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Original Articles

Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region

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Pages 4628-4654 | Received 08 Nov 2020, Accepted 24 Jan 2021, Published online: 22 Mar 2021

References

  • Agharazi H, Davoudirad AA, Khosrobagi S, Shadfar S, Nikchah S, Najimi A. 2017. Gully erosion Sufficiency mapping at Robatturk Watershed (Iran) using an artificial neural network model. Int J Comput Sci Netw Secur. 17(4):14.
  • Albaradeyia I, Hani A, Shahrour I. 2011. WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region. Environ Monit Assess. 180(1–4):537–556.
  • Angileri SE, 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.
  • Arabameri A, Cerda A, Pradhan B, Tiefenbacher JP, Lombardo L, Bui DT. 2020. A methodological comparison of head-cut based gully erosion susceptibility models: combined use of statistical and artificial intelligence. Geomorphology. 359:107136.
  • Arabameri A, Cerda A, Rodrigo CJ, Pradhan B, Sohrabi M, Blaschke T, Tien BD. 2019. Proposing a novel predictive technique for gully erosion susceptibility mapping in arid and semi-arid regions (Iran). Remote Sens. 11(21):2577.
  • Arabameri A, Chen W, Loche M, Zhao X, Li Y, Lombardo L, Cerda A, Pradhan B, Bui DT. 2019. Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front. 11(5):1609–1620.
  • Arabameri A, Pourghasemi HR. 2019. Spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In: Spat Model GIS R Earth Environ Sci. Amsterdam: Elsevier; p. 299–321.
  • Arabameri A, Pradhan B, Rezaei K. 2019. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manage. 232:928–942.
  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment part I: model development 1. J Am Water Resour Assoc. 34(1):73–89.
  • Avand M, Janizadeh S, Naghibi SA, Pourghasemi HR, Khosrobeigi BS, Blaschke T. 2019. A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water. 11(10):2076.
  • Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, Ahmad BB. 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: investigation of applicability of certainty factor and maximum entropy models. Sci Tot Environ. 655:684–696.
  • Band SS, Janizadeh S, Chandra Pal S, Saha A, Chakrabortty R, Shokri M, Mosavi A. 2020. Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility. Sensors. 20(19):5609.
  • Barendregt RW, Ongley ED. 1977. Piping in the Milk River Canyon, southeastern Alberta; a contemporary dryland geomorphic process. IAHS-AISH Publ Paris. 122:233–243.
  • Bernatek-Jakiel A, Poesen J. 2018. Subsurface erosion by soil piping: significance and research needs. Earth Sci Rev. 185:1107–1128.
  • Bhattacharya RK, Chatterjee ND, Das K. 2021. Land use and land cover change and its resultant erosion susceptible level: an appraisal using RUSLE and logistic regression in a tropical plateau basin of West Bengal. Environ Dev Sustain. 23(2):1411–1436.
  • Blair TC. 1987. Sedimentary processes, vertical stratification sequences, and geomorphology of the Roaring River alluvial fan, Rocky Mountain National Park, Colorado. J Sediment Res. 57(1):1–18.
  • Bonelli S, Brivois O, Borghi R, Benahmed N. 2006. On the modelling of piping erosion. Comptes Rendus Mécanique. 334(8–9):555–559.
  • Botschek J, Krause S, Abel T, Skowronek A. 2002. Hydrological parameterization of piping in loess-rich soils in the Bergisches Land, Nordrhein-Westfalen, Germany. Zeitschrift Für Pflanzenernährung Und Bodenkunde. 165(4):506–510.
  • Bou-Hamad I, Anouze AL, Larocque D. 2017. An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment. Ann Oper Res. 253(1):77–95.
  • Boucher SC. 1990. Field tunnel erosion, its characteristics and amelioration. Victoria, Australia: Dept. of Conservation and Environment, Land Protection Division.
  • Breiman L. 1998. Arcing classifier (with discussion and a rejoinder by the author). Ann Stat. 26(3):801–849.
  • Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. Monterey, CA: Wadsworth and Brooks/Cole.
  • Buchen T, Wohlrabe K. 2011. Forecasting with many predictors: is boosting a viable alternative? Econ Lett. 113(1):16–18.
