Document Type : Original Research Paper
Authors
Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, AREEO, Shahrekord, Iran
Abstract
Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.
Keywords
Main Subjects
Achour, Y., and Pourghasemi, H.R., 2019. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front. https://doi.org/10.1016/j.gsf.2019.10.001.
Adnan, R.M., Liang, Z., and Heddam, S.,2019. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol 124371. https://doi.org/10.1016/J.JHYDROL.2019.124371.
Ahmed, B., and Dewan, A., 2017. Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh. Remote Sens 9:304. https://doi.org/10.3390/rs9040304.
Alimohammadlou, Y., Najafi, A., and Yalcin, A., 2013. Landslide process and impacts: a proposed classification method. Catena 104:219–232.https://doi.org/10.1016/j.catena.2012.11.013.
Al-Sudani, Z., Salih, S.Q., Sharafati, A., and Yaseen, Z.M., 2019. Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1–12. https://doi.org/10.1016/j.jhydrol.2019.03.004.
Amiri, M., Pourghasemi, H.R., Ghanbarian, G.A., and 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. https://doi.org/10.1016/j.geoderma.2018.12.042.
Arabameri, A., Chen, W., and Loche, M., 2019a. Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front. https://doi.org/10.1016/j.gsf.2019.11.009.
Arabameri, A., Pradhan, B., and Rezaei, K., 2019b. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosci J 23:669–686. https://doi.org/10.1007/s12303-018-0067-3.
Avand, M., Janizadeh, S., and Naghibi, S.A., 2019. A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water (Switzerland) 11:2076.https://doi.org/10.3390/w11102076.
Barber, C.P., Cochrane, M.A., Souza, C.M., and Laurance, W.F., 2014. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol Conserv 177:203–209. https://doi.org/10.1016/j.biocon.2014.07.004.
Bednarik, M., Magulová, B., Matys, M., and Marschalko, M., 2010. Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Phys Chem Earth 35:162–171. https://doi.org/10.1016/j.pce.2009.12.002.
Blaschke, P.M., Trustrum, N.A., and Hicks, D.L., 2000. Impacts of mass movement erosion on land productivity: a review. Prog Phys Geogr 24:21–52. ttps://doi.org/10.1191/030913300669154532.
Boogar, A.R., Salehi, H., Pourghasemi, H.R., and Blaschke, T., 2019. Predicting habitat suitability and conserving Juniperus spp. habitat using SVM and maximum entropy machine learning techniques. Water (Switzerland) 11:2049. https://doi.org/10.3390/w11102049.
Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. https://doi.org/10.5194/nhess-5-853-2005.
Bui, D.T., Shahabi, H., and Shirzadi, A., 2018. Land subsidence susceptibility mapping in South Korea using machine learning algorithms.Sensors (Switzerland) 18:2464. https://doi.org/10.3390/s18082464.
Adnan, R.M., Liang, Z., and Heddam, S.,2019. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol 124371. https://doi.org/10.1016/J.JHYDROL.2019.124371.
Ahmed, B., and Dewan, A., 2017. Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh. Remote Sens 9:304. https://doi.org/10.3390/rs9040304.
Alimohammadlou, Y., Najafi, A., and Yalcin, A., 2013. Landslide process and impacts: a proposed classification method. Catena 104:219–232.https://doi.org/10.1016/j.catena.2012.11.013.
Al-Sudani, Z., Salih, S.Q., Sharafati, A., and Yaseen, Z.M., 2019. Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1–12. https://doi.org/10.1016/j.jhydrol.2019.03.004.
Amiri, M., Pourghasemi, H.R., Ghanbarian, G.A., and 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. https://doi.org/10.1016/j.geoderma.2018.12.042.
Arabameri, A., Chen, W., and Loche, M., 2019a. Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front. https://doi.org/10.1016/j.gsf.2019.11.009.
Arabameri, A., Pradhan, B., and Rezaei, K., 2019b. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosci J 23:669–686. https://doi.org/10.1007/s12303-018-0067-3.
Avand, M., Janizadeh, S., and Naghibi, S.A., 2019. A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water (Switzerland) 11:2076.https://doi.org/10.3390/w11102076.
Barber, C.P., Cochrane, M.A., Souza, C.M., and Laurance, W.F., 2014. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol Conserv 177:203–209. https://doi.org/10.1016/j.biocon.2014.07.004.
Bednarik, M., Magulová, B., Matys, M., and Marschalko, M., 2010. Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Phys Chem Earth 35:162–171. https://doi.org/10.1016/j.pce.2009.12.002.
Blaschke, P.M., Trustrum, N.A., and Hicks, D.L., 2000. Impacts of mass movement erosion on land productivity: a review. Prog Phys Geogr 24:21–52. ttps://doi.org/10.1191/030913300669154532.
Boogar, A.R., Salehi, H., Pourghasemi, H.R., and Blaschke, T., 2019. Predicting habitat suitability and conserving Juniperus spp. habitat using SVM and maximum entropy machine learning techniques. Water (Switzerland) 11:2049. https://doi.org/10.3390/w11102049.
Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. https://doi.org/10.5194/nhess-5-853-2005.
Bui, D.T., Shahabi, H., and Shirzadi, A., 2018. Land subsidence susceptibility mapping in South Korea using machine learning algorithms.Sensors (Switzerland) 18:2464. https://doi.org/10.3390/s18082464.
Busto Serrano, N., and Suárez Sánchez, A., and Sánchez Lasheras, F., 2020. Identification of gender differences in the factors influencing shoulders, neck and upper limb MSD by means of multivariate adaptive regression splines (MARS). Appl Ergon 82:102981. https://doi.org/10.1016/j.apergo.2019.102981.
Chaytor, J.D., Twichell, D.C., and Ten Brink, U.S., 2007. Revisiting submarine mass movements along the U.S. Atlantic continental margin: implications for tsunami hazards. In: Submarine Mass Movements and Their Consequences, 3rd International Symposium. Springer, pp 395–403. https://doi.org/10.1007/978-1-4020-6512-5_41.
Chen, W., Pourghasemi, H.R., Kornejady, A., and Zhang, N., 2017b. Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVMmachine learning techniques.Geoderma 305:314–327. https://doi.org/10.1016/j.geoderma.2017.06.020.
Corenblit, D., Baas, A.C.W., and Bornette, G., 2011. Feedbacks between geomorphology and biota controlling Earth surface processes and landforms: a review of foundation concepts and current understandings.Earth-Sci Rev 106:307–331. https://doi.org/10.1016/j.earscirev.2011.03.002.
Corte-Real, J., Zhang, X., and Wang, X., 1995. Downscaling GCM information to regional scales: a non-parametric multivariate regression approach. -Clim Dyn 11:413–424. https://doi.org/10.1007/BF00209515.
Deichmann, J., Eshghi, A., and Haughton, D., 2002. Application of multiple adaptive regression splines (mars) in direct response modeling. J Interact Mark 16:15–27. https://doi.org/10.1002/dir.10040.
Deng, X., Liu, Q., Deng, Y., and Mahadevan, S., 2016. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci (Ny) 340:250–261.
Dietrich, W.E., Wilson, C.J., and Montgomery, D.R., 1992. Erosion thresholds and land surface morphology. Geology 20:675–679. https://doi.org/10.1130/0091-7613 (1992)020<0675: ETALSM>2.3.CO; 2.
Elith, J., Leathwick, J.R., and Hastie, T., 2008. A working guide to boosted regression trees. J Anim Ecol 77:802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x.
Feizizadeh, B., and Blaschke, T., 2012. Comparing GIS-multicriteria decision analysis for landslide susceptibility mapping for the lake basin, Iran. Int Geosci Remote Sens Symp 65:5390–5393. https://doi.org/10.1109/IGARSS.2012.6352388.
Fort, M., Cossart, E., and Deline, P., 2009. Geomorphic impacts of large and rapid mass movements: a reviewImpacts géomorphologiques des mouvements de masse volumineux ET rapides: une revue.Groupe français de géomorphologie. https://doi.org/10.4000/geomorphologie.7495
Friedman, J., Hastie, T., and Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407. https://doi: 10.1214/aos/1016218223
Gayen, A., Pourghasemi, H.R., and Saha, S., 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436.
Ghanbarian, G., Raoufat, M.R., Pourghasemi, H.R., and Safaeian, R., 2019.Habitat suitability mapping of Artemisia aucheri Boiss based on the GLM model in R. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier, pp 213–227. https://doi:10.1016/B978-0-12-815226-3.00009-0.
Gokceoglu, C., Nefeslioglu, H.A., and Sezer, E., 2010. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng 2010. https://doi.org/10.1155/2010/901095.
Gu, C., and Wahba, G., 1991. Discussion: multivariate adaptive regression splines. Ann Stat 19:115–123. https://doi.org/10.1214/aos/1176347972.
Gutierrez, R.R., Abad, J.D., Choi, M., and Montoro, H., 2014. Characterization of confluences in free meandering rivers of the Amazon basin. Geomorphology 220:1–14. https://doi.org/10.1016/j.geomorph.2014.05.011.
Hastie, T., and Tibshirani, R., 1996. Discriminant analysis by Gaussian mixtures.J R Stat Soc Ser B 58:155–176. https://doi.org/10.1016/j.jmva.2003.12.003.
Hawryło, P., Bednarz, B., Wężyk, P., and Szostak, M., 2018. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. https://doi.org/10.1080/22797254.2017.1417745.
Chaytor, J.D., Twichell, D.C., and Ten Brink, U.S., 2007. Revisiting submarine mass movements along the U.S. Atlantic continental margin: implications for tsunami hazards. In: Submarine Mass Movements and Their Consequences, 3rd International Symposium. Springer, pp 395–403. https://doi.org/10.1007/978-1-4020-6512-5_41.
Chen, W., Pourghasemi, H.R., Kornejady, A., and Zhang, N., 2017b. Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVMmachine learning techniques.Geoderma 305:314–327. https://doi.org/10.1016/j.geoderma.2017.06.020.
