Prediction of Gully Erosion Susceptibility using ME, Dempster-Shafer and WOE Models in the Southern Slope of Alborz: Determining the Best Model and the Effect of the Most Important Indicators
Subject Areas : Optimal management of water and soil resources
Ebrahim Yousefi Mobarhan
1
*
,
Kourosh Shirani
2
,
Kazem Saber
3
1 - Assistant Professor, Soil Conservation and Watershed Management Research Institute, Semnan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Semnan, Iran.
2 - Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
3 - Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
Keywords: Jackknife test, multiple collinearity test, maximum entropy, gully erosion, machine learning model,
Abstract :
Introduction: Identifying areas sensitive to gully erosion using statistical models and making maximum use of available information with less cost and time and access to greater accuracy is of particular importance. The aim of this research is to prepare a map of sensitivity to gully erosion using maximum entropy, Dempster-Schaffer and witness weight models in the Alaa watershed in the southern slope of Alborz, Semnan province. Identifying the most important environmental factors affecting the occurrence of gully erosion using the jackknife method and examining the importance of each environmental factor in the study area by analyzing response curves are other objectives of this research.
method: In this study, after selecting the study area, the necessary information was collected and a map of the factors affecting gully erosion was prepared. Next, a gully occurrence distribution map was prepared and the data were randomly divided into two groups: training or calibration (70%) and experimental or prediction (30%). Also, a multiple collinearity test was performed with the variance inflation factor and tolerance coefficient indices to examine the overlap and importance of each of the effective factors. After implementing the maximum disorder, Dempster-Schaffer, and witness weight models, a gully erosion sensitivity map was prepared and classified into five classes from very low to very high. Finally, an evaluation of gully erosion sensitivity prediction models for the study area was performed and the superior method was selected for the study area.
Results and Discussion: Based on the results obtained from the collinearity test, 20 parameters from 23 effective parameters in the occurrence of gully erosion, including climate, land use, stream density, soil type, elevation, distance from the stream, average annual precipitation, slope, profile curvature index, slope curvature, vertical distance index from the stream, convergence index, vegetation index, topographic moisture index, slope direction, watershed area, light shade index, lithology, ground surface texture, and curvature classification index, were identified for appropriate modeling. The gully erosion sensitivity map of the study area showed that the outcrop of the Quaternary clay and marl rock unit in the southern regions of the basin has the greatest effect on causing gully erosion. The structure performance characteristic curve and the area under the curve were used to validate the models. The maximum irregularity, Dempster-Schaffer, and witness weight models have accuracies of 87.1, 81, and 83.7 percent in the modeling stage, respectively, and 87.5, 80, and 84.6 percent in the validation stage, and are very effective in predicting areas susceptible to gully erosion.
Conclusion: The maximum entropy model with a value of 0.91 in the model development mode and a value of 0.89 in the validation mode can be efficient in zoning and predicting the occurrence of gully erosion. Therefore, the maximum entropy model was used with good speed and accuracy in evaluating the effective factors and validating the zoning of sensitivity to gully erosion. Zoning the sensitivity map to gully erosion in the study area showed that most of the areas with high and very high sensitivity were concentrated in the central and southeastern regions of the study area, while the areas with low sensitivity to gully erosion are spread in the steep and high northern regions. The results of this research can be promoted and trained, and the implementation agencies can take the necessary measures to control gully erosion using the results of this research.
Asgari S, Shirani K. Evaluation of the effective factors in gully erosion sensitivity using Dempster-Shafer. 2024. Journal of Spatial Analysis Environmental Hazards: 11 (2): 137-159 (In persion).
Asgari S, Shirani K. and Soleimani, F. 2025. Evaluation of gully erosion using weight of evidence (WofE) and Dempster-Schiffer (DSH) models in Ilam watershed. Watershed Management Research, 37(4), 71-98 (In persion).
Azareh, A., Rahmati, O., Rafiei-Sardooi, E. Sankey, J. B., Lee, S., Shahabi, H., bin, B. Ahmad, B. 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment 655: 684-696
Baboli moakher, H., Shirani, K., and Taghian. A.R. 2018. Performance of chaos theory on natural systems in landslide hazard zonation in Fahlian River Basin. Journal of Geoscience, 28 (109): 187-200 (In Persian).
