Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation

Alin, 2010, Multicollinearity, Wiley Interdiscip. Rev. Comput. Stat., 2, 370, 10.1002/wics.84 Arabameri, 2020, A methodological comparison of head-cut based gully erosion susceptibility models: combined use of statistical and artificial intelligence, Geomorphology, 359, 107136, 10.1016/j.geomorph.2020.107136 Arabameri, 2020, Novel credal decision tree-based ensemble approaches for predicting the landslide susceptibility, Remote Sens., 12, 3389, 10.3390/rs12203389 Band, 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, 5609, 10.3390/s20195609 Battineni, 2019, Comparative machine-learning approach: a follow-up study on type 2 diabetes predictions by cross-validation methods, Machines, 7, 74, 10.3390/machines7040074 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Bryan, 1997, The significance of soil piping processes : inventory and prospect, Geomorphology (Amst.), 20, 209, 10.1016/S0169-555X(97)00024-X Bui, 2020, Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment, Catena, 188, 104426, 10.1016/j.catena.2019.104426 Canziani, 2017 Castillo, 2016, A century of gully erosion research: urgency, complexity and study approaches, Earth Sci. Rev., 160, 300, 10.1016/j.earscirev.2016.07.009 Chakrabortty, 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, J. Indian Soc. Remote Sens., 1 Chakrabortty, 2020, Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India, Nat. Hazards, 10.1007/s11069-020-04213-3 Chang, 2019, Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques, Sci. Rep., 9, 12296, 10.1038/s41598-019-48773-2 Chen, 2020, The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China, Nat. Hazards, 101, 853, 10.1007/s11069-020-03899-9 Choi, 2012, Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS, Eng. Geol., 124, 12, 10.1016/j.enggeo.2011.09.011 Choubin, 2018, River suspended sediment modelling using the CART model: a comparative study of machine learning techniques, Sci. Total Environ., 615, 272, 10.1016/j.scitotenv.2017.09.293 Chowdhuri, 2020, Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment, Remote Sens., 12, 3620, 10.3390/rs12213620 Chowdhuri, 2020, Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India, Adv. Space Res., 65, 1466, 10.1016/j.asr.2019.12.003 Coelho, 2017, A GPU deep learning metaheuristic based model for time series forecasting, Appl. Energy, 201, 412, 10.1016/j.apenergy.2017.01.003 Conforti, 2011, Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy), Nat. Hazards, 56, 881, 10.1007/s11069-010-9598-2 Conoscenti, 2014, Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy), Geomorphology, 204, 399, 10.1016/j.geomorph.2013.08.021 Costache, 2020, Novel ensembles of deep learning neural network and statistical learning for flash-flood susceptibility mapping, Water, 12, 1549, 10.3390/w12061549 Deng, 2015, Characterizing the morphology of gully cross-sections based on PCA: a case of Yuanmou Dry-Hot Valley, Geomorphology, 228, 703, 10.1016/j.geomorph.2014.10.032 Domazetović, 2019, Development of automated multicriteria GIS analysis of gully erosion susceptibility, Appl. Geogr., 112, 102083, 10.1016/j.apgeog.2019.102083 Du, 2019, Multi-modal deep learning for landform recognition, ISPRS J. Photogramm. Remote Sens., 158, 63, 10.1016/j.isprsjprs.2019.09.018 El Maaoui, 2012, Sediment yield from irregularly shaped gullies located on the Fortuna lithologic formation in semi-arid area of Tunisia, CATENA, 93, 97, 10.1016/j.catena.2012.02.004 Florinsky, 2016 Foster, 1986, Understanding ephemeral gully erosion Fox, 2016, Reservoir sedimentation and upstream sediment sources: perspectives and future research needs on streambank and gully erosion, Environ. Manag., 57, 945, 10.1007/s00267-016-0671-9 Garosi, 2018, Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping, Geoderma, 330, 65, 10.1016/j.geoderma.2018.05.027 