Volume 10, Issue 3 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(3): 163-182 |
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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-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3359-en.html
1- Department of Natural Engineering (Pasture and Watershed Management), Faculty of Agriculture, Ilam University, Ilam, Iran.
2- Department of Natural Engineering (Pasture and Watershed Management), Faculty of Agriculture, Ilam University, Ilam, Iran. ,faramarzi.marzban@gmail.com
2- Department of Natural Engineering (Pasture and Watershed Management), Faculty of Agriculture, Ilam University, Ilam, Iran. ,
Abstract: (2217 Views)
In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.
Type of Study: Research |
Subject:
Special
Received: 2023/01/17 | Accepted: 2023/11/14 | Published: 2023/09/23
Received: 2023/01/17 | Accepted: 2023/11/14 | Published: 2023/09/23
References
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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