Machine Learning Techniques for Gully Erosion Susceptibility Mapping (Case Study: Mukhtaran Watershed, South Khorasan Province, Iran)

Document Type : Original Research Article

Authors

1 Department of Natural Resources Engineering, Agriculture and Natural Resources Faculty, Hormozgan University, Bandarabbas, Iran

2 Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran

3 Department of Natural Resources Engineering, Natural Resources Faculty, Tehran University, Tehran, Iran

Abstract

Gully erosion, a significant environmental issue, can lead to severe consequences like soil loss, habitat destruction, and water pollution. To mitigate its impact, accurate mapping of land sensitivity to gully erosion is crucial. Machine learning models offer a powerful approach to predict and map gully erosion susceptibility. This study focuses on the Mukhtaran basin in South Khorasan province, Iran. By employing various machine learning techniques, including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt, the researchers aimed to identify the most suitable model for predicting gully erosion. Twenty-two environmental factors were selected and analyzed, with a focus on physiographic, climatic, hydrological, soil, land surface/cover, and geological variables. The results showed that the random forest (RF) and ensemble (ESMs) models demonstrated the highest accuracy in predicting gully erosion susceptibility, with a TSS index of 0.97. Sensitivity analysis revealed that the digital elevation model, soil electrical conductivity, bare soil percentage, land unit components, geology, runoff coefficient, and maximum storage capacity were the most influential factors. The study emphasizes the potential of machine learning models in generating accurate gully erosion susceptibility maps. However, further research is needed to explore additional factors and improve data quality. By combining topographic/hydrologic indices with machine learning models, more precise estimates of gully paths can be obtained for use in process-based models.