Garosi, 2019, Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion, Sci. Total Environ., 664, 1117, 10.1016/j.scitotenv.2019.02.093 Gholami, 2020, Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran, Atmos. Pollut. Res., 11, 1303, 10.1016/j.apr.2020.05.009 Girshick, 2015, Fast R-CNN, 1440 Gómez-Gutiérrez, 2015, Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations, Nat. Hazards, 79, 291, 10.1007/s11069-015-1703-0 Goodfellow, 2017 Guzzetti, 2006, Estimating the quality of landslide susceptibility models, Geomorphology, 81, 166, 10.1016/j.geomorph.2006.04.007 Han, 2016, Variable selection using mean decrease accuracy and mean decrease Gini based on random forest, 219 Hengl, 2006, Finding the right pixel size, Comput. Geosci., 32, 1283, 10.1016/j.cageo.2005.11.008 Hosseinalizadeh, 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, 10.1016/j.geomorph.2019.01.006 Hughes, 2001 Kheir, 2007, Use of terrain variables for mapping gully erosion susceptibility in Lebanon, Earth Surf. Process. Landf., 32, 1770, 10.1002/esp.1501 Kim, 2017, Deep learning, 103 Krishnan, 2016, DEM generation using Cartosat-I stereo data and its comparison with publically available DEM, 295 Laflen, 1986, Ephemeral gully erosion Landis, 1977, The measurement of observer agreement for categorical data, Biometrics, 33, 159, 10.2307/2529310 Lee, 2004, Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289, 10.1016/S0013-7952(03)00142-X Legorreta Paulin, 2010, Effect of pixel size on cartographic representation of shallow and deep-seated landslide, and its collateral effects on the forecasting of landslides by SINMAP and multiple logistic regression landslide models, 35, 137 Lewis, 2016 Li, 2020, Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery, Geomorphology, 354, 107045, 10.1016/j.geomorph.2020.107045 Li, 2021, Extracting check dam areas from high-resolution imagery based on the integration of object-based image analysis and deep learning, Land Degrad. Dev., 32, 2303, 10.1002/ldr.3908 Mafizul Haque, 2019, Microstructural evidence of Palaeo-Coastal Landform from Westernmost Fringe of Lower Ganga–Brahmaputra Delta, 61 Mallat, 2016, Understanding deep convolutional networks, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 374, 20150203, 10.1098/rsta.2015.0203 Nayak, 2006, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manag., 20, 77, 10.1007/s11269-006-4007-z Okereke, 2012 Oksanen, 2005, Error propagation of DEM-based surface derivatives, Comput. Geosci., 31, 1015, 10.1016/j.cageo.2005.02.014 Ouedraogo, 2019, Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale, Hydrogeol. J., 27, 1081, 10.1007/s10040-018-1900-5 Pal, 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. Space Res., 64, 352, 10.1016/j.asr.2019.04.033 Pal, 2019, Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of eastern India, Model. Earth Syst. Environ., 5, 369, 10.1007/s40808-018-0540-z Pal, 2020, Ensemble of machine-learning methods for predicting gully Erosion susceptibility, Remote Sens., 12, 3675, 10.3390/rs12223675 Pal, 2021, Chemical weathering and gully erosion causing land degradation in a complex river basin of Eastern India: an integrated field, analytical and artificial intelligence approach, Nat. Hazards Pal, 2021, Changing climate and land use of 21st century influences soil erosion in India, Gondwana Res., 94, 164, 10.1016/j.gr.2021.02.021 Paola, 1995, A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery, Int. J. Remote Sens., 16, 3033, 10.1080/01431169508954607 Paudel, 2016, Multi-resolution landslide susceptibility analysis using a DEM and random forest, Int. J. Geosci., 7, 726, 10.4236/ijg.2016.75056 Pham, 2019, A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers, Geocarto Int., 1 Poesen, 2018, Soil erosion in the Anthropocene: research needs, Earth Surf. Process. Landf., 43, 64, 10.1002/esp.4250 