Corenblit, D., Baas, A.C.W., and Bornette, G., 2011. Feedbacks between geomorphology and biota controlling Earth surface processes and landforms: a review of foundation concepts and current understandings.Earth-Sci Rev 106:307–331. https://doi.org/10.1016/j.earscirev.2011.03.002.
Corte-Real, J., Zhang, X., and Wang, X., 1995. Downscaling GCM information to regional scales: a non-parametric multivariate regression approach. -Clim Dyn 11:413–424. https://doi.org/10.1007/BF00209515.
Deichmann, J., Eshghi, A., and Haughton, D., 2002. Application of multiple adaptive regression splines (mars) in direct response modeling. J Interact Mark 16:15–27. https://doi.org/10.1002/dir.10040.
Deng, X., Liu, Q., Deng, Y., and Mahadevan, S., 2016. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci (Ny) 340:250–261.
Dietrich, W.E., Wilson, C.J., and Montgomery, D.R., 1992. Erosion thresholds and land surface morphology. Geology 20:675–679. https://doi.org/10.1130/0091-7613 (1992)020<0675: ETALSM>2.3.CO; 2.
Elith, J., Leathwick, J.R., and Hastie, T., 2008. A working guide to boosted regression trees. J Anim Ecol 77:802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x.
Feizizadeh, B., and Blaschke, T., 2012. Comparing GIS-multicriteria decision analysis for landslide susceptibility mapping for the lake basin, Iran. Int Geosci Remote Sens Symp 65:5390–5393. https://doi.org/10.1109/IGARSS.2012.6352388.
Fort, M., Cossart, E., and Deline, P., 2009. Geomorphic impacts of large and rapid mass movements: a reviewImpacts géomorphologiques des mouvements de masse volumineux ET rapides: une revue.Groupe français de géomorphologie. https://doi.org/10.4000/geomorphologie.7495
Friedman, J., Hastie, T., and Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407. https://doi: 10.1214/aos/1016218223
Gayen, A., Pourghasemi, H.R., and Saha, S., 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436.
Ghanbarian, G., Raoufat, M.R., Pourghasemi, H.R., and Safaeian, R., 2019.Habitat suitability mapping of Artemisia aucheri Boiss based on the GLM model in R. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier, pp 213–227. https://doi:10.1016/B978-0-12-815226-3.00009-0.
Gokceoglu, C., Nefeslioglu, H.A., and Sezer, E., 2010. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng 2010. https://doi.org/10.1155/2010/901095.
Gu, C., and Wahba, G., 1991. Discussion: multivariate adaptive regression splines. Ann Stat 19:115–123. https://doi.org/10.1214/aos/1176347972.
Gutierrez, R.R., Abad, J.D., Choi, M., and Montoro, H., 2014. Characterization of confluences in free meandering rivers of the Amazon basin. Geomorphology 220:1–14. https://doi.org/10.1016/j.geomorph.2014.05.011.
Hastie, T., and Tibshirani, R., 1996. Discriminant analysis by Gaussian mixtures.J R Stat Soc Ser B 58:155–176. https://doi.org/10.1016/j.jmva.2003.12.003.
Hawryło, P., Bednarz, B., Wężyk, P., and Szostak, M., 2018. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. https://doi.org/10.1080/22797254.2017.1417745.
Hjort, J., and Luoto, M., 2013. Statistical methods for geomorphic distribution modeling. Treatise Geomorphol 2:59–73. https://doi.org/10.1016/B978-0-12-374739-6.00028-2.
Hong, H., Pradhan, B., Xu, C., and Tien Bui, D., 2015. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines.Catena 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019.
Hong, H., Panahi, M., and Shirzadi, A., 2018. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114.
Hosseinalizadeh, M., Kariminejad, N., and Rahmati, O., 2019b. How can statistical and artificial intelligence approaches predict piping erosion susceptibility? Sci Total Environ 646:1554–1566. https://doi.org/10.1016/j.scitotenv.2018.07.396.
Hungr, O., Evans, S.G., and Hazzard, J., 1999. Magnitude and frequency of rock falls and rock slides along themain transportation corridors of southwestern British Columbia. Can Geotech J 36:224–238.
Imaizumi, F., and Sidle, R.C., 2012. Effect of forest harvesting on hydrogeomorphic processes in steep terrain of Central Japan.Geomorphology 169–170:109–122. https://doi.org/10.1016/j.geomorph.2012.04.017.
Jebur, M.N., Pradhan, B., and Tehrany, M.S., 2014. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013.
Ju, J., Kolaczyk, ED., and Gopal, S., 2003. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sens Environ 84:550–560. https://doi.org/10.1016/S0034-4257(02)00172-4
Kalantar, B., Al-Najjar, H.A.H., and Pradhan, B., 2019. Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water 11:1909.
Kennedy, I.T.R., Petley, D.N., Williams, R., and Murray, V., 2015. A systematicreview of the health impacts of mass earth movements (landslides). PLoS Curr 7. h t t p s : / / d o i . org/10.1371/currents. d i s.1d49e84c8bbe678b0e70cf7fc35d0b77
Kim, J.C., Lee, S., Jung, H.S., and Lee, S., 2018.Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang,-Korea. Geocarto Int 33:1000–1015. https://doi.org/10.1080/10106049.2017.1323964.