  • Bui Q-T, Nguyen Q-H, Nguyen XL, Pham VD, Nguyen HD, Pham V-M. 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol. 581:124379.
  • Cama M, Conoscenti C, Lombardo L, Rotigliano E. 2016. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy). Environ Earth Sci. 75(3):238.
  • Carey SK, Woo M-K. 2000. The role of soil pipes as a slope runoff mechanism, Subarctic Yukon, Canada. J Hydrol. 233(1–4):206–222.
  • Cerdà A. 1999. Seasonal and spatial variations in infiltration rates in badland surfaces under Mediterranean climatic conditions. Water Resour Res. 35(1):319–328.
  • Cerdà A, García-Fayos P. 1997. The influence of slope angle on sediment, water and seed losses on badland landscapes. Geomorphology. 18(2):77–90.
  • Chen SH, Su HB, Tian J, Zhang RH, Xia J. 2011. Estimating soil erosion using MODIS and TM images based on support vector machine and à trous wavelet. Int J Appl Earth Obs Geoinform. 13(4):626–635.
  • Choubin B, Rahmati O, Tahmasebipour N, Feizizadeh B, Pourghasemi HR. 2019. Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. In: Nat Hazards GIS-Based Spat Model Using Data Min Tech. Berlin: Springer; p. 105–125.
  • Chung Y-S. 2013. Factor complexity of crash occurrence: an empirical demonstration using boosted regression trees. Accid Anal Prev. 61:107–118.
  • Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M. 2014. Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology. 204:399–411.
  • Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gómez-Gutiérrez Á, Rotigliano E, Agnesi V. 2015. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology. 242:49–64.
  • Cortes C, Mohri M, Syed U. 2014. Deep boosting.
  • Romero Díaz A, Marín Sanleandro P, Sánchez Soriano A, Belmonte Serrato F, Faulkner H. 2007. The causes of piping in a set of abandoned agricultural terraces in southeast Spain. Catena. 69(3):282–293.
  • Dao DV, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Phong TV, Ly H-B, Le T-T, Trinh PT, et al. 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena. 188:104451.
  • Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Kløve B. 2019. Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol. 569:142–154.
  • Debanshi S, Pal S. 2020. Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India. Environ Dev Sustain. 22(2):883–914.
  • Deng QC, Zhang B, Luo J, Shu CQ, Qin FC, Luo ML, Lin YB. 2014. Types and controlling factors of piping landform in Yuanmou dry-hot valley. J Arid L Resour Environ. 28(8):138–144.
  • Dodangeh E, Choubin B, Eigdir AN, Nabipour N, Panahi M, Shamshirband S, Mosavi A. 2020. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Tot Environ. 705:135983.
  • Douglas I, Bidin K, Balamurugan G, Chappell NA, Walsh RPD, Greer T, Sinun W. 1999. The role of extreme events in the impacts of selective tropical forestry on erosion during harvesting and recovery phases at Danum Valley, Sabah. In: Chang Disturb Trop Rainfor South-East Asia. Singapore: World Scientific; p. 25–37.
  • Du G, Zhang Y, Iqbal J, Yang Z, Yao X. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province. J Mt Sci. 14(2):249–268.
  • El-Swaify SA, Dangler EW, Armstrong CL. 1982. Soil erosion by water in the tropics.
  • Elith J, Leathwick JR, Hastie T. 2008. A working guide to boosted regression trees. J Anim Ecol. 77(4):802–813.
  • Esmali Ouri A, Golshan M, Janizadeh S, Cerdà A, Melesse AM. 2020. Soil erosion susceptibility mapping in Kozetopraghi catchment, Iran: a mixed approach using rainfall simulator and data mining techniques. Land. 9(10):368.
  • Farifteh J, Soeters R. 1999. Factors underlying piping in the Basilicata region, southern Italy. Geomorphology. 26(4):239–251.
  • Faulkner H. 2013. Badlands in marl lithologies: a field guide to soil dispersion, subsurface erosion and piping-origin gullies. Catena. 106:42–53.
  • Fell R, Wan CF, Cyganiewicz J, Foster M. 2003. Time for development of internal erosion and piping in embankment dams. J Geotech Geoenviron Eng. 129(4):307–314.
