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Erschienen in: Environmental Earth Sciences 8/2021

01.04.2021 | Original Article

Modeling gully erosion susceptibility in Phuentsholing, Bhutan using deep learning and basic machine learning algorithms

verfasst von: Sunil Saha, Raju Sarkar, Gautam Thapa, Jagabandhu Roy

Erschienen in: Environmental Earth Sciences | Ausgabe 8/2021

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Abstract

The present study attempts to demarcate the areas susceptible to gully erosion in Phuentsholing, Bhutan, using Deep Learning CNN (convolution neural network) and artificial neuron network (ANN), Support Vector Machine (SVM) and maximum entropy, three basic machine learning techniques in the GIS setting. Application of deep learning technique is new in the field of gully erosion. Considering the 240 gully pixels and seventeen gully erosion conditioning factors (GECFs), the gully erosion susceptibility maps (GESMs) were prepared. Out of the 240 gully pixels, 70% were used as training datasets and 30% were used as validation datasets for modeling and judging the GESMs. The GECFs were selected based on the previous literatures and multi-collinearity test. The importance of the GECFs was assessed by the chi-square attribute evaluation (CSEA) and random forest (RF) methods. Finally, applying the receiver operating characteristics’ area under curve (AUC-ROC), RMSE, MAE and R-index, the robustness of the GESMs was evaluated and compared. The GESMs were classified using natural break classification method into very high, high, moderate, low and very low susceptible classes. Nearly, 20% of the study area has very high susceptibility to gully erosion. As per the results of CSEA and RF methods, sand concentration, land use\cover and altitudes have the largest contribution in making the area very susceptible to gully erosion. Results of the validation techniques recognized the entire selected model as accurate and robust. Among the selected models, the capability of CNN model (AUC = 0.910, MAE = 0.029, RMSE = 0.171 for training data and AUC = 0.929, MAE = 0.089, RMSE = 0.299 for testing data) in predicting the gully erosion susceptibility is higher than other models. The produced GESMs will be helpful to the researchers as well as decision makers in establishing gully erosion management strategies.