Korup, O., and Stolle, A., 2014. Landslide prediction from machine learning. Geol Today 30:26–33. https://doi.org/10.1111/gto.12034.
Krishnakumar, K.N., Rao, G.P. and Gopakumar, C.S., 2009. Rainfall trends in twentieth century over Kerala, India. Atmos Environ 43:1940–1944. https://doi.org/10.1016/j.atmosenv.2008.12.053
Lai, W., and Khan, A., 2012. Modeling dam-break flood over natural rivers using discontinuous Galerkin method. J Hydrodyn 24:467–478.https://doi.org/10.1016/S1001-6058(11)60268-0.
Lazarus, E.D., and Constantine, J.A., 2013. Generic theory for channel sinuosity.Proc Natl Acad Sci U S A 110:8447–8452. https://doi.org/10.1073/pnas.1214074110.
Lee, S., and Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models.Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y.
Lee, J.D., Sun, Y., and Taylor, J.E ., 2015. On model selection consistency of regularized M-estimators. Electron J Stat 9:608–642. https://doi.org/10.1214/15-EJS1013.
Mastere, M., 2020. Mass movement hazard assessment at a medium scale using weight of evidence model and neo-predictive variables creation. In: Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development. Springer, pp 73–85. https://doi:10.1007/978-3-030-21166-0_7.
Merow, C., Smith, M.J., and Silander, J.A., 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop) 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x.
Moayedi, H., Mehrabi, M., and Mosallanezhad, M., 2019. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984. https://doi.org/10.1007/s00366-018-0644-0.
Moeyersons, J., Van Den Eeckhaut, M., and Nyssen, J., 2008. Mass movement mapping for geomorphological understanding and sustainable development: Tigray, Ethiopia. Catena 75:45–54. https://doi.org/10.1016/j.catena.2008.04.004.
Hong, H., Pradhan, B., Xu, C., and Tien Bui, D., 2015. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines.Catena 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019.
Hong, H., Panahi, M., and Shirzadi, A., 2018. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114.
Hosseinalizadeh, M., Kariminejad, N., and Rahmati, O., 2019b. How can statistical and artificial intelligence approaches predict piping erosion susceptibility? Sci Total Environ 646:1554–1566. https://doi.org/10.1016/j.scitotenv.2018.07.396.
Hungr, O., Evans, S.G., and Hazzard, J., 1999. Magnitude and frequency of rock falls and rock slides along themain transportation corridors of southwestern British Columbia. Can Geotech J 36:224–238.
Imaizumi, F., and Sidle, R.C., 2012. Effect of forest harvesting on hydrogeomorphic processes in steep terrain of Central Japan.Geomorphology 169–170:109–122. https://doi.org/10.1016/j.geomorph.2012.04.017.
Jebur, M.N., Pradhan, B., and Tehrany, M.S., 2014. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013.
Ju, J., Kolaczyk, ED., and Gopal, S., 2003. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sens Environ 84:550–560. https://doi.org/10.1016/S0034-4257(02)00172-4
Kalantar, B., Al-Najjar, H.A.H., and Pradhan, B., 2019. Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water 11:1909.
Kennedy, I.T.R., Petley, D.N., Williams, R., and Murray, V., 2015. A systematicreview of the health impacts of mass earth movements (landslides). PLoS Curr 7. h t t p s : / / d o i . org/10.1371/currents. d i s.1d49e84c8bbe678b0e70cf7fc35d0b77
Kim, J.C., Lee, S., Jung, H.S., and Lee, S., 2018.Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang,-Korea. Geocarto Int 33:1000–1015. https://doi.org/10.1080/10106049.2017.1323964.
Korup, O., and Stolle, A., 2014. Landslide prediction from machine learning. Geol Today 30:26–33. https://doi.org/10.1111/gto.12034.
Krishnakumar, K.N., Rao, G.P. and Gopakumar, C.S., 2009. Rainfall trends in twentieth century over Kerala, India. Atmos Environ 43:1940–1944. https://doi.org/10.1016/j.atmosenv.2008.12.053
Lai, W., and Khan, A., 2012. Modeling dam-break flood over natural rivers using discontinuous Galerkin method. J Hydrodyn 24:467–478.https://doi.org/10.1016/S1001-6058(11)60268-0.
Lazarus, E.D., and Constantine, J.A., 2013. Generic theory for channel sinuosity.Proc Natl Acad Sci U S A 110:8447–8452. https://doi.org/10.1073/pnas.1214074110.
Lee, S., and Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models.Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y.
Lee, J.D., Sun, Y., and Taylor, J.E ., 2015. On model selection consistency of regularized M-estimators. Electron J Stat 9:608–642. https://doi.org/10.1214/15-EJS1013.
Mastere, M., 2020. Mass movement hazard assessment at a medium scale using weight of evidence model and neo-predictive variables creation. In: Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development. Springer, pp 73–85. https://doi:10.1007/978-3-030-21166-0_7.
