Gully erosion mapping based on hydro-geomorphometric factors and geographic information system.
Frequency ratio
Geographically weighted regression
Gully erosion
Logistic regression
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
25 May 2023
25 May 2023
Historique:
received:
13
06
2022
accepted:
01
04
2023
medline:
26
5
2023
pubmed:
25
5
2023
entrez:
24
5
2023
Statut:
epublish
Résumé
Delineation of areas susceptible to gully erosion with high accuracy and low cost using significant factors and statistical model is essential. In the present study, a gully susceptibility erosion map (GEM) was developed using hydro-geomorphometric parameters and geographic information system in western Iran. For this aim, a geographically weighted regression (GWR) model was applied, and its results compared to frequency ratio (FreqR) and logistic regression (LogR) models. Almost twenty effective parameters on gully erosion were detected and mapped in the ArcGIS
Identifiants
pubmed: 37226003
doi: 10.1007/s10661-023-11197-7
pii: 10.1007/s10661-023-11197-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
721Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Akgün, A., & Türk, N. (2011). Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region, NW Turkey. Computers and geosciences, 37, 1515–1524.
doi: 10.1016/j.cageo.2010.09.006
Alencar, P. H. L., Simplício, A. A. F., & de Araújo, J. C. (2022). Entropy-based Model for Gully Erosion – A combination of probabilistic and deterministic components. Science of The Total Environment, 836, 155629.
doi: 10.1016/j.scitotenv.2022.155629
Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. https://doi.org/10.1016/j.catena.2013.10.011
doi: 10.1016/j.catena.2013.10.011
Amiri, M., & Pourghasemi, H. R. (2020). Mapping and preparing a susceptibility map of gully erosion using the MARS Model. In P. Shit, H. Pourghasemi, & G. Bhunia (Eds.), Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology and Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_27
Amiri, M., Pourghasemi, H. R., Ghanbarian, G. A., & Afzali, S. F. (2019). Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma, 340, 55–69. https://doi.org/10.1016/j.geoderma.2018.12.042
doi: 10.1016/j.geoderma.2018.12.042
Amundsen, R., Harden, J. W., & Singer, M. J. (1994). Factors of soil formation: A fiftieth anniversary retrospective. The Soil Science Society of America, Inc.
Arabameri, A., Yamani, M., Pradhan, B., Melesse, A., Shirani, K., & Tien Bui, D. (2019). Novel ensembles of COPRAS multi-criteria decision-making with logistic regression boosted regression tree and random forest for spatial prediction of gully erosion susceptibility. Science of the Total Environment, 688, 903–916.
doi: 10.1016/j.scitotenv.2019.06.205
Asteriou, D., & Hall, S. G. (2016). ARIMA models and the Box-Jenkins methodology. Applied Econometrics, 275–296. https://doi.org/10.1057/978-1-13741547-9_13
Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan. Geomorphology, 65(1–2), 15–31.
doi: 10.1016/j.geomorph.2004.06.010
Azareh, A., Rahmati, O., Rafiei-Sardooi, E., Sankey, J. B., Lee, S., Shahabi, H., & Bin Ahmad, B. (2019). Modeling 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. https://doi.org/10.1016/j.scitotenv.2018.11.235
doi: 10.1016/j.scitotenv.2018.11.235
Boehner, J., & Selige, T. (2006). Spatial prediction of soil attributes using terrain analysis and climate regionalisation. In J. Boehner, K. R. McCloy, & J. Strobl (Eds.), 'SAGA - Analysis and Modelling Applications' (Vol. 115, pp. 13–27). Goettinger Geographische Abhandlungen.
Bonham-Carter, G. F. (1994). Geographic information system for geoscientists: Modelling with GIS (p. 398). Pergamon Press, 585 Oxford.