Ballabio, C., & Sterlacchini, S. 2012. Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical geosciences, 44(1): 47-70.
Bernini, A., Bosino, A., Botha, G., & Mearker, M., 2021. Elevation of gully erosion suscepceptibility using a maximum entropy model in the upper Mkhomazi River Basin in South Africa. Geo Information,10 (11),1-20.
Boos, D.D. and C. Brownie. 2004. Comparing variances and other measures of dispersion. Statistical Science, 19(4): 571-578.
Castillo, C., E.V. Taguas, P. Zarco-Tejada, M.R. James and J.A. Gómez. 2014. The normalized topographic method: an automated procedure for gully mapping using GIS. Earth Surface Processes and Landforms, 39(15): 2002–2015.
Chaplot, V., Giboire, G., Marchand, P., Valentin, C. 2005. Dynamic modelling for linear erosion initiation and development under climate and land-use changes in northern Laos. Catena, 63(2-3): 318-328.
Conoscenti, C., Agnesi, V., Angileri, S., Cappadonia, C., Rotigliano, E., Märker, M. 2013. A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ Earth Sci, 70: 1179- 1195.
Conforti, M., Aucelli, P. P., Robustelli, G., & Scarciglia, F., 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural hazards, 56(3): 881-898.
Davoudi Moghaddam, D., & Haghizadeh, A. 2020. Detection of Susceptible Areas to Flooding and its Most Important Contributing Factors Using the Maximum Entropy Model in the Tashan Watershed, Khuzestan. Watershed Management Research Journal, 33(4): 94-109.
Dempster, A.P. 1967. Upper and lower probabilities induced by a multi valued mapping. Ann Math Stat, 38 (2): 325–339.
Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, J. R. G. Marquéz, B. Gruber, B. Lafourcade, P.J. Leitão, T. Münkemüller, C. Mc Clean, P. E. Osborne, B. Reineking, B. Schröder, A. K. Skidmore, D. Zurell and S. Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36: 27-46.
Dube, F., Nhapi, I., Murwira, A., Gumindoga, W., Goldin, J., Mashauri, D.A. 2014. Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District-Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 67: 145–152.
Elith, J., S. Phillips, T. Hastie, M. Dudík, Y. Chee, and C. Yates. 2010. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1): 43–57.
Entezari, M., M. Amjad, Kh. Moradi and S. Olfati. 2014. Zoning of gully erosion in catchment of Dyreh by Analytical Hierarchy Process (AHP). The Journal of Spatial Planning, 17: 63-86 (in Persian).
Erfani, M., Jahanishakib, F., & Enayat, A. 2021. Modeling the habitat distribution of Black Francolin (Francolinus francolinus) using MaxEnt algorithm in Sistan region. Journal of Animal Environment, 13(1): 139-146.
Farajzadeh, M., A. Afzali, Y. Khalili and E. Gholichi. 2012. Gully erosion susceptibility assessment using multivariate regression model, case study: Kiasar, Southern Mazandaran Province. Environmental Erosion Research Journal, 2: 42-57 (in Persian).
Gearman M, Blinnikov, Ms. 2019. Mapping the potential distribution of Oak Wilt (Bretziella fagacearum) in east central southeast the Minnesota using the Maxent. Journal of forestry. 117(6): 579-591.
Geissen, V., Kampichler, C., López-de Llergo-Juárez, J.J., GalindoAcántara, A. 2007. Superficial and subterranean soil erosion in Tabasco, tropical Mexico: Development of a decision tree modeling approach. Geoderma 139(3-4): 277–287.
Gómez Gutiérrez, Á., Schnabel, S., Lavado Contador, J.F. 2009. Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling 220 (24): 3630-3637.
Graham, C.H., J. Elith, R.J. Hijmans, A. Guisan, A.T. Peterson, B.A. Loiselle. 2008. The NCEAS predicting species distributions working group. The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45: 239–247.
orhan İNİK, Mustaf UTLU. Erosion Susceptibility Analysis in Bingöl (Türkiye) using Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon’s Entropy methods. 2024. PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4402292/v1].
Jafarzadeh, M. S., Tahmasebipoor, N., Haghizadeh, A., Pourghasemi, H., & Rouhani, H. 2021. Prediction of susceptible areas for groundwater recharge based on maximum entropy model. Advanced Applied Geology, 11(4): 723-739.