Merow, C., Smith, M.J., and Silander, J.A., 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop) 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x.
Moayedi, H., Mehrabi, M., and Mosallanezhad, M., 2019. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984. https://doi.org/10.1007/s00366-018-0644-0.
Moeyersons, J., Van Den Eeckhaut, M., and Nyssen, J., 2008. Mass movement mapping for geomorphological understanding and sustainable development: Tigray, Ethiopia. Catena 75:45–54. https://doi.org/10.1016/j.catena.2008.04.004.
Morris, K., and Nicholas, PD., 2016. Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures. Comput Stat Data Anal 97:133–150. https://doi.org/10.1016/j.
Murray, A.B., Lazarus, E., and Ashton, A., 2009. Geomorphology, complexity, and the emerging science of the Earth’s surface.Geomorphology 103:496–505 https://doi:10.1016/j.geomorph.2008.08.013
Nabiollahi, K., Eskandari, S., and Taghizadeh-Mehrjardi, R., 2019. Assessing soil organic carbon stocks under land-use change scenarios using random forest models. Carbon Manag 10:63–77. https://doi.org/10.1080/17583004.2018.1553434.
Naghibi, S.A., and Pourghasemi, H.R., 2015. Acomparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29:5217–5236. https://doi.org/10.1007/s11269-015-1114-8.
Ozdemir, A., and Altural, T., 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan mountains, SW Turkey. J Asian Earth Sci 64:180–197. https://doi.org/10.1016/j.jseaes.2012.12.014.
Parker, J.R., 2001. Rank and response combination from confusion matrix data. Inf fusion 2:113–120. https://doi.org/10.1016/S1566-2535(01)00030-6
Pertille, R.H., Sachet, M.R., and Guerrezi, M.T., and Citadin, I., 2019. An R package to quantify different chilling and heat models for temperate fruit trees. Comput Electron Agric 167:105067. https://doi.org/10.1016/j.compag.2019.105067.
Popescu, M.E., 1994. A suggested method for reporting landslide causes.Bull Int Assoc Eng Geol l’Association Int Géologie l’Ingénieur 50:71–74. osti, M. Hashizume, and Masayuki Watanabe https://doi.org/10.1007/BF02590201
Pourghasemi, H.R., Pradhan, B., and Gokceoglu, C., 2013. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6:2351–2365. https://doi.org/10.1007/s12517-012-0532-7.
Pourghasemi, H.R., Yousefi, S., Kornejady, A., and Cerdà, A., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775. https://doi.org/10.1016/j.scitotenv.2017.07.198.
Pourghasemi, H.R., Gayen, A., and Edalat, M., 2019a. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? Geosci Front. https://doi.org/10.1016/j.gsf.2019.10.008.
Pourghasemi, H.R., Gayen, A., and Panahi, M., 2019b. Multi-hazard probability assessment and mapping in Iran. Sci Total Environ 692:556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203.
Pourghasemi, H.R., Pouyan, S., Heidari, B., Farajzadeh, Z., Fallah, S.R., Babaei, S., Khosravi, R., Etemadi, M., Ghanbarian, G., Farhadi, A., Safaeian, R., Heidari, Z., Tarazkar, M.H., Tiefenbacher, J.P., Azmi, A., and Sadeghian, F., 2020b. Spatial modelling, risk mapping, change detection,and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between 19 February to 14 June 2020). Int J Infect is.https://doi.org/10.1016/j.ijid.2020.06.058.
Pourtaghi, Z.S. Pourghasemi, H.R., and Rossi, M., 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci 73:1515–1533. https://doi.org:10.1007/s12665-014-3502-4
Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R., and Semeraro, T., 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic 64:72–84. https://doi.org/10.1016/j.ecolind.2015.12.030.
Pouteau, R., Meyer, J.Y. Taputuarai, R., and Stoll, B., 2012. Support vector machines to map rare and endangered native plants in Pacific islands forests. Ecol Inform 9:37–46. https://doi.org/10.1016/j.ecoinf.2012.03.003.
Pradhan, B., Abokharima, M.H., Jebur, M.N., and Tehrany, M.S., 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042. https://doi.org 10.1007/s11069-014-1128-1.
Prakasam, C., Aravinth, R., Kanwar, V.S., and Nagarajan, B., 2020. Landslide hazard mapping using geo-environmental parameters—a case study on Shimla Tehsil, Himachal Pradesh. In: Lecture Notes in Civil Engineering. Springer, pp 123–139. . https://doi.org 10.1007/978-981-13-7067-0_9.
Rahmani, R., Sadoddin, A., and Ghorbani, S., 2011. Measuring and modelling precipitation components in an Oriental beech stand of the Hyrcanian region, Iran. J Hydrol 404:294–303. https://doi.org/10.1016/j.jhydrol.2011.04.036.