  • Flanagan DC, Gilley JE, Franti TG. 2007. Water erosion prediction project (WEPP): development history, model capabilities, and future enhancements. Trans ASABE. 50(5):1603–1612.
  • Fox GA, Wilson GV. 2010. The role of subsurface flow in hillslope and stream bank erosion: a review. Soil Sci Soc Am J. 74(3):717–733.
  • Fox GA, Wilson GV, Simon A, Langendoen EJ, Akay O, Fuchs JW. 2007. Measuring streambank erosion due to ground water seepage: correlation to bank pore water pressure, precipitation and stream stage. Earth Surf Process Landforms. 32(10):1558–1573.
  • Frattini P, Crosta G, Carrara A. 2010. Techniques for evaluating the performance of landslide susceptibility models. Eng Geol. 111(1–4):62–72.
  • Freund RM, Grigas P, Mazumder R, o. 2017. A new perspective on boosting in linear regression via subgradient optimization and relatives. Ann Statist. 45(6):2328–2364.
  • Freund Y, Schapire RE, et al. 1996. Experiments with a new boosting algorithm. In: International Conference on Machine Learning (ICML). Vol. 96; p. 148–156.
  • Friedman J, Hastie T, Tibshirani R, et al. 2000. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Statist. 28(2):337–407.
  • Friedman JH. 2001. Greedy function approximation: a gradient boosting machine. Ann Statist. 29(5):1189–1232.
  • Fukuda S, De Baets B, Waegeman W, Verwaeren J, Mouton AM. 2013. Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ Model Softw. 47:1–6.
  • García-Ruiz JM, Lasanta T, Alberto F. 1997. Soil erosion by piping in irrigated fields. Geomorphology. 20(3–4):269–278.
  • Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S. 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Tot Environ. 668:124–138.
  • George CM, Anu VV. 2018. Predicting piping erosion susceptibility by statistical and artificial intelligence approaches: a review. Int Res J Eng Technol. 5:239–243.
  • Ghasemain B, Asl DT, Pham BT, Avand M, Nguyen HD, Janizadeh S. 2020. Shallow landslide susceptibility mapping: a comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam J Earth Sci. 3(2020):208–227.
  • Gholami V, Booij MJ, Tehrani EN, Hadian MA. 2018. Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena. 163:210–218.
  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2):196.
  • Gu H, Wang J, Ma L, Shang Z, Zhang Q. 2019. Insights into the BRT (Boosted Regression Trees) method in the study of the climate–growth relationship of Masson pine in subtropical China. Forests. 10(3):228.
  • Gutierrez M, Sancho C, Benito G, Sirvent J, Desir G. 1997. Quantitative study of piping processes in badland areas of the Ebro Basin, NE Spain. Geomorphology. 20(3–4):237–253.
  • Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL, et al. 1998. Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
  • Hao M, Wang Y, Bryant SH. 2014. An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data. Anal Chim Acta. 806:117–127.
  • Hembram TK, Paul GC, Saha S. 2020. Modelling of gully erosion risk using new ensemble of conditional probability and index of entropy in Jainti River basin of Chotanagpur Plateau Fringe Area, India. Appl Geomat. 12(3):337–324.
  • Holden J, Burt TP. 2002. Piping and pipeflow in a deep peat catchment. Catena. 48(3):163–199.
  • Hong H, Pradhan B, Bui DT, Xu C, Youssef AM, Chen W. 2017. Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomat Nat Hazards Risk. 8(2):544–569.
  • Hongliang J. 2004. Studies on incremental learning sequence algorithm of Naive Bayesian classifier. Comput Eng Appl. 14: 57–59.
  • Hosseinalizadeh M, Kariminejad N, Alinejad M. 2018. An application of different summary statistics for modelling piping collapses and gully headcuts to evaluate their geomorphological interactions in Golestan Province, Iran. Catena. 171:613–621.
  • Hosseinalizadeh M, Kariminejad N, Chen W, Pourghasemi HR, Alinejad M, Behbahani AM, Tiefenbacher JP. 2019. Gully headcut susceptibility modeling using functional trees, na{\"\i}ve Bayes tree, and random forest models. Geoderma. 342:1–11.
