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Transferability of predictive models to map susceptibility of ephemeral gullies at large scale

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Abstract

Ephemeral gully erosion is one of the main sources of soil loss from agricultural landscapes. Various tools including predictive models with machine learning (ML) algorithms have shown promise to identify susceptible areas. However, ML models have two limitations: (1) a trained model in one area may not be applicable in another area and (2) their application for susceptibility mapping of ephemeral gullies at large-scale areas presents a challenge due to the small size of these features and the need for digitization of all gullies for accurate susceptibility mapping. To overcome these limitations, a novel approach was introduced in the current study for comprehensive validation of ML models and prepare a susceptibility map of ephemeral gullies using an areal transfer of calibration–validation relations. Five ML models were evaluated in Northern Lake Erie Basin as a large-scale region. First, the region was divided into three zones based on the most effective factors of gully formation, and a total of eight watersheds were selected in Zone 1 and Zone 2 (hereafter study area). Zone 3 was not considered, because no gullies were observed in this zone. All the ML models were compared using a new validation approach, including local (trained and validated in the same area) and transferred (trained in one area and tested in other areas). Results showed that random forest (RF) was the most accurate local model in both Zone 1 (accuracy = 0.8833, AUC = 0.8830, sensitivity = 0.9239, and specificity = 0.8537) and Zone 2 (accuracy = 0.8606, AUC = 0.8608, sensitivity = 0.8987, and specificity = 0.8381), while gradient boosting decision tree (GBDT) was the most accurate transferred model (accuracy = 0.7298, AUC = 0.7297, and sensitivity = 0.7826). From the results of the current study, it can be concluded that (1) zonation technique supports the prediction of ephemeral gullies by dividing the study area into the small zones that carry similar topographical and morphological characteristics and (2) the local-transferred validation technique is a helpful method for finding the ML model that can be trained in a small watershed and scale up to the larger area without further calibration.

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References

  • Amiri M, Pourghasemi HR, Ghanbarian GA, Afzali SF (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

    Google Scholar 

  • 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

    Google Scholar 

  • Arabameri A, Chen W, Loche M, Zhao X, Li Y, Lombardo L, Cerda A, Pradhan B, Bui DT (2019a) Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front 11:1609–1620

    Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K (2019b) Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manage 232:928–942

    Google Scholar 

  • 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

    Google Scholar 

  • Arabameri A, Chandra Pal S, Costache R, Saha A, Rezaie F, Seyed Danesh A, Pradhan B, Lee S, Hoang N-D (2021a) Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomat Nat Hazards Risk 12(1):469–498

    Google Scholar 

  • Arabameri A, Rezaie F, Pal SC, Cerda A, Saha A, Chakrabortty R, Lee S (2021b) Modelling of piping collapses and gully headcut landforms: evaluating topographic variables from different types of DEM. Geosci Front 12(6):101230

    Google Scholar 

  • Başar CA, Korkmaz AA, Önal Y, Utku T (2022) Evaluation of optimum carbonization conditions of the blended domestic polymeric waste, biomass and lignite in the presence of catalyst by Taguchi and ANOVA optimization analysis. J Hazard Mater 8:100164

    Google Scholar 

  • Bouchnak H, Felfoul MS, Boussema MR, Snane MH (2009) Slope and rainfall effects on the volume of sediment yield by gully erosion in the Souar lithologic formation (Tunisia). Catena 78(2):170–177

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Google Scholar 

  • Casali J, Bennett SJ, Robinson KM (2000) Processes of ephemeral gully erosion. Int J Sediment Res 15(1):31–41

    Google Scholar 

  • Chowdhuri I, Pal SC, Arabameri A, Saha A, Chakrabortty R, Blaschke T, Pradhan B, Band SS (2020) Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment. Remote Sens 12(21):3620

    Google Scholar 

  • Chowdhuri I, Pal SC, Saha A, Chakrabortty R, Roy P (2021) Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation. Ecol Inform 65:101425

    Google Scholar 

  • Claps P, Fiorentino M, Oliveto G (1996) Informational entropy of fractal river networks. J Hydrol 187(1–2):145–156

    Google Scholar 

  • Conforti M, Aucelli PP, Robustelli G, Scarciglia F (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Nat Hazards 56(3):881–898

    Google Scholar 

  • Conoscenti C, Rotigliano E (2020) Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models. Geomorphology 359:107123

    Google Scholar 

  • Conoscenti C, Di Maggio C, Rotigliano E (2008) Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily. Nat Hazards 46(3):287–305

    Google Scholar 

  • Coover JB (2014) Phosphorus sorption and desorption in ephemeral gully erosion. Kansas State University, Manhattan