Javidan, N.; Kavian, A.; Pourghasemi, H.R.; Conoscenti, C.; Jafarian, Z. Data Mining Technique (Maximum Entropy Model) for Mapping Gully Erosion Susceptibility in the Gorganrood Watershed, Iran. 2020. In Gully Erosion Studies from India and Surrounding Regions; Shit, P.K., Pourghasemi, H.R., Bhunia, G.S., Eds.; Advances in Science, Technology & Innovation; Springer International Publishing: Cham, Switzerland, pp. 427–448
Jiao, Y., Zhao, D., Ding, Y., Liu, Y., Xu, Q., Qiu, Y., Liu, C., Liu, Z., and Zha, Z., Li, R. 2019. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China. Catena, 183: 104221.
Kariminejad N, Hosseinalizadeh M, Pourghasemi HR, Bernatek-Jakiel A, Campetella G, Ownegh M. 2019. Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran. Science Total Enviroment, 677: 281-298.
Karimi Sangchini, E. and M. Ownegh. 2015. Evaluation of gully erosion hazard by statistical models in Naghan Inter basin, Chaharmahal Va Bakhtiari province. Journal of Water and Soil Conservation, 22(5): 315-319 (In Persian).
Kuhnert, P. M., A. K. Henderson, R. Bartley and A. Herr. 2010. Incorporating uncertainty in gully erosion calculations using the random forests modelling approach. Environmetrics 21: 493-509.
Lal, R., 2003. Offsetting global CO2 emissions by restoration of degraded soils and intensification of world agriculture and forestry. Land Degradation & Development, vol. 14(3): 309-322.
Li, Heyang, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, and Yucheng Zhang. 2024. Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models. Remote Sensing 16, no. 24: 4742.
Lombardo, L., Cama, M., Conoscenti, C., Märker, M., & Rotigliano, E. 2015. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Natural Hazards, 79(3): 1621-1648.
Lucà, F., Conforti, M., Robustelli, G., 2011. Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology, 134: 297–308.
Madadi, A., Asgharisaraskanroud, S., Negahban, S., Marhamat, M. (2022). Evaluation
of gully erosion sensitivity using Maximum Entropy model in Shoor River watershed (Mohr Township). Journal of Geography and Environmental Hazards. 11(2): 123-145
Magliulo, P. 2010. Soil erosion susceptibility maps of the Janare Torrent Basin (Southern Italy). J. Maps, 6: 435–447.
Magliulo, P. 2012. Assessing the susceptibility to water-induced soil erosion using a geomorphological, bivariate statistics-based approach. Enviroment earth Science, 67: 1801–1820.
Märker, M., Pelacani, S., Schröder, B. 2011. A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy. Geomorphology 125(4): 530-540.
Milaghardan, A. H., M. Delavar and A. Chehreghan. 2016. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory. Modelling of Earth System Environments 2: 188, 1-10.
Mohammady, M., Dustmohammadian, A. H., Amiri, M., & Kianian, M. K. 2020. Investigating Quantitative Changes of Groundwater in the Semnan Plain. Water Resources Engineering, 13(47), 61-70.
Phillips, S.J., Dudík, M., and Schapire, R.E. 2004. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning Banff, Alberta, Canada, 83p.
Phillips, S.J., Anderson, R.P., and Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190 (3/4): 231-259.
Park, N.W. 2015. Using maximum entropy modeling for landslide susceptibility mapping with multiple geo environmental data sets. Environmental Earth Science, 73: 937–949.
Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, M. Mohammadi and H.R. Moradi. 2013. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences, 6: 2351–2365.
Pourghasemi, H. R., Yousefi, S., Kornejady, A. and Cerdà, A. 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609:764- 775.
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R., Feizizadeh, B. 2017. Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. Science of the Total Environment. 579: 913-927.
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H., & Feizizadeh, B. 2018. Assessing the effectiveness of the maximum entropy model to gully erosion susceptibility prediction in the Kashkan-Poldokhtar Watershed. Watershed Engineering and Management, 10(4): 727-738.
saberchenari K, salmani H, mirabedini M. Landslide Hazard Mapping Using Dempster-Shafer Theory- A Case Study: Ziarat Watershed, Golestan Province, Iran. 2018. Journal of Engineering Geology, 11 (4) :385-404(In Persian).
