Evaluating the efficiency of different machine learning models in extracting the map of erosion forms of arid watersheds (Case study: Mukhtaran plain watershed, South Khorasan, Iran)

Memarian, Hadi; Momeni Damaneh, Javad

doi:10.61186/jeer.14.4.119

year 14, Issue 4 (Winter 2025) E.E.R. 2025, 14(4): 119-145 | Back to browse issues page

‎ 10.61186/jeer.14.4.119

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Memarian H, Momeni Damaneh J. Evaluating the efficiency of different machine learning models in extracting the map of erosion forms of arid watersheds (Case study: Mukhtaran plain watershed, South Khorasan, Iran). E.E.R. 2025; 14 (4) :119-145
URL: http://magazine.hormozgan.ac.ir/article-1-856-en.html

Evaluating the efficiency of different machine learning models in extracting the map of erosion forms of arid watersheds (Case study: Mukhtaran plain watershed, South Khorasan, Iran)

Hadi Memarian ^*

, Javad Momeni Damaneh

Department of Watershed Management (Research Group of Drought and Climate Change), Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran , hadi_memarian@birjand.ac.ir

Abstract: (1682 Views)

1- Introduction
Soil conservation is of paramount importance for sustainable development, food security, and environmental protection. Understanding soil erosion is considered as a critical practice for soil conservation programs worldwide. Currently, soil erosion is increasingly recognized as a serious concern for sustainable agriculture, water resources management, and modern civilization. Soil erosion poses a significant threat to soil, ecology, and humanity since the long-term productive capacity of soil is profoundly impacted by the degradation and washing away of topsoil and soil organic matter. In arid regions like Iran, soil erosion is a major crisis and can be considered as one of the critical challenges for agricultural development, natural resources, and the environment. The high sediment load entering rivers from upstream areas leads to increased water turbidity, reduces the lifespan of dams due to reservoir sedimentation, and negatively affects water quality and biological activity.
2- Materials and Methods
The Mukhtaran watershed is located in the South Khorasan province of Iran, covering an area of 2421 square kilometers. It lies on the southern slopes of the Bagheran Mountains and encompasses a diverse range of landforms, including mountainous and hilly terrain, vast plains, and playa devoid of vegetation. Annual precipitation in the Mukhtaran region varies between 150 millimeters in the lowlands and 220 millimeters in the highlands. The average annual temperature is 14.3 degrees Celsius; the average minimum temperature is 5.6 degrees Celsius, and the average maximum temperature is 22.7 degrees Celsius. In this work, at the first stage, various base maps including the drainage network, basin slope, geology, geomorphology, soil, and land use were extracted. Then, the map of work units was identified and classified. Subsequently, by interpreting the available aerial photographs, the extent of rill erosion forms was separated on the primary map. Through field visits, the locations of rill, gully, and streambank erosion were recorded using the GPS. The number of points for each erosion type and severity level were as follows: Rill erosion: Low: 55 points, Medium: 90 points, Severe: 58 points, very severe: 66 points, Gully erosion: Low: 62 points, Medium: 69 points, Severe: 63 points, Streambank erosion: Low: 95 points, Medium: 107 points, Severe: 12 points. Based on a review of previous studies and considering the nature of water erosion and the available baseline information resources in the region, 25 important and influential variables on water erosion were identified and their maps were prepared using various sources. Employing the available information, 25 environmental variables were considered for model generation, including 5 physiographic variables, 2 climatic variables, 4 hydrologic variables, 8 soil variables, 4 land cover variables, and 2 geological variables. The data was divided into two groups for training and validation with a ratio of 70 to 30. The model was repeated five times to evaluate its stability. The performance of the model was evaluated using the metrics ROC, KAPPA, and TSS.
3- Results
Based on the validation analysis results, the best model for slight to very severe rill erosion was the ensemble model (ESMs) with the accuracies of 97.0%, 85.0%, 90.0%, and 98.0%, respectively. For slight to severe gully erosion, the ensemble model (ESMs) also performed best simulation with the accuracies of 88.0%, 96.0%, and 96.0%. Finally, for slight and moderate streambank erosion, most models performed well, but the ensemble model (ESMs) had the highest accuracies with 94.0%, 98.0%, and 99.0%, respectively. In all forms of erosion, the ensemble model (ESMs) performed best simulation based on all three coefficients, at an excellent level.
4- Discussion & Conclusions
Mukhtaran watershed is severely faced with the problem of land degradation in various forms of erosion such as channel erosion, rill erosion and stream bank erosion, and this is the reason that not only the economy of this area is affected, also the natural environment and ecosystem related to it. In this study, different machine learning approaches with random sample partitioning were applied to estimate the most accurate vulnerable areas with the maximum possible accuracy. An examination of the relative importance of all environmental factors in each of the three major types of water erosion in the study area showed that physiographic factors, geological factors, land cover, and soil had a significant impact on the geographical distribution of water erosion forms in the Mukhtaran watershed. These results are consistent with the studies that have used support vector machine as an effective tool for mapping erosion susceptibility in watersheds. Overall, it can be concluded that machine learning models are effective and novel approaches for land use planning and erosion risk management.