    Google Scholar 

  • Daggupati P, Roy S, Mckague K, Patterson C, Biswas A (2018) Simulating ephemeral gully erosion using AnnAGNPS in agricultural fields of Southern Ontario, Canada. EGU General Assembly Conference Abstracts, Vienna, Austria

  • Dolan DM, Chapra SC (2012) Great Lakes total phosphorus revisited: 1. Loading analysis and update (1994–2008). J Great Lakes Res 38(4):730–740

    CAS  Google Scholar 

  • Duman TY, Çan T, Emre Ö, Keçer M, Doğan A, Ateş Ş, Durmaz S (2005) Landslide inventory of northwestern Anatolia. Turk Eng Geol 77(1–2):99–114

    Google Scholar 

  • El Bilali A, Taleb A (2020) Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. J Saudi Soc Agric Sci 19(7):439–451

    Google Scholar 

  • El Bilali A, Taleb A, Brouziyne Y (2021) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 245:106625

    Google Scholar 

  • Foster G (1986) Understanding ephemeral gully erosion. Soil Cons 2:90–125

    Google Scholar 

  • Fox G, Sheshukov A, Cruse R, Kolar R, Guertault L, Gesch K, Dutnell R (2016) Reservoir sedimentation and upstream sediment sources: perspectives and future research needs on streambank and gully erosion. J Environ Manage 57(5):945–955

    CAS  Google Scholar 

  • Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K (2019) Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci Total Environ 664:1117–1132

    CAS  Google Scholar 

  • Gayen A, Saha S (2017) Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India. Model Earth Syst Environ 3(3):1123–1139

    Google Scholar 

  • 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 Total Environ 668:124–138

    CAS  Google Scholar 

  • Geroy I, Gribb M, Marshall H-P, Chandler D, Benner SG, McNamara JP (2011) Aspect influences on soil water retention and storage. Hydrol Process 25(25):3836–3842

    Google Scholar 

  • Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecol 84(11):2809–2815

    Google Scholar 

  • Gu Q, Chang Y, Xiong N, Chen L (2021) Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees. Appl Soft Comput 109:107472

    Google Scholar 

  • Han J, Guzman JA, Chu ML (2022) Gully erosion susceptibility considering spatiotemporal environmental variables: Midwest US region. J Hydrol Reg Stud 43:101196

    Google Scholar 

  • Hanief A, Laursen AE (2019) Meeting updated phosphorus reduction goals by applying best management practices in the Grand River watershed, southern Ontario. Ecol Eng 130:169–175

    Google Scholar 

  • Hr P, Beheshtirad M, Pradhan B (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geom Nat Hazards Risk 7(2):861–885

    Google Scholar 

  • Hu G, Yin C, Wan M, Zhang Y, Fang Y (2020) Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier. Biosyst Eng 194:138–151

    Google Scholar 

  • Huan J, Li H, Li M, Chen B (2020) Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: A study of Chang Zhou fishery demonstration base, China. Comput Electron Agric 175:105530

    Google Scholar 

  • Istanbulluoglu E, Bras RL, Flores‐Cervantes H, Tucker GE (2005) Implications of bank failures and fluvial erosion for gully development: Field observations and modeling. J Geophys Res 110:F01014. https://doi.org/10.1029/2004JF000145

    Article  Google Scholar 

  • Jahantigh M, Pessarakli M (2011) Causes and effects of gully erosion on agricultural lands and the environment. Commun Soil Sci Plant Anal 42(18):2250–2255

    CAS  Google Scholar 

  • Lainder AD, Wolfinger RD (2022) Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies: Winning solution to the M5 Uncertainty competition. Int J Forecast 38(4):1426–1433

    Google Scholar 

  • Liu J-J, Liu J-C (2021) An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm-A case study of the Yanchang Formation, mid-eastern Ordos Basin. China Mar Pet Geol 126:104939

    Google Scholar 

  • Luffman IE, Nandi A, Spiegel T (2015) Gully morphology, hillslope erosion, and precipitation characteristics in the Appalachian Valley and Ridge province, southeastern USA. Catena 133:221–232

    Google Scholar 

  • Ma X, Ding C, Luan S, Wang Y, Wang Y (2017) Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Trans Intell Transp Syst 18(9):2303–2310

    Google Scholar 

  • Malinin A, Prokhorenkova L, Ustimenko A (2020) Uncertainty in gradient boosting via ensembles. arXiv preprint arXiv:2006.10562

  • Märker M, Pelacani S, Schröder B (2011) A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy. Geomorphology 125(4):530–540