Bouramtane, T., Hilal, H., Rezende-Filho, A. T., Bouramtane, K., Barbiero, L., Abraham, S., Valles, V., Kacimi, I., Sanhaji, H., Torres-Rondon, L., Dantas de Castro, D., Santos, J. C. V., Quardi, J., Beqqali, E. L., & O., Kassou, N. & Morarech, M. (2022). Mapping gully erosion variability and susceptibility using remote sensing, multivariate statistical analysis, and machine learning in South Mato Grosso. Brazil. Geosciences, 12, 235. https://doi.org/10.3390/geosciences12060235
doi: 10.3390/geosciences12060235
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28, 281–298.
doi: 10.1111/j.1538-4632.1996.tb00936.x
Capra, A., Di Stefano, C., Ferro, V., & Scicolone, B. (2009). Similarity between morphological characteristics of rills and ephemeral gullies in Sicily, Italy. Hydrological Processes, 3341, 3334–3341.
doi: 10.1002/hyp.7437
Casal, J., López, J., & Giráldez, J. (1999). Ephemeral gully erosion in southern Navarra (Spain). Catena, 36, 65–84.
doi: 10.1016/S0341-8162(99)00013-2
Cevik, E., & Topal, T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental geology, 44, 949–962.
doi: 10.1007/s00254-003-0838-6
Chakrabortty, R., Pal, S. C., Chowdhuri, I., Malik, S., & Das, B. (2020). Assessing the importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and novel ensemble model in a sub-tropical environment. The Indian Society of Remote Sensing, 48, 765–789. https://doi.org/10.1007/s12524-020-01110-x
Chaplot, V. (2013). Impact of terrain attributes, parent material, and soil types on gully erosion. Geomorphology, 186, 1–11.
doi: 10.1016/j.geomorph.2012.10.031
Chen, J., Zhang, J., Peng, J., Zou, L., Fan, Y., Yang, F., & Hu, Z. (2023). Alp-valley and elevation effects on the reference evapotranspiration and the dominant climate controls in Red River Basin, China: Insights from geographical differentiation. Journal of Hydrology, 620(Part A), 129397. https://doi.org/10.1016/j.jhydrol.2023.129397 .
Choubin, B., Rahmati, O., Tahmasebipour, N., Feizizadeh, B., & Pourghasemi, H. R. (2019). Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques (pp. 105–125). Springer.
doi: 10.1007/978-3-319-73383-8_5
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, 881–898.
doi: 10.1007/s11069-010-9598-2
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.
doi: 10.1016/j.geomorph.2013.08.021
Craney, T. A., & Surles, J. G. (2002). Model-dependent variance inflation factor cutoff values[J]. Quality Engineering, 2002(14), 391–403.
doi: 10.1081/QEN-120001878
Dahal, B., & Dahal, R. (2017). Landslide hazard map: Tool for optimization of low-cost mitigation. Geoenvironmental Disasters, 4(1), 8. https://doi.org/10.1186/s40677-017-0071-3
doi: 10.1186/s40677-017-0071-3
Dai, F., Lee, C., Li, J., & Xu, Z. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40, 381–391.
doi: 10.1007/s002540000163
Dewitte, O., Daoudi, M., Bosco, C., & Van Den Eeckhaut, M. (2015). Predicting the susceptibility to gully initiation in data-poor regions. Geomorphology, 228, 101–115.
doi: 10.1016/j.geomorph.2014.08.010
Ding, L., Qin, F., Fang, H., Liu, H., Zhang, B., Shu, C., Deng, Q., Liu, G., & Yang, Q. (2017). Morphology and controlling factors of the longitudinal profile of gullies in the Yuanmou dry-hot valley. Mountain Science, 14(4), 674–693.
doi: 10.1007/s11629-016-4189-7
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., Marquez, J. R. G., Gruber, B., Lafourcade, B., Leitao, P. J., Munkemuller, T., McClean, C., Osborne, P. E., Reineking, B., Schroder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 027–046.
doi: 10.1111/j.1600-0587.2012.07348.x
Dube, F., Nhapi, I., Murwira, A., Gumindoga, W., Goldin, J., & Mashauri, D. (2014). Potential of the weight of evidence modeling for gully erosion hazard assessment in Mbire District-Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 67, 145–152.
doi: 10.1016/j.pce.2014.02.002
FAO & ITPS. (2015). Status of the world’s soil resources (SWSR) – main report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships/CA. Stewart Fotherington, Chris Brunsdon, and Martin Charlton. Wiley.