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Literatur
Zurück zum Zitat Anderson CG, Maxwell DC (2004) Starting a Digitization Center. Elsevier, Amsterdam, The Netherlands (ISBN 978-1843340737)CrossRef Anderson CG, Maxwell DC (2004) Starting a Digitization Center. Elsevier, Amsterdam, The Netherlands (ISBN 978-1843340737)CrossRef
Zurück zum Zitat Bosino A, Giordani P, Quénéhervé G, Maerker M (2020) Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: a case study in the Oltrepo Pavese, Northern Apennines, Italy. Earth Surface Process Landforms 45(12):3025–3041CrossRef Bosino A, Giordani P, Quénéhervé G, Maerker M (2020) Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: a case study in the Oltrepo Pavese, Northern Apennines, Italy. Earth Surface Process Landforms 45(12):3025–3041CrossRef
Zurück zum Zitat Bull LJ, Kirkby MJ (1997) Gully processes and modelling. Prog Phys Geogr 21(3):354–374CrossRef Bull LJ, Kirkby MJ (1997) Gully processes and modelling. Prog Phys Geogr 21(3):354–374CrossRef
Zurück zum Zitat Burrough PA, McDonell RA (1998) Principles of geographical information systems. Oxford University Press, New York, NY, USA, p 190 Burrough PA, McDonell RA (1998) Principles of geographical information systems. Oxford University Press, New York, NY, USA, p 190
Zurück zum Zitat Cama M, Schillaci C, Kropáček J, Hochschild V, Bosino A, Märker M (2020) A probabilistic assessment of soil erosion susceptibility in a head catchment of the Jemma Basin, Ethiopian Highlands. Geosciences 10(7):248CrossRef Cama M, Schillaci C, Kropáček J, Hochschild V, Bosino A, Märker M (2020) A probabilistic assessment of soil erosion susceptibility in a head catchment of the Jemma Basin, Ethiopian Highlands. Geosciences 10(7):248CrossRef
Zurück zum Zitat Capra A, Mazzara LM, Scicolone B (2005) Application of the EGEM model to predict ephemeral gully erosion in Sicily, Italy. CATENA 59(2):133–146CrossRef Capra A, Mazzara LM, Scicolone B (2005) Application of the EGEM model to predict ephemeral gully erosion in Sicily, Italy. CATENA 59(2):133–146CrossRef
Zurück zum Zitat Cherkassky V, Krasnopolsky V, Solomatine DP, Valdes J (2006) Computational intelligence in earth sciences and environmental applications: issues and challenges. Neural Netw 19:113CrossRef Cherkassky V, Krasnopolsky V, Solomatine DP, Valdes J (2006) Computational intelligence in earth sciences and environmental applications: issues and challenges. Neural Netw 19:113CrossRef
Zurück zum Zitat Conoscenti C, Agnesi V, Cama M, Caraballo-Arias NA, Rotigliano E (2018) Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degrad Dev 29(3):724–736. https://doi.org/10.1002/ldr.2772CrossRef Conoscenti C, Agnesi V, Cama M, Caraballo-Arias NA, Rotigliano E (2018) Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degrad Dev 29(3):724–736. https://​doi.​org/​10.​1002/​ldr.​2772CrossRef
Zurück zum Zitat Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci. Model Dev 8:1991–2007 (8(7), 2271–2312)CrossRef Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci. Model Dev 8:1991–2007 (8(7), 2271–2312)CrossRef
Zurück zum Zitat Desta L, Adunga B (2012) A field guide on gully prevention and control Nile Basin initiative, eastern Nile subsidiary action program (ENSAP), Eastern Nile, Technical Regional Office (ENTRO), Eastern Nile Watershed Management Project, Addis Ababa Desta L, Adunga B (2012) A field guide on gully prevention and control Nile Basin initiative, eastern Nile subsidiary action program (ENSAP), Eastern Nile, Technical Regional Office (ENTRO), Eastern Nile Watershed Management Project, Addis Ababa
Zurück zum Zitat Dudík M, Phillips SJ, Schapire RE (2004) Performance guarantees for regularized maximum entropy density estimation. In: Learning Theory, Conference on Learning Theory, COLT 2004, Banff, Canada, July 1–4, 2004, Proceedings, 472–486. Dudík M, Phillips SJ, Schapire RE (2004) Performance guarantees for regularized maximum entropy density estimation. In: Learning Theory, Conference on Learning Theory, COLT 2004, Banff, Canada, July 1–4, 2004, Proceedings, 472–486.
Zurück zum Zitat Gallant JC, Wilson JP (2000) Primary topographic attributes. In: Wilson JP, Gallant JC (eds) Terrain analysis: principles and applications. Wiley, New York, pp 51–85 Gallant JC, Wilson JP (2000) Primary topographic attributes. In: Wilson JP, Gallant JC (eds) Terrain analysis: principles and applications. Wiley, New York, pp 51–85
Zurück zum Zitat Hosseinalizadeh M, Kariminejad N, Chen W, Pourghasemi HR, Alinejad M, Behbahani AM, 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 329:184–193CrossRef Hosseinalizadeh M, Kariminejad N, Chen W, Pourghasemi HR, Alinejad M, Behbahani AM, 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 329:184–193CrossRef
Zurück zum Zitat Jakkula V (2006) Tutorial on Support Vector Machine (svm). 37 School of EECS, Washington State University. Jakkula V (2006) Tutorial on Support Vector Machine (svm). 37 School of EECS, Washington State University.
Zurück zum Zitat Karegowda AG, Manjunath A, Jayaram M (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2:271–277 Karegowda AG, Manjunath A, Jayaram M (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2:271–277
Zurück zum Zitat Kecman V (2005) Support Vector Machines—An Introduction. Springer, Berlin HeidelbergCrossRef Kecman V (2005) Support Vector Machines—An Introduction. Springer, Berlin HeidelbergCrossRef
Zurück zum Zitat Kiss R (2004) Determination of drainage network in digital elevation model, utilities and limitations. J Hung Geomath 2:16–29 Kiss R (2004) Determination of drainage network in digital elevation model, utilities and limitations. J Hung Geomath 2:16–29
Zurück zum Zitat Knighton D (1998) Fluvial forms and processes: a new perspective 1998. Arnold, London Knighton D (1998) Fluvial forms and processes: a new perspective 1998. Arnold, London