  • Hosseinalizadeh M, Kariminejad N, Chen W, Pourghasemi HR, Alinejad M, Mohammadian Behbahani A, Tiefenbacher JP. 2019. Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree). Geomorphology. 329:184–193.
  • Hosseinalizadeh M, Kariminejad N, Rahmati O, Keesstra S, Alinejad M, Behbahani AM. 2019. How can statistical and artificial intelligence approaches predict piping erosion susceptibility? Sci Tot Environ. 646:1554–1566.
  • Hosseini FS, Choubin B, Mosavi A, Nabipour N, Shamshirband S, Darabi H, Haghighi AT. 2020. Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method. Sci Tot Environ. 711:135161.
  • Javidan N, Kavian A, Pourghasemi HR, Conoscenti C, Jafarian Z. 2019. Gully erosion susceptibility mapping using multivariate adaptive regression splines—replications and sample size scenarios. Water. 11(11):2319.
  • Jenks GF. 1967. The data model concept in statistical mapping. Int Yearb Cartogr. 7:186–190.
  • Jones JAA. 1981. The nature of soil piping: a review of research. BGRG Res Monogr.
  • Jones JAA, Cottrell CI. 2007. Long-term changes in stream bank soil pipes and the effects of afforestation. J Geophys Res Earth Surf. 112:1–11.
  • Jones JAA, Richardson JM, Jacob HJ. 1997. Factors controlling the distribution of piping in Britain: a reconnaissance. Geomorphology. 20(3–4):289–306.
  • Kachouri S, Achour H, Abida H, Bouaziz S. 2015. Soil erosion hazard mapping using Analytic Hierarchy Process and logistic regression: a case study of Haffouz watershed, central Tunisia. Arab J Geosci. 8(6):4257–4268.
  • Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S. 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics Nat Hazards Risk. 9(1):49–69.
  • Kalantar B, Ueda N, Saeidi V, Ahmadi K, Halin AA, Shabani F. 2020. Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sens. 12(11):1737.
  • Kariminejad N, Hosseinalizadeh M, Pourghasemi HR, Bernatek-Jakiel A, Alinejad M. 2019. GIS-based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi-arid environment: Golestan Province, NE Iran. Land Degrad Dev. 30(18):2211–2225.
  • 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. Sci Tot Environ. 677:281–298.
  • Kim J-C, Lee S, Jung H-S, Lee S. 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int. 33(9):1000–1015.
  • Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S. 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics Nat Hazards Risk. 8(2):1185–1203.
  • Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A. 2020. GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens. 12(15):2478.
  • Li Y-F, Xie M, Goh T-N. 2010. Adaptive ridge regression system for software cost estimating on multi-collinear datasets. J Syst Softw. 83(11):2332–2343.
  • Lin W, Wu Y, Mao D. 2007. Region assessment of soil erosion based on naive bayes. In: 2007 International Conference on Computational Intelligence and Security (CIS 2007). p. 6–9.
  • Löffler E. 1974. Piping and pseudokarst features in the tropical lowlands of New Guinea (Röhrenerosion und Pseudokarsterscheinungen im tropischen Tiefland von Neuguinea). 28(1):13–18.
  • 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). Nat Hazards. 79(3):1621–1648.
  • Lucà F, Conforti M, Robustelli G. 2011. Geomorphology comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology. 134(3–4):297–308.
  • Mao D, Zeng Z, Wang C, Lin W. 2007. Support vector machines with PSO algorithm for soil erosion evaluation and prediction. In: Third Int Conf Nat Comput (ICNC 2007). Vol 1: 656–660.
  • Masannat YM. 1980. Development of piping erosion conditions in the Benson area, Arizona, USA. Q J Eng Geol Hydrogeol. 13(1):53–61.
  • Masoodi A, Majdzadeh Tabatabai MR, Noorzad A, Samadi A. 2017. Effects of soil physico-chemical properties on stream bank erosion induced by seepage in northeastern Iran. Hydrol Sci J. 62(16):2597–2613.
  • Massanat YM. 1972. Evaluation of factors contributing to piping erosion near Benson, Cochise County, Arizona.