Murray, A.B., Lazarus, E., and Ashton, A., 2009. Geomorphology, complexity, and the emerging science of the Earth’s surface.Geomorphology 103:496–505 https://doi:10.1016/j.geomorph.2008.08.013
Nabiollahi, K., Eskandari, S., and Taghizadeh-Mehrjardi, R., 2019. Assessing soil organic carbon stocks under land-use change scenarios using random forest models. Carbon Manag 10:63–77. https://doi.org/10.1080/17583004.2018.1553434.
Naghibi, S.A., and Pourghasemi, H.R., 2015. Acomparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29:5217–5236. https://doi.org/10.1007/s11269-015-1114-8.
Ozdemir, A., and Altural, T., 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan mountains, SW Turkey. J Asian Earth Sci 64:180–197. https://doi.org/10.1016/j.jseaes.2012.12.014.
Parker, J.R., 2001. Rank and response combination from confusion matrix data. Inf fusion 2:113–120. https://doi.org/10.1016/S1566-2535(01)00030-6
Pertille, R.H., Sachet, M.R., and Guerrezi, M.T., and Citadin, I., 2019. An R package to quantify different chilling and heat models for temperate fruit trees. Comput Electron Agric 167:105067. https://doi.org/10.1016/j.compag.2019.105067.
Popescu, M.E., 1994. A suggested method for reporting landslide causes.Bull Int Assoc Eng Geol l’Association Int Géologie l’Ingénieur 50:71–74. osti, M. Hashizume, and Masayuki Watanabe https://doi.org/10.1007/BF02590201
Pourghasemi, H.R., Pradhan, B., and Gokceoglu, C., 2013. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6:2351–2365. https://doi.org/10.1007/s12517-012-0532-7.
Pourghasemi, H.R., Yousefi, S., Kornejady, A., and Cerdà, A., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775. https://doi.org/10.1016/j.scitotenv.2017.07.198.
Pourghasemi, H.R., Gayen, A., and Edalat, M., 2019a. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? Geosci Front. https://doi.org/10.1016/j.gsf.2019.10.008.
Pourghasemi, H.R., Gayen, A., and Panahi, M., 2019b. Multi-hazard probability assessment and mapping in Iran. Sci Total Environ 692:556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203.
Pourghasemi, H.R., Pouyan, S., Heidari, B., Farajzadeh, Z., Fallah, S.R., Babaei, S., Khosravi, R., Etemadi, M., Ghanbarian, G., Farhadi, A., Safaeian, R., Heidari, Z., Tarazkar, M.H., Tiefenbacher, J.P., Azmi, A., and Sadeghian, F., 2020b. Spatial modelling, risk mapping, change detection,and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between 19 February to 14 June 2020). Int J Infect is.https://doi.org/10.1016/j.ijid.2020.06.058.
Pourtaghi, Z.S. Pourghasemi, H.R., and Rossi, M., 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci 73:1515–1533. https://doi.org:10.1007/s12665-014-3502-4
Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R., and Semeraro, T., 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic 64:72–84. https://doi.org/10.1016/j.ecolind.2015.12.030.
Pouteau, R., Meyer, J.Y. Taputuarai, R., and Stoll, B., 2012. Support vector machines to map rare and endangered native plants in Pacific islands forests. Ecol Inform 9:37–46. https://doi.org/10.1016/j.ecoinf.2012.03.003.
Pradhan, B., Abokharima, M.H., Jebur, M.N., and Tehrany, M.S., 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042. https://doi.org 10.1007/s11069-014-1128-1.
Prakasam, C., Aravinth, R., Kanwar, V.S., and Nagarajan, B., 2020. Landslide hazard mapping using geo-environmental parameters—a case study on Shimla Tehsil, Himachal Pradesh. In: Lecture Notes in Civil Engineering. Springer, pp 123–139. . https://doi.org 10.1007/978-981-13-7067-0_9.
Rahmani, R., Sadoddin, A., and Ghorbani, S., 2011. Measuring and modelling precipitation components in an Oriental beech stand of the Hyrcanian region, Iran. J Hydrol 404:294–303. https://doi.org/10.1016/j.jhydrol.2011.04.036.
Rahmati, O., Pourghasemi, H.R., and Melesse, A.M., 2016. Application of GISbased data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region,Iran. Catena 137:360–372. https://doi.org/10.1016/j.catena.2015.10.010.
Rahmati, O., Kalantari, Z., Samadi. 2019a. GIS-based site selection for check dams in watersheds: considering geomorphometric and topo-hydrological factors. Sustain 11:5639. https://doi.org/10.3390/su11205639.
Rahmati, O., Yousefi, S., and Kalantari, Z., 2019b. Multi-hazard exposure mapping using machine learning techniques: a case study from Iran. Remote Sens 11:1943. https://doi.org/10.3390/rs11161943.
Reineking, B., and Schröder, B., 2006. Constrain to perform: regularization of habitat models. EcolModel 193:675–690. https://doi.org/10.1016/j.ecolmodel.2005.10.003.
Ruppert, D., 2004. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Santos, M., Aguiar, M., and Oliveira, A., 2020. Vulnerability to mass movements’ hazards. Contribution of sociology to increasing community resilience. In: Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge. Springer, pp 105–108https://doi.org/10.1007/978-3-030-34397-2_21.