Gayen, A., Haque, S. M., & Saha, S. (2020). Modeling of gully erosion based on random forest using GIS and R. In P. Shit, H. Pourghasemi, & G. Bhunia (Eds.), Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology and Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_3
Gayen, A., Pourghasemi, H. R., Saha, S., Keesstra, S., & Bai, S. (2019). Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of the Total Environment, 668, 124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436
doi: 10.1016/j.scitotenv.2019.02.436
Geological Survey and Mineral Exploration of Iran (GSI). (1997). Map of the geological quadrangle of Ilam in scale of 100,000th. https://gsi.ir/en/map
Gunz, M., & Vanacker, V. (2012). Logistic regression applied to natural hazards: Rare event logistic regression with replications. Natural Hazards and Earth System Sciences, 12, 1937–1947.
doi: 10.5194/nhess-12-1937-2012
Gutiérrez, Á. G., Schnabel, S., & Contador, J. F. L. (2009). Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological modeling, 220, 3630–3637.
doi: 10.1016/j.ecolmodel.2009.06.020
Hembram, T. K., Paul, G. C., & Saha, S. (2020). Modeling of gully erosion risk using new ensemble of conditional probability and index of entropy in Jainti River basin of Chotanagpur Plateau Fringe Area, India. Applied Geomatic, 12, 337–360. https://doi.org/10.1007/s12518-020-00301-y
doi: 10.1007/s12518-020-00301-y
Hembram, T.k., Paul, G.C. & Saha, S. (2019). Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: A comparison of information value and logistic regression models. Modeling Earth Systems Environment, 5, 689–708. https://doi.org/10.1007/s40808-018-0560-8
doi: 10.1007/s40808-018-0560-8
Hengl, T., & Reuter, H. I. (Eds.). (2008). Geomorphometry: Concepts, software, applications. Developments in Soil Science (Vol. 33, p. 772). Elsevier.
Hong, H., Naghibi, S. A., Dashtpagerdi, M. M., Pourghasemi, H. R., & Chen, W. (2017). A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences, 10, 167.
doi: 10.1007/s12517-017-2905-4
Hook, P. B., & Burke, I. C. (2000). Biogeochemistry in a shortgrass landscape: Control by topography, soil texture, and microclimate. Ecology, 81, 2686–2703.
doi: 10.1890/0012-9658(2000)081[2686:BIASLC]2.0.CO;2
Hosseinalizadeh, M., Kariminejad, N., Chen, W., Pourghasemi, H. R., Alinejad, M., Mohammadian, A., Behbahani, M., & Tiefenbacher, J. P. (2019). Spatial modeling of gully head cuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree). Geomorphology, 329, 184–193. https://doi.org/10.1016/j.geomorph.2019.01.006
doi: 10.1016/j.geomorph.2019.01.006
Hosseinalizadeh, M., Kariminejad, N., Chen, W., Pourghasemi, H. R., Alinejad, M., Mohammadian Behbahani, A., & Tiefenbacher, J. P. (2019). Gully’s head cut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models. Geoderma, 342, 1–11. https://doi.org/10.1016/j.geoderma.2019.01.050
doi: 10.1016/j.geoderma.2019.01.050
Hosseinalizadeh, M., Kariminejad, N., Rahmati, O., Keesstra, S., Alinejad, M., & Behbahani, A. M. (2019). How can statistical and artificial intelligence approaches predict piping erosion susceptibility? Science of the Total Environment, 646, 1554–1566.
doi: 10.1016/j.scitotenv.2018.07.396
Javidan, N., Kavian, A., Pourghasemi, H. R., Conoscenti, C., & Jafarian, Z. (2020). Data mining technique (maximum entropy model) for mapping gully erosion susceptibility in the Gorganrood watershed, Iran. In P. Shit, H. Pourghasemi, & G. Bhunia (Eds.), Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology and Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-23243-629
Jenny, H. (1941). Factors of soil formation: A system of quantitative pedology (Kindle, p. 320). McGraw-Hill.