  • McCulloch CE, Neuhaus JM. 2011. Prediction of random effects in linear and generalized linear models under model misspecification. Biometrics. 67(1):270–279.
  • McMaster R. 1997. In Memoriam: George F. Jenks (1916–1996). Cartogr Geogr Inf Syst. 24(1):56–59.
  • Midi H, Sarkar SK, Rana S. 2010. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 13(3):253–267.
  • Milchevski A, Rozza A, Taskovski D. 2015. Multimodal affective analysis combining regularized linear regression and boosted regression trees. In: Proc 5th Int Work Audio/Visual Emot Chall.; p. 33–39.
  • Moradi HR, Avand MT, Janizadeh S. 2019. Landslide susceptibility survey using modeling methods. Amsterdam: Elsevier; p. 259–276.
  • Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME. 1998. The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf Process Landforms. 23(6):527–544.
  • Mosavi A, Golshan M, Janizadeh S, Choubin B, Melesse AM, Dineva AA. 2020. Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto Int. 35:1–20.
  • Nadal-Romero E, Verachtert E, Maes R, Poesen J. 2011. Quantitative assessment of the piping erosion susceptibility of loess-derived soil horizons using the pinhole test. Geomorphology. 135(1–2):66–79.
  • Naghibi SA, Pourghasemi HR, Dixon B. 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess. 188(1):44.
  • Nhu V-H, Janizadeh S, Avand M, Chen W, Farzin M, Omidvar E, Shirzadi A, Shahabi H, J. Clague J, Jaafari A, et al. 2020. GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models. Appl Sci. 10(6):2039.
  • Ojha CSP, Singh VP, Adrian DD. 2003. Determination of critical head in soil piping. J Hydraul Eng. 129(7):511–518.
  • Pal SC, Chakrabortty R. 2019. Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Adv Sp Res. 64(2):352–377.
  • Parker AP. 2005. Assessment and extension of an analytical formulation for prediction of residual stress in autofrettaged thick cylinders. In: ASME 2005 Press Vessel Pip Conf. p. 67–71.
  • Parker GG. 1964. Piping: a geomorphic agent in landform development of the drylands. International Association of Scientific Hydrology. 65:103–113.
  • Parzen M, Ghosh S, Lipsitz S, Sinha D, Fitzmaurice GM, Mallick BK, Ibrahim JG. 2011. A generalized linear mixed model for longitudinal binary data with a marginal logit link function. Ann Appl Stat. 5(1):449–467.
  • Paul SS, Li J, Li Y, Shen L. 2019. Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm. Geocarto Int. 34:1–15.
  • Peng Z, Lin L, Zhang R, Xu J. 2014. Deep boosting: layered feature mining for general image classification. In: 2014 IEEE Int Conf Multimed Expo. 1–6.
  • Pereyra MA, Fernández DS, Marcial ER, Puchulu ME. 2020. Agricultural land degradation by piping erosion in Chaco Plain, Northwestern Argentina. Catena. 185:104295.
  • Pham QB, Mukherjee K, Norouzi A, Linh NTT, Janizadeh S, Ahmadi K, Cerdà A, Doan TNC, Anh DT. 2020. Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran. Geomat Nat Hazards Risk. 11(1):2385–2410.
  • Phinzi K, Ngetar NS, Ebhuoma O. 2020. Soil erosion risk assessment in the Umzintlava catchment (T32E), Eastern Cape, South Africa, using RUSLE and random forest algorithm. S Afr Geogr J. 102:1–24.
  • Pike AC, Mueller TG, Schörgendorfer A, Shearer SA, Karathanasis AD. 2009. Erosion index derived from terrain attributes using logistic regression and neural networks. Agron J. 101(5):1068–1079.
  • Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B. 2017. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology. 298:118–137.
  • Renard KG. 1997. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing. United States Department of Agriculture.
  • Rotunno AF, Callari C, Froiio F. 2017. Computational modeling of backward erosion piping. In: Model simulation, Exp issues Struct Mech. Berlin: Springer; p. 225–234.
  • Roy J, Saha S. 2019. GIS-based gully erosion susceptibility evaluation using frequency ratio, cosine amplitude and logistic regression ensembled with fuzzy logic in Hinglo River Basin, India. Remote Sens Appl Soc Environ. 15:100247.