Saberi Chenari, K., A. Bahremand, V. Berdi Sheikh and C. Biram Komaki. 2016. Gully erosion hazard zoning using of Dempster-Shafer model in the Gharnaveh watershed, Golestan province. EcoHydrology 3(2): 219-231 (In Persian).
Shirani, K., 2017. Modelling and Assessment of Landslide Susceptibility Zonation using Shannon’s Entropy Index and Bayesian Weight of Evidence (Case Study: Sarkhoon Basin, Karoon), J. Water and Soil Sci (Sci. & Technol. Agric. & Natur. Resour.), 21(1), 11.
Shirani, K., and Arabameri, A.R. 2015. Landslide hazard zonation using logistic regression method (Case study: Dez-e-Oulia basin). Journal of Science and Technology of Agriculture and Natural Resources, 19 (72): 321-335.
Shirani, K., Pasandi, M., and Arabameri, A.R. 2018. Landslide susceptibility assessment by Dempster– Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran, Natural Hazards, 93 (3): 1379- 1418.
Shirani K. 2021. Gully Erosion Mapping and Susceptibility Assessment Using Statistical and Probabilistic Methods. JWSS - Isfahan University of Technology; 25 (2): 151-174(In Persian).
Shirani, K., & Naderi Samani, R. 2022. Prioritization of effective parameters, landslide susceptibility zonation using maximum entropy, and dempster shafer in Doab Samsami, Chaharmahal Bakhtiyari. Journal of Range and Watershed Managment, 75(1): 51-72 (In Persian).
Shrestha S, Kang TS. 2019. Assessment of seismically- induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal. Bulletin of Engineering Geology and the Environment. 78(3): 1829-1842/
Silakhori, Z., VahabzadeKebriya, GH, and Pourghasemi, H.R. 2022. Landslide Susceptibility Mapping using Bayesian Model: A Case Study of some Regions of Talar Watershed, Mazandaran Province. Quarterly journal of Environmental Erosion Research. 2(50): 122-140.
Tahmassebipoor, N., Rahmati, O., Noormohamadi, F., Lee, S. 2016. Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arabian Journal of Geosciences, 9(1): 79.
Teimouri, M., & Asadi Nalivan, O. 2021. Determination of Groundwater Spring Potential Using Maximum Entropy, GIS and RS Emphasizing HAND Topographic-Hydrologic New Index (Case Study: Urmia Lake Basin). Iranian Journal of Remote Sensing & GIS, 13(2): 119-138.
Tien Bui, D., B. Pradhan, I. Revhaug, D. B. Nguyen, H. V. Pham and Q. N. Bui. 2015. A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam) Geomatics. Natural Hazards Risk 6: 243-271.
Tsangaratos, P., I Ilia, H. Hong, W. Chen and C. Xu. 2017. Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14: 1091-1111.
Valentin, C., J. Poesen and Y. Li. 2005. Gully erosion: Impacts, factors and control. Catena, 63: 132- 153.
Yousefi Mobarhan, E., Karimi Sangchini, E., 2021. Continuous Rainfall-Runoff Modeling Using HMS-SMA with Emphasis on the Different Calibration Scale. Journal of Chinese Soil and Water Conservation, 52 (2): 112-119.
Yousefi Mobarhan, E and K. Shirani. 2023. Assessment of Maximum Entropy (ME) to identify Effective Factors on Gully Erosion and Determination of Sensitive Areas in Alaa Semnan Watershed. Journal of Watershed Management Research, 14(28): 37-52 (In Persian).
Yousefi Mobarhan, E., & Zandifar, S. (2023). Zoning of changes in the decreasing groundwater table and temporal monitoring of drought in the Ghorove-Dehgolan plain. Iranian Journal of Rainwater Catchment Systems, 11(1), 17-35 (In Persian).
Zakerinejad, R., Märker, M., 2014. Prediction of Gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: a case study in the Mazayejan Plain, Southwest Iran. Geografia Fisica e Dinamicca Quaternaria, 37(1): 67-76.
Zabihi, M., F. Mirchooli, A. Motevalli, A. K. Darvishan, H. R. Pourghasemi, M. A. Zakeri and F. Sadighi. 2018. Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena 161: 1-13.