    Google Scholar 

  • Meinen BU, Robinson DT (2020) Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field. Sci Total Environ 729:138320

    CAS  Google Scholar 

  • Merz W, Bryan RB (1993) Critical conditions for rill initiation on sandy loam Brunisols: laboratory and field experiments in southern Ontario, Canada. Geoderma 57(4):357–385

    Google Scholar 

  • Mohebzadeh H, Biswas A, Rudra R, Daggupati P (2022) Machine learning techniques for gully erosion susceptibility mapping: a review. Geosci 12(12):429

    Google Scholar 

  • Ni X, Yuan Y, Liu W (2020) Impact factors and mechanisms of dissolved reactive phosphorus (DRP) losses from agricultural fields: a review and synthesis study in the Lake Erie basin. Sci Total Environ 714:136624

    CAS  Google Scholar 

  • O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690

    Google Scholar 

  • Pal SC, Arabameri A, Blaschke T, Chowdhuri I, Saha A, Chakrabortty R, Lee S, Band SS (2020) Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sens 12(22):3675

    Google Scholar 

  • Peters J, Verhoest NE, Samson R, Van Meirvenne M, Cockx L, De Baets B (2009) Uncertainty propagation in vegetation distribution models based on ensemble classifiers. Ecol Modell 220(6):791–804

    Google Scholar 

  • Pham BT, Nguyen MD, Nguyen-Thoi T, Ho LS, Koopialipoor M, Quoc NK, Armaghani DJ, Van Le H (2021) A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN Modeling. Transp Geotech 27:100508

    Google Scholar 

  • Phinzi K, Abriha D, Bertalan L, Holb I, Szabó S (2020) Machine learning for gully feature extraction based on a pan-sharpened multispectral image: multiclass vs. binary approach ISPRS. Int J Geo-Inf 9(4):252

    Google Scholar 

  • Ploton P, Mortier F, Réjou-Méchain M, Barbier N, Picard N, Rossi V, Dormann C, Cornu G, Viennois G, Bayol N (2020) Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat Commun 11(1):4540

    CAS  Google Scholar 

  • Poesen J (1996) Contribution of gully erosion to sediment production. In: Erosion and sediment yield: global and regional perspectives: proceedings of an international symposium, Exeter, UK

  • Pourghasemi HR, Rossi M (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(1):609–633

    Google Scholar 

  • Pourghasemi HR, Sadhasivam N, Kariminejad N, Collins AL (2020) Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geosci Front 11(6):2207–2219

    Google Scholar 

  • Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258

    Google Scholar 

  • 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

    Google Scholar 

  • Richards R, Baker D, Crumrine J (2009) Improved water quality in Ohio tributaries to Lake Erie: A consequence of conservation practices. J Soil Water Conserv 64(3):200–211

    Google Scholar 

  • Roy J, Saha S (2021) Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin, Eastern India. Adv Space Res 67(1):316–333

    Google Scholar 

  • Saha S (2017) Groundwater potential mapping using analytical hierarchical process: a study on Md Bazar Block of Birbhum District, West Bengal. Spat Inf Res 25(4):615–626

    Google Scholar 

  • Sahakyan N, Vardanyan V, Khachatryan M (2023) Gradient boosting decision trees classification of blazars of uncertain type in the fourth Fermi-LAT catalogue. Mon Not R Astron Soc 519(2):3000–3010

    CAS  Google Scholar 

  • Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161:40–57

    Google Scholar 

  • Shahin KA, Hassan N (2000) Sources of shared variability among body shape characters at marketing age in New Zealand White and Egyptian rabbit breeds. Ann Zootech 49(5):435–445

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Google Scholar 

  • Sheshukov AY, Sekaluvu L, Hutchinson SL (2018) Accuracy of topographic index models at identifying ephemeral gully trajectories on agricultural fields. Geomorphology 306:224–234

    Google Scholar 

  • Siddique T, Mahmud MS, Keesee AM, Ngwira CM, Connor H (2022) A survey of uncertainty quantification in machine learning for space weather prediction. Geoscience 12(1):27

    Google Scholar 

  • Soil Science Society of America (2001) Glossary of soil science terms.