Keywords: Soil erosion, Morphometric variables, Machine learning, Model evaluation, Ensemble model, Mukhtaran watershed

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Received: 2024/07/15 | Published: 2024/12/21

References

1. Afshar, F. A., Ayoubi, S., & Jalalian, A. (2010). Soil redistribution rate and its relationship with soil organic carbon and total nitrogen using 137Cs technique in a cultivated complex hillslope in western Iran. Journal of environmental radioactivity, 101(8), 606-614. [DOI:10.1016/j.jenvrad.2010.03.008]

2. Alkharabsheh, M. M., Alexandridis, T. K., Bilas, G., Misopolinos, N., & Silleos, N. (2013). Impact of land cover change on soil erosion hazard in northern Jordan using remote sensing and GIS. Procedia environmental sciences, (19), 912-921. [DOI:10.1016/j.proenv.2013.06.101]

3. Angileri, S. E., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., & Agnesi, V. (2016). Water erosion susceptibility mapping by applying stochastic gradient treeboost to the Imera Meridionale river basin (Sicily, Italy). Geomorphology, (262), 61-76. [DOI:10.1016/j.geomorph.2016.03.018]

4. Arabameri, A., Pradhan, B., Rezaei, K., Yamani, M., Pourghasemi, H. R., & Lombardo, L. (2018). Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm. Land Degradation & Development, 29(11), 4035-4049. [DOI:10.1002/ldr.3151]

5. Arabameri, A., Rezaei, K., Cerda, A., Lombardo, L., & Rodrigo-Comino, J. (2019). GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Science of the total environment, (658), 160-177. [DOI:10.1016/j.scitotenv.2018.12.115]

6. Bordbar, M., Aghamohammadi, H., Pourghasemi, H. R., & Azizi, Z. (2022). Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques. Scientific Reports, 12(1), 1451. [DOI:10.1038/s41598-022-05364-y]

7. Can, A., Dagdelenler, G., Ercanoglu, M. et al. (2019). Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bull Eng Geol Environ (78), 89-102. [DOI:10.1007/s10064-017-1034-3]

8. Chang, T. C. (2007). Risk degree of debris flow applying neural networks. Natural hazards, 42(1), 209-224. [DOI:10.1007/s11069-006-9069-y]

9. Chang, T. C., & Chao, R. J. (2006). Application of back-propagation networks in debris flow prediction. Engineering Geology, 85(3-4), 270-280. [DOI:10.1016/j.enggeo.2006.02.007]

10. Choubin, B., Rahmati, O., Tahmasebipour, N., Feizizadeh, B., & Pourghasemi, H. R. (2019). Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. Natural hazards gis-based spatial modeling using data mining techniques, 105-125. [DOI:10.1007/978-3-319-73383-8_5]

11. Conoscenti, C., Agnesi, V., Cama, M., Caraballo‐Arias, N. A., & Rotigliano, E. (2018). Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land degradation & development, 29(3), 724-736. [DOI:10.1002/ldr.2772]

12. Damaneh, J. M., Ahmadi, J., Rahmanian, S., Sadeghi, S. M. M., Nasiri, V., & Borz, S. A. (2022). Prediction of wild pistachio ecological niche using machine learning models. Ecological Informatics, 72, 101907. [DOI:10.1016/j.ecoinf.2022.101907]

13. Dou, J., Yamagishi, H., Pourghasemi, H. R., Yunus, A. P., Song, X., Xu, Y., & Zhu, Z. (2015). An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, (78), 1749-1776. [DOI:10.1007/s11069-015-1799-2]

14. Eustace, A. H., Pringle, M. J., & Denham, R. J. (2011). A risk map for gully locations in central Queensland, Australia. European journal of soil science, 62(3), 431-441. [DOI:10.1111/j.1365-2389.2011.01375.x]

15. Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental conservation, 24(1), 38-49. [DOI:10.1017/S0376892997000088]

16. Galton, F. (1892). Finger prints (No. 57490-57492). Macmillan and Company.

17. García‐Ruiz, J. M., Beguería, S., Lana‐Renault, N., Nadal‐Romero, E., & Cerdà, A. (2017). Ongoing and emerging questions in water erosion studies. Land Degradation & Development, 28(1), 5-21. [DOI:10.1002/ldr.2641]

18. Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H. R., & Van Oost, K. (2019). Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of the Total Environment, (664), 1117-1132. [DOI:10.1016/j.scitotenv.2019.02.093]

19. Gorsevski, P. V., Brown, M. K., Panter, K., Onasch, C. M., Simic, A., & Snyder, J. (2016). Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides, (13), 467-484. [DOI:10.1007/s10346-015-0587-0]

20. Goyes-Peñafiel, P., & Hernandez-Rojas, A. (2021). Doble evaluación de la susceptibilidad por movimientos en masa basada en redes neuronales artificiales y pesos de evidencia. Boletín de Geología, 43(1), 173-191. DOI: 10.18273/revbol. v43n1-2021009 [DOI:10.18273/revbol]

21. Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2016). Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, (259), 105-118. [DOI:10.1016/j.geomorph.2016.02.012]

22. Jalali, M., Gholami, H., Rezaie, M., & Omidvar, E. (2023). Integrated Modeling of Soil Erosion by Water and Wind Using Machine Learning Methods. Watershed Management Research Journal, 36(3), 128-145. doi: 10.22092/wmrj.2022.358127.1458. (in persian)

23. Kornejady, A., Ownegh, M., & Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, (152), 144-162. [DOI:10.1016/j.catena.2017.01.010]

24. Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, (44), 820-833. [DOI:10.1007/s00254-003-0825-y]

25. Mao, D., Zeng, Z., Wang, C., & Lin, W. (2007, August). Support vector machines with PSO algorithm for soil erosion evaluation and prediction. In Third International Conference on Natural Computation (ICNC 2007) (Vol. 1, pp. 656-660). IEEE. doi: 10.1109/ICNC.2007.697. [DOI:10.1109/ICNC.2007.697]

26. Moghadam, B. K., Jabarifar, M., Bagheri, M., & Shahbazi, E. (2015). Effects of land use change on soil splash erosion in the semi-arid region of Iran. Geoderma, (241), 210-220. [DOI:10.1016/j.geoderma.2014.11.025]

27. Mohammadifar, A., Gholami, H., Comino, J. R., & Collins, A. L. (2021). Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory. Catena,( 200), 105178. [DOI:10.1016/j.catena.2021.105178]

28. Momeni Damaneh, J., Ahmadi, J., & Jafarpour Chekab, Z. (2023). Identification of Suitable Areas for Cultivation of Saffron (Crocus sativus L.) Using Artificial Intelligence-Based Models in Khorasan Razavi Province. Journal of Saffron Research, 11(2), 328-345. doi: 10.22077/jsr.2024.7137.1231. (in persian)

29. Momeni damaneh, J., Tajbakhsh, S. M., Ahmadi, J., & safdari, A. A. (2023). Determining The Areas Prone to The Growth of Rhume ribes L. Specie in Razavi Khorasan Province Using Vector Machine Models. Water and Soil Management and Modelling, (), -. doi: 10.22098/mmws.2023.12726.1276. (in persian)

30. Momeni Damaneh, J., Tajbakhsh, S. M., Ahmadi, J., & Safdari, A. A. (2022). Comparison of species distribution models in determining the habitat landscape of Pistacia vera L. specie in Razavi Khorasan province. Water and Soil Management and Modelling, 3(4), 77-92. doi: 10.22098/mmws.2022.11698.1160. (in persian)

31. mohammadi, S., Karimzadeh, H., & Alizadeh, M. (2018). Spatial estimation of soil erosion in Iran using RUSLE model. Iranian journal of Ecohydrology, 5(2), 551-569. doi: 10.22059/ije.2018.239777.706. (in persian)

32. Morgan, R. P. C. (2009). Soil erosion and conservation. John Wiley & Sons. ISBN 9788578110796

33. Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. (2015). Estimating the soil erosion cover-management factor at the European scale. Land use policy, (48), 38-50. http://dx.doi.org/10.1016/j.landusepol.2015.05.021 [DOI:10.1016/j.landusepol.2015.05.021]