Kuhnert, P. M., Henderson, A. K., Bartley, R., & Herr, A. (2010). Incorporating uncertainty in gully erosion calculations using the random forests modeling approach. Environmetrics, 21, 493–509.
Kutner, M. H., Nachtsheim, C., & Neter, J. (2004). Applied linear regression models. McGraw-Hill/Irwin.
Lee, M.-J., Kang, J.-E., & Jeon, S. (2012). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In Geoscience and Remote Sensing Symposium (IGARSS) (pp. 895-898). 2012 IEEE International. IEEE.
Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50, 847–855. https://doi.org/10.1007/s00254-006-0256-7
doi: 10.1007/s00254-006-0256-7
Li, Y., Duan, X., Li, Y., Li, Y., & Zhang, L. (2021). Interactive effects of land use and soil erosion on soil organic carbon in the dry-hot valley region of southern China. Catena, 201, 105187. https://doi.org/10.1016/j.catena.2021.105187
Lombardo, L., & Mai, P. M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244, 14–24.
doi: 10.1016/j.enggeo.2018.07.019
Luca, F., Conforti, M., & Robustelli, G. (2011). Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria. South Italy. Geomorphology, 134(3–4), 297–308.
doi: 10.1016/j.geomorph.2011.07.006
Maerker, M., Quénéhervé, G., Bachofer, F., & Mori, S. (2015). A simple DEM assessment procedure for gully system analysis in the Lake Manyara area, northern Tanzania. Natural Hazards, 79(1), 235–253.
doi: 10.1007/s11069-015-1855-y
Marco, K. (2006). A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology, 71(1–4), 17–28.
Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12, 591–612.
doi: 10.2307/1267205
Meten, M., Bhandary, N. P., & Yatabe, R. (2015). GIS-based frequency ratio and logistic regression modeling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. Mountain Science, 12, 1355–1372.
doi: 10.1007/s11629-015-3464-3
Moore, I., & Burch, G. (1986). Sediment transport capacity of sheet and rill flow: Application of unit stream power theory. Water Resources Research, 22, 1350–1360.
doi: 10.1029/WR022i008p01350
Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.
doi: 10.1002/hyp.3360050103
O’ Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity., 41(5), 673–690.
doi: 10.1007/s11135-006-9018-6
Pal, S. C., Arabameri, A., Blaschke, T., Chowdhuri, I., Saha, A., Chakrabortty, R., Lee, S., & Band, S. S. (2020). Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sensing, 12, 3675. https://doi.org/10.3390/rs12223675
Poesen, J., Nachtergaele, J., Verstraeten, G., & Valentin, C. (2003). Gully erosion and environmental change: importance and research needs. Catena, 50, 91–133.
doi: 10.1016/S0341-8162(02)00143-1
Pourghasemi, H. R., Gayen, A., Haque, S. M., & Bai, S. (2020a). Gully erosion susceptibility assessment through the SVM machine learning algorithm (SVM-MLA). In P. Shit, H. Pourghasemi, & G. Bhunia (Eds.), Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology and Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_28
Pourghasemi, H. R., Moradi, H., & Aghda, S. F. (2013). Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural hazards, 69, 749–779.
doi: 10.1007/s11069-013-0728-5
Pourghasemi, H. R., Sadhasivam, N., Kariminejad, N., & Collins, A. L. (2020b). Gully erosion spatial modeling: Role of machine learning algorithms in selection of the best controlling factors and modeling process. Geoscience Frontiers, 11(6), 2207–2219. https://doi.org/10.1016/j.gsf.2020.03.005
Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60, 1037–1054.