  • 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 Degrad Dev. 29(9):3092–3103.
  • Samadi M, Jabbari E, Azamathulla HM, Mojallal M. 2015. Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks. Eng Appl Comput Fluid Mech. 9(1):291–300.
  • Samani AN, Ahmadi H, Jafari M, Boggs G, Ghoddousi J, Malekian A. 2009. Geomorphic threshold conditions for gully erosion in Southwestern Iran (Boushehr–Samal watershed). J Asian Earth Sci. 35(2):180–189.
  • Sarkar T, Mishra M. 2018. Soil erosion susceptibility mapping with the application of logistic regression and artificial neural network. J Geovisualization Spat Anal. 2(1):8.
  • Schweckendiek T, Kanning W, Jonkman SN. 2014. Advances in reliability analysis of the piping failure mechanism of flood defences in the Netherlands. Heron. 59(2/3):101.
  • Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T. 2016. Fuzzy Shannon entropy: a hybrid GIS-based landslide susceptibility mapping method. Entropy. 18(10):343.
  • Shahabi H, Hashim M. 2015. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep. 5(1):9899–9815.
  • Sirvent J, Desir G, Gutierrez M, Sancho C, Benito G. 1997. Erosion rates in badland areas recorded by collectors, erosion pins and profilometer techniques (Ebro Basin, NE-Spain). Geomorphology. 18(2):61–75.
  • Soufi M, Bayat R, Davudirad A, Zanjanijam M, Esaei H. 2020. Topographic threshold of gully erosion in Iran: a case study of Fars, Zanjan, Markazi and Golestan Provinces. In: Gully Eros Stud from India Surround Reg. Berlin: Springer. p. 381–392.
  • Sudakov O, Burnaev E, Koroteev D. 2019. Driving digital rock towards machine learning: predicting permeability with gradient boosting and deep neural networks. Comput Geosci. 127:91–98.
  • Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science. 240(4857):1285–1293.
  • Therneau T, Atkinson B, Ripley B. 2015. rpart: recursive partitioning and regression trees. R Packag Version. 4:1–9.
  • Tutz G, Groll A. 2010. Generalized linear mixed models based on boosting. In: Stat Model Regres Struct. Berlin: Springer; p. 197–215.
  • Vandenboer K, Celette F, Bezuijen A. 2019. The effect of sudden critical and supercritical hydraulic loads on backward erosion piping: small-scale experiments. Acta Geotech. 14(3):783–794.
  • Verachtert E, Van Den Eeckhaut M, Martínez-Murillo JF, Nadal-Romero E, Poesen J, Devoldere S, Wijnants N, Deckers J. 2013. Impact of soil characteristics and land use on pipe erosion in a temperate humid climate: field studies in Belgium. Geomorphology. 192:1–14.
  • Verachtert E, Van Den Eeckhaut M, Poesen J, Deckers J. 2010. Factors controlling the spatial distribution of soil piping erosion on loess-derived soils: a case study from central Belgium. Geomorphology. 118(3–4):339–348.
  • Verachtert E, Maetens W, Van Den Eeckhaut M, Poesen J, Deckers J. 2011. Soil loss rates due to piping erosion. Earth Surf Process Landforms. 36(13):1715–1725.
  • Wang J, Zou B, Liu Y, Tang Y, Zhang X, Yang P. 2014. Erosion–creep–collapse mechanism of underground soil loss for the karst rocky desertification in Chenqi village, Puding county, Guizhou, China. Environ Earth Sci. 72(8):2751–2764.
  • Wilson GV, Rigby JR, Dabney SM. 2015. Soil pipe collapses in a loess pasture of Goodwin Creek watershed, Mississippi: role of soil properties and past land use. Earth Surf Process Landforms. 40(11):1448–1463.
  • Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses: a guide to conservation planning. Maryland: Department of Agriculture, Science and Education Administration.
  • Yesilnacar E, Topal T. 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol. 79(3–4):251–266.
  • Yesilnacar EK. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey. Melbourne, Australia: University of Melbourne, Department. p. 200.

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