34. Pandey, V. K., Pourghasemi, H. R., & Sharma, M. C. (2020). Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto International, 35(2), 168-187. [DOI:10.1080/10106049.2018.1510038]

35. Park, N. W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, (73), 937-949. [DOI:10.1007/s12665-014-3442-z]

36. Pourghasemi, H. R., & Rossi, M. (2017). Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology, 130(1), 609-633. [DOI:10.1007/s00704-016-1919-2]

37. Pourghasemi, H. R., Yousefi, S., Kornejady, A., & Cerdà, A. (2017). Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, (609), 764-775. [DOI:10.1016/j.scitotenv.2017.07.198]

38. Pradeep, G. S., Krishnan, M. N., & Vijith, H. (2015). Identification of critical soil erosion prone areas and annual average soil loss in an upland agricultural watershed of Western Ghats, using analytical hierarchy process (AHP) and RUSLE techniques. Arabian Journal of Geosciences, 8(6), 3697-3711. [DOI:10.1007/s12517-014-1460-5]

39. 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 & Geosciences, 51, 350-365. [DOI:10.1016/j.cageo.2012.08.023]

40. Rahman, M. R., Shi, Z. H., & Chongfa, C. (2009). Land use/land cover change analysis using geo-information technology: two case studies in Bangladesh and China. International Journal of Geoinformatics, 5(2), 25.

41. Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R., & Feizizadeh, B. (2017). Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, (298), 118-137. [DOI:10.1016/j.geomorph.2017.09.006]

42. Sajedi‐Hosseini, F., Choubin, B., Solaimani, K., Cerdà, A., & Kavian, A. (2018). Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: Application of the fuzzy decision-making trial and evaluation laboratory approach. Land degradation & development, 29(9), 3092-3103. [DOI:10.1002/ldr.3058]

43. Scott, A. C. (2001). Water erosion in the Murray-Darling Basin: Learning from the past (p. 134). Canberra, Australia: CSIRO Land and Water.

44. Senanayake, S., & Pradhan, B. (2022). Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka. Journal of Environmental Management, (308), 114589. [DOI:10.1016/j.jenvman.2022.114589]

45. Shahri, A. A., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, (183), 104225. [DOI:10.1016/j.catena.2019.104225]

46. Sharma, A., Tiwari, K. N., & Bhadoria, P. B. S. (2011). Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environmental monitoring and assessment, (173), 789-801. [DOI:10.1007/s10661-010-1423-6]

47. Smeeton, N. C. (1985). Early history of the kappa statistic. Biometrics, (41), 795.

48. Svoray, T., Michailov, E., Cohen, A., Rokah, L., & Sturm, A. (2012). Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold. Earth Surface Processes and Landforms, 37(6), 607-619. [DOI:10.1002/esp.2273]

49. Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. [DOI:10.1126/science.3287615]

50. Taner San, B. (2014). An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey). International journal of applied earth observation and geoinformation, (26), 399-412. http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28046489 [DOI:10.1016/j.jag.2013.09.010]

51. Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD-a platform for ensemble forecasting of species distributions. Ecography, 32(3), 369-373. [DOI:10.1111/j.1600-0587.2008.05742.x]

52. Vaezi, A. R., Abbasi, M., Bussi, G., & Keesstra, S. (2017). Modeling sediment yield in semi‐arid pasture micro‐catchments, NW Iran. Land Degradation & Development, 28(4), 1274-1286. [DOI:10.1002/ldr.2526]

53. Vanmaercke, M., Poesen, J., Verstraeten, G., de Vente, J., & Ocakoglu, F. (2011). Sediment yield in Europe: Spatial patterns and scale dependency. Geomorphology, 130(3-4), 142-161. [DOI:10.1016/j.geomorph.2011.03.010]

54. Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J., ... & Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416(6879), 389-395. [DOI:10.1038/416389a]

55. Yi, Y. J., Cheng, X., Yang, Z. F., & Zhang, S. H. (2016). Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, (92), 260-269. [DOI:10.1016/j.ecoleng.2016.04.010]

56. Yuan, L., Zhang, Q., Li, W., & Zou, L. (2006, July). Debris flow hazard assessment based on support vector machine. In 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 4221-4224). IEEE. doi: 10.1109/IGARSS.2006.1083. [DOI:10.1109/IGARSS.2006.1083]