doi: 10.1007/s12665-009-0245-8
Pradhan, B., & Youssef, A. M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geosciences, 3, 319–326.
doi: 10.1007/s12517-009-0089-2
Quinn, P. F., Beven, K. J., & Lamb, R. (1995). The ln (a/tan b) index: how to calculate it and how to use it within in the TOPMODEL framework. Hydrological Processes, 9, 161–182.
doi: 10.1002/hyp.3360090204
Rahmati, O., Haghizadeh, A., Pourghasemi, H. R., & Noormohamadi, F. (2016). Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison. Natural Hazards, 82, 1231–1258.
doi: 10.1007/s11069-016-2239-7
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R., & Feizizadeh, B. (2017). Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298, 118–137.
doi: 10.1016/j.geomorph.2017.09.006
Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS-based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3(1). https://doi.org/10.1186/s40677-016-0053-x
Razavi Termeh, S. V., Sadeghi-Niaraki, A., & Choi, S.-M. (2020). Gully erosion susceptibility mapping using artificial intelligence and statistical models. Geomatics, Natural Hazards and Risk, 11(1), 821–844.
doi: 10.1080/19475705.2020.1753824
Razavi Termeh, S. V., Kornejady, A., Pourghasemi, H. R., & Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro-fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 615, 438–451.
doi: 10.1016/j.scitotenv.2017.09.262
Refahi, H. (2009). Soil erosion by water: Conservation and control (p. 202). Tehran University Press.
Rengers, F., & Tucker, G. E. (2013). Analysis and modeling of gully headcut dynamics, North American high plains. Geophysical Research: Earth Surface, 119(5). https://doi.org/10.1002/2013JF002962
Romer, C., & Ferentinou, M. (2016). Shallow landslide susceptibility assessment in a semiarid environment-A Quaternary catchment of KwaZulu-Natal, South Africa. Engineering Geology, 201, 29–44.
doi: 10.1016/j.enggeo.2015.12.013
Roy, J., & Saha, D. 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 Sensing Applications: Society and Environment, 15, 100247. https://doi.org/10.1016/j.rsase.2019.100247
Rusco, E., Montanarella, L., & Bosco, C. (2008). Soil erosion: A main threats to the soils in Europe. In G. Tóth, L. Montanarella, & E. Rusco, (Eds.), Threats to Soil Quality in Europe (pp. 37–45). Luxembourg: European Commission Joint Research Centre.
Saha, S. (2017). Groundwater potential mapping using hierarchical analytical process: A study on Md. Bazar block of Birbhum District, West Bengal. Spatial Information Research, 25(4), 615–626.
Saha, A., Pal S. C., Arabameri, A., Chowdhuri, I., Rezaie, F., Chakrabortty, R., Roy, P., & Shit, M. (2021). Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. Environmental Management, 287, 112284. https://doi.org/10.1016/j.jenvman.2021.112284
Seibert, J., Stendahl, J., & Sorensen, R. (2007). Topographical influences on soil properties in boreal forests. Geoderma, 141(1–2), 139–148. https://doi.org/10.1016/j.geoderma.2007.05.013
doi: 10.1016/j.geoderma.2007.05.013
Sela, S., Svoray, T., & Assouline, S. (2012). Soil water content variability at the hillslope scale: Impact of surface sealing. Water Resources Research, 48(3), 1–14.
doi: 10.1029/2011WR011297
Shahabi, H., Hashim, M., & Ahmad, B. B. (2015). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin. Iran. Environmental Earth Sciences, 73, 8647–8668.
doi: 10.1007/s12665-015-4028-0
Shellberg, J. G., Spencer, J., Brooks, A. P., & Pietsch, T. J. (2016). Degradation of the Mitchell River fluvial megafan by alluvial gully erosion increased by post-European land use change, Queensland, Australia. Geomorphology, 266, 105–120.