Santosa, F., and Symes, W., 1986. Linear inversion of band-limited reflection seismograms. SIAM J Sci Stat Comput 7:1307–1330. https://doi.org/10.1137/090708.
Schmid, U., Rösch, P., and Krause, M., 2009. Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy. Chemom Intell Lab Syst 96:159–171. https://doi.org/10.1016/j.chemolab.2009.01.008.
Shahabi, H., Jarihani, B., and Piralilou, S.T., 2019.A semi-automated objectbased gully networks detection using different machine learning models: a case study of bowen catchment, Queensland, Australia.Sensors (Switzerland) 19:4893. https://doi.org/10.3390/s19224893
Shataee, S., Weinaker, H., and Babanejad, M., 2011. Plot-level forest volume estimation using airborne laser scanner and TM data, comparison of boosting and random forest tree regression algorithms. Procedia Environ Sci 7:68–73. https://doi.org/10.1016/j.proenv.2011.07.013.
Shi, Y., and Jin, F., 2009. Landslide stability analysis based on generalized information entropy. In: Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009. IEEE, pp 83–85. https://doi.org/10.1109/ESIAT.2009.258.
Shirzadi, A., Bui, D.T., and Pham, B., 2017. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76:60 -Sidle RC, Ziegler AD, Negishi JN, et al .2006. Erosion processes in steep terrain-truths, myths, and uncertainties related to forest management in Southeast Asia. 224:199–225. doi: https://doi.org/10.1016/j.foreco.2005.12.019.
Simon, N., Friedman, J., Hastie, T., and Tibshirani, R., 2013. A sparse-group lasso. J Comput Graph Stat 22:231–245 Stoffel M, Huggel C (2012) Effects of climate change on mass movements in mountain environments. Prog Phys Geogr 36:421–439.https://doi.org/10.1177/0309133312441010.
Suresh, D., Yarrakula, K., and Venkateswarlu, B., 2019. Risk mapping analysis with geographic information systems for landslides using supply chain. In: Emerging Applications in Supply Chains for Sustainable Business Development. IGI Global, pp 131–141. https://doi.org/10.4018/978-1-5225-5424-0.ch008.
Taalab, K., Cheng, T., and Zhang, Y., 2018. Mapping landslide susceptibility and types using random forest. Big Earth Data 2:159–178. https://doi.org/10.1080/20964471.2018.1472392.
Taylor, R., Digby, P., and Kempton, R.A., 1987. Multivariate analysis of ecological communities. Springer Science & Business Media.
Thanh Noi, P., and Kappas, M., 2018. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18:18. https://doi.org/10.3390/s18010018.
Tiranti, D., and Cremonini, R., 2019. Editorial: landslide hazard in a changing environment. Front Earth Sci 7. https://doi.org/10.3389/feart.2019.00003.
Vilar, L., Gómez, I., and Martínez-Vega, J., 2016. Multitemporal modelling of socio-economic wildfire drivers in Central Spain between the 1980s and the 2000s: comparing generalized linear models to machine learning algorithms. PLoS One 11:e0161344. https://doi.org/10.1371/journal.pone.0161344.
Visa, S., Ramsay, B., Ralescu A.L., and Van Der Knaap, E., 2011.Confusion matrix-based feature selection. MAICS 710:120–127.
Rahmati, O., Kalantari, Z., Samadi. 2019a. GIS-based site selection for check dams in watersheds: considering geomorphometric and topo-hydrological factors. Sustain 11:5639. https://doi.org/10.3390/su11205639.
Rahmati, O., Yousefi, S., and Kalantari, Z., 2019b. Multi-hazard exposure mapping using machine learning techniques: a case study from Iran. Remote Sens 11:1943. https://doi.org/10.3390/rs11161943.
Reineking, B., and Schröder, B., 2006. Constrain to perform: regularization of habitat models. EcolModel 193:675–690. https://doi.org/10.1016/j.ecolmodel.2005.10.003.
Ruppert, D., 2004. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Santos, M., Aguiar, M., and Oliveira, A., 2020. Vulnerability to mass movements’ hazards. Contribution of sociology to increasing community resilience. In: Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge. Springer, pp 105–108https://doi.org/10.1007/978-3-030-34397-2_21.
Santosa, F., and Symes, W., 1986. Linear inversion of band-limited reflection seismograms. SIAM J Sci Stat Comput 7:1307–1330. https://doi.org/10.1137/090708.
Schmid, U., Rösch, P., and Krause, M., 2009. Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy. Chemom Intell Lab Syst 96:159–171. https://doi.org/10.1016/j.chemolab.2009.01.008.
Shahabi, H., Jarihani, B., and Piralilou, S.T., 2019.A semi-automated objectbased gully networks detection using different machine learning models: a case study of bowen catchment, Queensland, Australia.Sensors (Switzerland) 19:4893. https://doi.org/10.3390/s19224893
Shataee, S., Weinaker, H., and Babanejad, M., 2011. Plot-level forest volume estimation using airborne laser scanner and TM data, comparison of boosting and random forest tree regression algorithms. Procedia Environ Sci 7:68–73. https://doi.org/10.1016/j.proenv.2011.07.013.
