aridité. Bulletin de l'Association de géographes français, 3(9): 3-5.
42. Friedman, J., Hastie, T. and Tibshirani, R., 2010. Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1): 1.
43. Ganapuram, S., Kumar, G.V., Krishna, I.M., Kahya, E. and Demirel, M.C. 2009. Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS. Advances in Engineering Software, 40(7): 506-518.
44. Halder, S., Roy, M. B., & Roy, P. K. 2020. Analysis of groundwater level trend and groundwater drought using Standard Groundwater Level Index: A case study of an eastern river basin of West Bengal, India. SN Applied Sciences, 2(3): 1-24.
45. Halder, S., Roy, M. B., Roy, P. K., & Sedighi, M. (2023). Groundwater vulnerability assessment for drinking water suitability using Fuzzy Shannon Entropy model in a semi-arid river basin. Catena, 229, 107206.
46. Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A. 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2): e0169748.
47. Hou, E., Wang, J. and Chen, W. 2018. A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models. Geocarto international, 33(7): 754-769.
48. Jaafari, A., Najafi, A., Pourghasemi, H. R., Rezaeian, J., & Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11: 909-926.
49. Jothibasu, A. and 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.
50. Khoshtinat, S., Aminnejad, B., Hassanzadeh, Y. and Ahmadi, H. 2019. Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater. Journal of Hydroinformatics, 21(5): 745-760.
51. Madani, A., & Niyazi, B. (2023). Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia. Sustainability, 15(3), 2772.
52. Malekian, A. and Azarnivand, A. 2016. Application of integrated Shannon’s entropy and VIKOR techniques in prioritization of flood risk in the Shemshak watershed, Iran. Water Resources Management, 30(1): 409-425.
53. Massey, D.S. and Denton, N.A., 1988. The dimensions of residential segregation. Social forces, 67(2):281-315.
54. Moghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S. and 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.
55. Molden, D. (2013). Water for food water for life: A comprehensive assessment of water management in agriculture: Routledge.
56. Naghibi, S. A., Ahmadi, K. and Daneshi, A. 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9): 2761-2775.
57. Naghibi, S.A., Pourghasemi, H.R. and Dixon, B. 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188(1): 44.
58. Naghibi, S.A., Pourghasemi, H.R., Pourtaghi, Z.S. and 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.
59. Nampak, H., Pradhan, B. and Abd Manap, M. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513: 283-300.
60. Nhu, V.H., Rahmati, O., Falah, F., Shojaei, S., Al-Ansari, N., Shahabi, H., Shirzadi, A., Górski, K., Nguyen, H. and Ahmad, B.B. 2020. Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models. Water, 12(4): 985.
61. Oh, H.J., Kim, Y.S., Choi, J.K., Park, E. and Lee, S. 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4): 158-172.
62. Ozdemir, A. 2011. GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411(3-4): 290-308.
63. Pande, C. B., Moharir, K. N., Panneerselvam, B., Singh, S. K., Elbeltagi, A., Pham, Q. B., ... & Rajesh, J. 2021. Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques. Applied Water Science, 11(12), 186.
64. Pourghasemi, H.R., Mohammady, M. and Pradhan, B. 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97: 71-84.
65. Pourtaghi, Z.S. and 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.
66. Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosciences, 1(1): 120-129.
67. Rahmati, O., Pourghasemi, H.R. and 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.
68. Razavi-Termeh, S. V., Sadeghi-Niaraki, A., & Choi, S. M. (2019). Groundwater potential mapping using an integrated ensemble of three bivariate statistical models with random forest and logistic model tree models. Water, 11(8): 1596.
69. Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M. and Rigol-Sanchez, J.P. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 93-104.
70. Shannon, C.E., 2001. A mathematical theory of communication. ACM SIGMOBILE mobile computing and communications review, 5(1):3-55.
71. Thanh, N. N., Chotpantarat, S., Ha, N. T., & Trung, N. H. (2023). Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy. Environmental Geochemistry and Health, 1-20.
72. Tien Bui, D., Le, K.T.T., Nguyen, V.C., Le, H.D. and Revhaug, I. 2016. Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sensing, 8(4): 347.
73. Tweed, S.O., Leblanc, M., Webb, J.A. and Lubczynski, M.W. 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia. Hydrogeology Journal, 15(1): 75-96.
74. Yaghobi, S., Faramarzi, M., Karimi, H., & Sarvarian, J. 2019. Simulation of land-use changes in relation to changes of groundwater level in arid rangeland in western Iran. International Journal of Environmental Science and Technology, 16(3): 1637-1648.
75. s, southeastern Australia. Hydrogeology Journal, 15(1), pp.75-96.
76. Yaghobi, S., Faramarzi, M., Karimi, H., & Sarvarian, J. (2019). Simulation of land-use changes in relation to changes of groundwater level in arid rangeland in western Iran. International Journal of Environmental Science and Technology, 16(3), 1637-1648.
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