  • Stout B (1965) Soil erosion by water, some measures for its control on cultivated lands. FAO Agric Dev Paper (81)

  • Sun R, Wang G, Zhang W, Hsu L-T, Ochieng WY (2020) A gradient boosting decision tree based GPS signal reception classification algorithm. Appl Soft Comput 86:105942

    Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. J Environ Geol 45(5):665–679

    Google Scholar 

  • Swift A, Heale R, Twycross A (2020) What are sensitivity and specificity? Evid Based Nurs 23(1):2–4

    Google Scholar 

  • Tang J, Henderson A, Gardner P (2021) Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets. Analyst 146(19):5880–5891

    CAS  Google Scholar 

  • Tziachris P, Nikou M, Aschonitis V, Kallioras A, Sachsamanoglou K, Fidelibus MD, Tziritis E (2023) Spatial or random cross-validation? The effect of resampling methods in predicting groundwater salinity with machine learning in Mediterranean Region. Water 15(12):2278

    CAS  Google Scholar 

  • Unwin DJ (1995) Geographical information systems and the problem of’error and uncertainty’. Prog Hum Geogr 19(4):549–558

    Google Scholar 

  • Van Vliet L, Wall G (1979) Comparison of predicted and measured sheet and rill erosion losses in southern Ontario. Can J Soil Sci 59(2):211–213

    Google Scholar 

  • Vandaele K, Poesen J, Govers G, van Wesemael B (1996) Geomorphic threshold conditions for ephemeral gully incision. Geomorphology 16(2):161–173

    Google Scholar 

  • Wall G, Driver G (1982) Cropland soil erosion: estimated cost to agriculture in Ontario. A report prepared by the Ontario Institute of Pedology for the Ontario Ministry of Agriculture and Food

  • Wang K, Wang X (2021) Generational differences in automobility: comparing America’s Millennials and Gen Xers using gradient boosting decision trees. Cities 114:103204

    Google Scholar 

  • Wang C, Cruse RM, Gelder B, James D, Liu X (2021a) Grid order prediction of ephemeral gully head cut position: regional scale application. Catena 200:105158

    Google Scholar 

  • Wang F, Sahana M, Pahlevanzadeh B, Pal SC, Shit PK, Piran MJ, Janizadeh S, Band SS, Mosavi A (2021b) Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alex Eng J 60(6):5813–5829

    Google Scholar 

  • Xiao L, Lo S, Liu J, Zhou J, Li Q (2021) Nonlinear and synergistic effects of TOD on urban vibrancy: applying local explanations for gradient boosting decision tree. Sustain Cities Soc 72:103063

    Google Scholar 

  • Xu D, Chen S, Rossel RV, Biswas A, Li S, Zhou Y, Shi Z (2019) X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content. Geoderma 352:61–69

    CAS  Google Scholar 

  • Yang Y, Zhang X, Yang L (2022) Data-driven power system small-signal stability assessment and correction control model based on XGBoost. Energy Rep 8:710–717

    Google Scholar 

  • Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, University of Melbourne, Department 200

  • Yu Z, Guindani M, Grieco SF, Chen L, Holmes TC, Xu X (2022) Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron 110(1):21–35

    CAS  Google Scholar 

  • Zabihi M, Pourghasemi HR, Motevalli A, Zakeri MA (2019) Gully erosion modeling using GIS-based data mining techniques in Northern Iran: a comparison between boosted regression tree and multivariate adaptive regression spline. Natural hazards GIS-based spatial modeling using data mining techniques, Springer, Switzerland, pp 1–26

  • Zheng F, Gao X (2000) Soil erosion processes and modeling at loessial hillslope. Sha Peo Press, Xi’an

    Google Scholar 

  • Zhou M, Wei P, Deng L (2022) Research on the factorial effect of science and technology innovation (STI) policy mix using multifactor analysis of variance (ANOVA). J Innov Knowl 7(4):100249

    Google Scholar 

  • Zucca C, Canu A, Della Peruta R (2006) Effects of land use and landscape on spatial distribution and morphological features of gullies in an agropastoral area in Sardinia (Italy). Catena 68(2–3):87–95

    Google Scholar 

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Acknowledgements

Funding for this study was provided by a Natural Sciences and Engineering Research Council of Canada (NSERC) grant and the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) Agrifood Alliance.

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The work was supported by OMAFRA.

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  1. School of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada

    Hamid Mohebzadeh, Ramesh Rudra & Prasad Daggupati

  2. School of Environmental Sciences, University of Guelph, Guelph, ON, N1G2W1, Canada

    Asim Biswas

  3. Department of Geography, Environment and Geomatics, University of Guelph, Guelph, Canada

    Ben DeVries

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hamid Mohebzadeh. The first draft of the manuscript was written by Hamid Mohebzadeh, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Mohebzadeh, H., Biswas, A., DeVries, B. et al. Transferability of predictive models to map susceptibility of ephemeral gullies at large scale. Nat Hazards 120, 4527–4561 (2024). https://doi.org/10.1007/s11069-023-06377-0

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