Shi, Y., and Jin, F., 2009. Landslide stability analysis based on generalized information entropy. In: Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009. IEEE, pp 83–85. https://doi.org/10.1109/ESIAT.2009.258.
Shirzadi, A., Bui, D.T., and Pham, B., 2017. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76:60 -Sidle RC, Ziegler AD, Negishi JN, et al .2006. Erosion processes in steep terrain-truths, myths, and uncertainties related to forest management in Southeast Asia. 224:199–225. doi: https://doi.org/10.1016/j.foreco.2005.12.019.
Simon, N., Friedman, J., Hastie, T., and Tibshirani, R., 2013. A sparse-group lasso. J Comput Graph Stat 22:231–245 Stoffel M, Huggel C (2012) Effects of climate change on mass movements in mountain environments. Prog Phys Geogr 36:421–439.https://doi.org/10.1177/0309133312441010.
Suresh, D., Yarrakula, K., and Venkateswarlu, B., 2019. Risk mapping analysis with geographic information systems for landslides using supply chain. In: Emerging Applications in Supply Chains for Sustainable Business Development. IGI Global, pp 131–141. https://doi.org/10.4018/978-1-5225-5424-0.ch008.
Taalab, K., Cheng, T., and Zhang, Y., 2018. Mapping landslide susceptibility and types using random forest. Big Earth Data 2:159–178. https://doi.org/10.1080/20964471.2018.1472392.
Taylor, R., Digby, P., and Kempton, R.A., 1987. Multivariate analysis of ecological communities. Springer Science & Business Media.
Thanh Noi, P., and Kappas, M., 2018. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18:18. https://doi.org/10.3390/s18010018.
Tiranti, D., and Cremonini, R., 2019. Editorial: landslide hazard in a changing environment. Front Earth Sci 7. https://doi.org/10.3389/feart.2019.00003.
Vilar, L., Gómez, I., and Martínez-Vega, J., 2016. Multitemporal modelling of socio-economic wildfire drivers in Central Spain between the 1980s and the 2000s: comparing generalized linear models to machine learning algorithms. PLoS One 11:e0161344. https://doi.org/10.1371/journal.pone.0161344.
Visa, S., Ramsay, B., Ralescu A.L., and Van Der Knaap, E., 2011.Confusion matrix-based feature selection. MAICS 710:120–127.
Vorpahl, P., Elsenbeer, H., Märker, M., and Schröder, B., 2012. How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39. https://doi.org/10.1016/j.ecolmodel.2011.12.007.
Yamada, Y., Kawamura, K., and Ikehara, K., 2012. Submarine mass movements and their consequences. In: SubmarineMass Movements and Their Consequences - 5th International Symposium. Springer, pp 1–12. . https://doi.org/10.1007/978-94-007-2162-3_1.
Yousefi, S., Moradi, H., Boll, J., and Schönbrodt-Stitt, S., 2016. Effects of road construction on soil degradation and nutrient transport in Caspian Hyrcanian mixed forests. Geoderma 284:103–112. https://doi.org/10.1016/j.geoderma.2016.09.002.
Youssef, A.M., Pourghasemi, H.R., El-Haddad, B.A., and Dhahry, B.K., 2016. Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull Eng Geol Environ 75:63–87. https://doi.org/10.1007/s10064-015-0734-9.
Zabihi, M., Pourghasemi, H.R., Motevalli, A., and Zakeri, M.A., 2019. Gully erosion modeling using gis-based data mining techniques in northern Iran: a comparison between boosted regression tree and multivariate adaptive regression spline. In: Advances in Natural and Technological Hazards Research. Springer, pp 1–26. https://doi.org/10.1007/978-3-319-73383-8_1.
Yamada, Y., Kawamura, K., and Ikehara, K., 2012. Submarine mass movements and their consequences. In: SubmarineMass Movements and Their Consequences - 5th International Symposium. Springer, pp 1–12. . https://doi.org/10.1007/978-94-007-2162-3_1.
Yousefi, S., Moradi, H., Boll, J., and Schönbrodt-Stitt, S., 2016. Effects of road construction on soil degradation and nutrient transport in Caspian Hyrcanian mixed forests. Geoderma 284:103–112. https://doi.org/10.1016/j.geoderma.2016.09.002.
Youssef, A.M., Pourghasemi, H.R., El-Haddad, B.A., and Dhahry, B.K., 2016. Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull Eng Geol Environ 75:63–87. https://doi.org/10.1007/s10064-015-0734-9.
Zabihi, M., Pourghasemi, H.R., Motevalli, A., and Zakeri, M.A., 2019. Gully erosion modeling using gis-based data mining techniques in northern Iran: a comparison between boosted regression tree and multivariate adaptive regression spline. In: Advances in Natural and Technological Hazards Research. Springer, pp 1–26. https://doi.org/10.1007/978-3-319-73383-8_1.
)