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References

Amiri, M., Pourghasemi, H.R., Ghanbarian, G.A. & Afzali, S.F., 2019. Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma, 340, 55–69.
Anderson, R.L., Rowntree, K.M. & Le Roux, J.J., 2021. An interrogation of research on the influence of rainfall on gully erosion. Catena, 206, 105482.
Angileri, S.E., Conoscenti, C., Hochschild, V., Marker, 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.
Arabameri, A., Pradhan, B., Rezaei, K. & Conoscenti, C., 2019. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. Catena, 180, 282–297.
Bastola, S., Dialynas, Y.G., Bras, R. L., Noto, L.V. & Istanbulluoglu, E., 2018. The role of vegetation on gully erosion stabilization at a severely degraded landscape: A case study from Calhoun Experimental Critical Zone Observatory. Geomorphology, 308, 25–39.
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.
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.
Galton, F., 1892. Finger prints (No. 57490-57492). Macmillan and Company.
Gao, Y., Zhu, B., Zhou, P., Tang, J.L., Wang, T. & Miao, C.Y., 2009. Effects of vegetation cover on phosphorus loss from a hillslope cropland of purple soil under simulated rainfall: A case study in China. Nutrient Cycling in Agroecosystems, 85(3), 263–273.
Garosi, Y., Sheklabadi, M., Pourghasemi, H.R., Besalatpour, A.A., Conoscenti, C.& Van Oost, K.,2018. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma, 330, 65–78.
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.
Gayen, A., Pourghasemi, H.R., Saha, S., Keesstra, S. & Bai, S., 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of the Total Environment, 668, 124–138.
Gomez Gutierrez, A., Schnabel, S. & FelicIsimo, A.M., 2009. Modelling the occurrence of gullies in rangelands of southwest Spain. Earth Surface Processes and Landforms: The Journal of the
British Geomorphological Research Group, 34, 1894–1902.
Gumus, M. & Kiran, M.S., 2017. Crude oil price forecasting using XGBoost, 2017. In: International Conference on Computer Science and Engineering (UBMK). IEEE, pp. 1100–1103.
Kantardzic, M.E.H.M.E.D., 2011. Data mining: concepts, models, methods and algorithms, A John Wiley & Sons. Inc. Hoboken, New Jersey.
Kuhnert, P.M., Henderson, A.K., Bartley, R. & Herr, A., 2010. Incorporating uncertainty in gully erosion calculations using the random forests modelling approach. Environmetrics, 21, 493–509.
Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R., Shahabi, H., Shirzadi, A. & Mosavi, A., 2020. GISbased machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sensing, 12(15), 2478.
Li, C. & Pan, C., 2018. The relative importance of different grass components in controlling runoff and erosion on a hillslope under simulated rainfall. Journal of Hydrology, 558, 90–103.
Liu, J., Gao, G., Wang, S., Jiao, L., Wu, X. & Fu B., 2018. The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 28, 59–78.
Luffman, I. E., Nandi, A. & Spiegel, T., 2015. Gully morphology, hillslope erosion, and precipitation characteristics in the Appalachian Valley and Ridge province, southeastern USA. Catena, 133, 221–232.
Luo, D., Caldas, M.M. & Goodin, D.G., 2021. Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data. Journal of Environmental Management, 289, 112502.
Mahala, A., 2020. Land degradation processes of Silabati river basin, West Bengal, India: a physical perspective. Gully erosion studies from India and surrounding regions, pp.265-278.
Majhi, A., Nyssen, J. & Verdoodt, A., 2021. What is the best technique to estimate topographic thresholds of gully erosion? Insights from a case study on the permanent gullies of Rarh plain, India. Geomorphology, 375 107547.
Marker, M., Pelacani, S. & Schroder, B.A., 2011. functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy. Geomorphology, 125, 530–540.
Mrad, D., Boukhari, S., Dairi, S. & Djebbar, Y., 2024. Mapping the potential for erosion gullies using frequency ratio and fuzzy analytical hierarchy process: case study Medjerda basin, northeast algeria. Eurasian Soil Science, 1-17.
Mohebzadeh, H., Biswas, A., Rudra, R. & Daggupati, P., 2022. Machine learning techniques for gully erosion susceptibility mapping: a review. Geosciences, 12(12), 429.
Momeni Damaneh, J., Ahmadi, J. & Jafarpour Chekab, Z., 2023b. 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, (In Persian).
Momeni Damaneh, J., Tajbakhsh, S.M., Ahmadi, J. & safdari, A.A., 2023a. 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, 4(3), 75-94, (In Persian).
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, (In Persian).
Pourghasemi, H.R., Sadhasivam, N.,Kariminejad, N. & Collins, A.L., 2020. Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geoscience Frontiers, 11, 2207–2219.
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.
Roy, J. & Saha, S.,2021. Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin, Eastern India. Advances in Space Research. 67(1), 316–333.
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.
Setargie, T.A., Tsunekawa, A., Haregeweyn, N., Tsubo, M., Fenta, A.A., Berihun, M.L., Sultan, D., Yibeltal, M., Ebabu, K., Nzioki, B. & Meshesha, T.M., 2023. Random Forest–based gully erosion susceptibility assessment across different agro-ecologies of the Upper Blue Nile basin, Ethiopia. Geomorphology, 108671. https://doi.org/10.1016/ j. geomorph.2023.108671.
Shruthi, R.B., Kerle, N., Jetten, V. & Stein, A., 2014. Object-based gully system prediction from medium resolution imagery using Random Forests. Geomorphology, 216, 283–294.
Smeeton, N.C., 1985. Early history of the kappa statistic. Biometrics, 41, 795.
Soleimanpour, S.M., Pourghasemi, H.R. & Zare, M., 2021. A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models. Catena, 207, 105679.
Swets, J. A.,1988. Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293.
Thuiller, W., Lafourcade, B., Engler, R. & Araujo, M.B., 2009. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography, ‌32(3),‌ 369-373.
Vanmaercke, M., Poesen, J., Van Mele, B., Demuzere, M., Bruynseels, A., Golosov, V., et al. 2016. How fast do gully headcuts retreat? Earth‐Science Reviews, 154, 336–355.
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.
 
Yi, Y.J.X., Cheng, Z.F., Yang, S. &  Zhang. H., 2016. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, (92), 260-269.
Zabihi, M., Mirchooli, F., Motevalli, A., Darvishan, A.K., Pourghasemi, H.R., Zakeri, M.A. & Sadighi, F., 2018. Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena, 161, 1–13.
Zhang, X., Wenhong, C., Qingchao, G. & Sihong, W., 2010. Effects of landuse change on surface runoff and sediment yield at different watershed scales on the Loess Plateau. International Journal of Sediment Research, 25(3), 283–293.
Zhang, X., Fan, J., Liu, Q.& Xiong, D.,2018. The contribution of gully erosion to total sediment production in a small watershed in Southwest China. Physical Geography, 39, 246–263.
Zhao, G., Mu, X., Wen, Z., Wang, F. & Gao, P., 2013. Soil erosion, conservation, and eco‐environment changes in the Loess Plateau of China. Land Degradation & Development, 24(5), 499–510. 

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