doi: 10.1016/j.geomorph.2016.04.021
Shirani, K., Pasandi, M., & Arabameri, A. (2018). Landslide susceptibility assessment by Dempster-Shafer and index of entropy models, Sarkhoun basin, Southwestern Iran. Natural Hazards, 93(3), 1379–1418.
doi: 10.1007/s11069-018-3356-2
Shit, P. K., Bhunia, G. S., & Pourghasemi, H. R. (2020). Gully erosion susceptibility mapping based on bayesian weight of evidence. In P. Shit, H. R, Pourghasemi, & G. Bhunia, (Eds.), Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology and Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_8
Tien Bui, D., Pradhan, B., Revhaug, I., Nguyen, D. B., Pham, H. V., & Bui, Q. N. (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 and Risk, 6, 243–271.
doi: 10.1080/19475705.2013.843206
Tien Bui, D., Shirzadi, A., Shahabi, H., Chapi, K., Omidavr, E., Pham, B. T., Talebpour Asl, D., Khaledian, H., Pradhan, B., & Panahi, M. (2019). A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors, 19(11), 24–44.
doi: 10.3390/s19112444
Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13, 361–378. https://doi.org/10.1007/s10346-015-0557-6
doi: 10.1007/s10346-015-0557-6
Torri, D., Poesen, J., Borselli, L., Bryan, R., & Rossi, M. (2012). Spatial variation of bed roughness in eroding rills and gullies. Catena, 90, 76–86.
doi: 10.1016/j.catena.2011.10.004
Tsangaratos, P., & Benardos, A. (2014). Estimating landslide susceptibility through an artificial neural network classifier. Natural hazards, 74, 1489–1516.
doi: 10.1007/s11069-014-1245-x
Valentin, C., Poesen, J., & Li, Y. (2005). Gully erosion: Impacts, factors, and control. Catena, 63, 132–153.
doi: 10.1016/j.catena.2005.06.001
Van Den Eeckhaut, M., Marre, A., & Poesen, J. (2010). Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). Geomorphology, 115, 141–155.
doi: 10.1016/j.geomorph.2009.09.042
Weisberg, S., & Fox, J. (2011). An R companion to applied regression. SAGE Publications Inc.
Wheeler, D. C. (2009). Simultaneous coefficient penalization and model selection in geographically weighted regression: The geographically weighted lasso. Environment and Planning: A Economy and Space, 41, 722–742.
doi: 10.1068/a40256
Wheeler, D. C., & Páez, A. (2010). Geographically weighted regression. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 461–486). Springer.
doi: 10.1007/978-3-642-03647-7_22
Yang, W., Deng, M., Tang, J., & Luo, L. (2022). Geographically weighted regression with the integration of machine learning for spatial prediction. Journal Geographical Systems. https://doi.org/10.1007/s10109-022-00387-5
doi: 10.1007/s10109-022-00387-5
Yujie, W., Zheng, L., Yong, Z., Tingting, C., Zhonglu, G., Chongfa, C. & Zhaoxia, L. (2022). Analysis of gully erosion susceptibility and spatial modelling using a GIS-based approach, Geoderma, 420, 115869. https://doi.org/10.1016/j.geoderma.2022.115869
Zabihi, M., Mirchooli, F., Motevalli, A., Darvishan, A. K., Pourghasemi, H. R., Zakeri, M. A., & Sadighi, F. (2018). Spatial modeling of gully erosion in Mazandaran Province, northern Iran. Catena, 161, 1–13.
doi: 10.1016/j.catena.2017.10.010
Zheng, F. L. (2006). Effect of vegetation changes on soil erosion on the loess plateau1. Pedosphere, 16, 420–427.
doi: 10.1016/S1002-0160(06)60071-4
Zhu, C., & Wang, X. (2009). Landslide susceptibility mapping: A comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology, 2009, 342–346.