Pourghasemi, 2018, Prediction of the landslide susceptibility: which algorithm, which precision?, CATENA, 162, 177, 10.1016/j.catena.2017.11.022 Pourghasemi, 2017, Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods, Theor. Appl. Climatol., 130, 609, 10.1007/s00704-016-1919-2 Pourghasemi, 2017, Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling, Sci. Total Environ., 609, 764, 10.1016/j.scitotenv.2017.07.198 Pourghasemi, 2020, Gully erosion spatial modelling: role of machine learning algorithms in selection of the best controlling factors and modelling process, Geosci. Front., 10.1016/j.gsf.2020.03.005 Pradhan, 2010, Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environ. Model Softw., 25, 747, 10.1016/j.envsoft.2009.10.016 Rahmati, 2017, Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion, Geomorphology, 298, 118, 10.1016/j.geomorph.2017.09.006 Rahmati, 2017, Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: an integrated framework, Sci. Total Environ., 579, 913, 10.1016/j.scitotenv.2016.10.176 Roy, 2019, A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong districts, West Bengal, India, Remote Sens., 11, 2866, 10.3390/rs11232866 Roy, 2020, Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility, Remote Sens., 12, 3284, 10.3390/rs12203284 Saha, 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, J. Environ. Manag., 287, 112284, 10.1016/j.jenvman.2021.112284 Sbroglia, 2018, Mapping susceptible landslide areas using geotechnical homogeneous zones with different DEM resolutions in Ribeirão Baú basin, Ilhota/SC/Brazil, Landslides, 15, 2093, 10.1007/s10346-018-1052-7 Sen, 2008 Sena, 2020, Analysis of terrain attributes in different spatial resolutions for digital soil mapping application in southeastern Brazil, Geoderma Reg., 21 Sharif Razavian, 2014, CNN features off-the-shelf: an astounding baseline for recognition, 806 Shumack, 2020, Deep learning for dune pattern mapping with the AW3D30 global surface model, Earth Surf. Process. Landf., 45, 2417, 10.1002/esp.4888 Sidle, 2019, Hydrogeomorphic processes affecting dryland gully erosion: implications for modelling, Prog. Phys. Geogr. Earth Environ., 43, 46, 10.1177/0309133318819403 Sinha, 2012, Application of Universal Soil Loss Equation (USLE) to recently reclaimed badlands along the Adula and Mahalungi Rivers, Pravara Basin, Maharashtra, J. Geol. Soc. India, 80, 341, 10.1007/s12594-012-0152-6 Sirtoli, 2008, Atributos topográficos secundários no mapeamento de pedoformas, Geociências (São Paulo), 27, 63 Stefano, 2011, Measurements of rill and gully erosion in Sicily, Hydrol. Process., 25, 2221, 10.1002/hyp.7977 Szegedy, 2015, Going deeper with convolutions, 1 Tien Bui, 2020, A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area, Sci. Total Environ., 701, 134413, 10.1016/j.scitotenv.2019.134413 Valentin, 2005, Gully erosion: impacts, factors and control, CATENA, Gully Erosion: A Global Issue, 63, 132, 10.1016/j.catena.2005.06.001 Wang, 2019, Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China, Sci. Total Environ., 666, 975, 10.1016/j.scitotenv.2019.02.263 Wechsler, 2007 Williams, 2011 Youssef, 2016, Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia, Landslides, 13, 839, 10.1007/s10346-015-0614-1 Zhang, 1994, Digital elevation model grid size, landscape representation, and hydrologic simulations, Water Resour. Res., 30, 1019, 10.1029/93WR03553 Zhang, 2008, Effects of DEM resolution and source on soil erosion modelling: a case study using the WEPP model, Int. J. Geogr. Inf. Sci., 22, 925, 10.1080/13658810701776817 Zhang, 2018, An object-based convolutional neural network (OCNN) for urban land use classification, Remote Sens. Environ., 216, 57, 10.1016/j.rse.2018.06.034 Zhou, 2004, Analysis of errors of derived slope and aspect related to DEM data properties, Comput. Geosci., 30, 369, 10.1016/j.cageo.2003.07.005