Zurück zum Zitat LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRef LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRef
Zurück zum Zitat Li Z, Zhu Q, Gold C (2005) Digital Terrain Modeling: Principles and Methodology. CRC Press, Boca Raton, FL, USA Li Z, Zhu Q, Gold C (2005) Digital Terrain Modeling: Principles and Methodology. CRC Press, Boca Raton, FL, USA
Zurück zum Zitat Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22 Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22
Zurück zum Zitat Maharaj RJ (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica West Indies. Eng Geol 34(1–2):53–79CrossRef Maharaj RJ (1993) Landslide processes and landslide susceptibility analysis from an upland watershed: a case study from St. Andrew, Jamaica West Indies. Eng Geol 34(1–2):53–79CrossRef
Zurück zum Zitat Mararakanye N, Sumner PD (2017) Gully erosion: a comparison of contributing factors in two catchments in South Africa. Geomorphology 288:99–110CrossRef Mararakanye N, Sumner PD (2017) Gully erosion: a comparison of contributing factors in two catchments in South Africa. Geomorphology 288:99–110CrossRef
Zurück zum Zitat Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30CrossRef Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30CrossRef
Zurück zum Zitat Myung IJ (2003) Tutorial on maximum likelihood estimation. J Math Psychol 47:90–100CrossRef Myung IJ (2003) Tutorial on maximum likelihood estimation. J Math Psychol 47:90–100CrossRef
Zurück zum Zitat Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58(4):275–287CrossRef Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58(4):275–287CrossRef
Zurück zum Zitat Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), 807–814 Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), 807–814
Zurück zum Zitat Parlak M (2007) Determination of erosion risk according to CORINE methodology (a case study: Kurtboğazı Dam). Int Congress River Basin Manage 1:856 Parlak M (2007) Determination of erosion risk according to CORINE methodology (a case study: Kurtboğazı Dam). Int Congress River Basin Manage 1:856
Zurück zum Zitat Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775CrossRef Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775CrossRef
Zurück zum Zitat Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017b) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology 298:118–137CrossRef Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017b) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology 298:118–137CrossRef
Zurück zum Zitat Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness that quantifies topographic heterogeneity. Intermt J Sci 5(1–4):23–27 Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness that quantifies topographic heterogeneity. Intermt J Sci 5(1–4):23–27
Zurück zum Zitat Rodriguez F, Maire E, Courjault-Rade P, Darrozes J (2002) The Black Top Hat function applied to a DEM: a tool to estimate recent incision in a mountainous watershed (Estibere Watershed, Central Pyrenees). Geophys Res Lett 29(6):9-1-9–4CrossRef Rodriguez F, Maire E, Courjault-Rade P, Darrozes J (2002) The Black Top Hat function applied to a DEM: a tool to estimate recent incision in a mountainous watershed (Estibere Watershed, Central Pyrenees). Geophys Res Lett 29(6):9-1-9–4CrossRef
Zurück zum Zitat Shannon CE (1948) Amathematical theory of communication. Bell Syst Tech J 27:379–423 (Math. Rev. (MathSciNet: MR10, 133e))CrossRef Shannon CE (1948) Amathematical theory of communication. Bell Syst Tech J 27:379–423 (Math. Rev. (MathSciNet: MR10, 133e))CrossRef
Zurück zum Zitat Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint rXiv:1409.1556. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint rXiv:1409.1556.
Zurück zum Zitat Statnikov A, Aliferis CF, Hardin DP, Guyon I (2013) A Gentle Introduction to Support Vector Machines in Biomedicine: Volime 2: Case Studies and Benchmarks, vol 2. World Scientific Publishing Co IncCrossRef Statnikov A, Aliferis CF, Hardin DP, Guyon I (2013) A Gentle Introduction to Support Vector Machines in Biomedicine: Volime 2: Case Studies and Benchmarks, vol 2. World Scientific Publishing Co IncCrossRef
Zurück zum Zitat Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
Zurück zum Zitat Tien Bui D, Tuan TA, 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/s10346-015-0557-6CrossRef Tien Bui D, Tuan TA, 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/​s10346-015-0557-6CrossRef
Zurück zum Zitat Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in west Sumatera Province, Indonesia. CATENA 118:124–135CrossRef Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in west Sumatera Province, Indonesia. CATENA 118:124–135CrossRef
Zurück zum Zitat Vafaie H, Imam IF (1994) Feature selection methods: Genetic algorithms vs. Greedy-like search. In International Conference on Fuzzy and Intelligent Control Systems; Walt Disney World: Orlando, FL, USA, 28 Vafaie H, Imam IF (1994) Feature selection methods: Genetic algorithms vs. Greedy-like search. In International Conference on Fuzzy and Intelligent Control Systems; Walt Disney World: Orlando, FL, USA, 28
Zurück zum Zitat Weiss A. (2001). Topographic Position and Landforms Analysis. Poster presentation. ESRI User Conference, San Diego, CA. Weiss A. (2001). Topographic Position and Landforms Analysis. Poster presentation. ESRI User Conference, San Diego, CA.
Zurück zum Zitat Wentworth CKA (1930) simplified method of determining the average slope of land surfaces. Am J Sci 117:184–194CrossRef Wentworth CKA (1930) simplified method of determining the average slope of land surfaces. Am J Sci 117:184–194CrossRef
Zurück zum Zitat 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. In: Pourghasemi HR, Rossi M (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Springer, pp 1–29 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. In: Pourghasemi HR, Rossi M (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Springer, pp 1–29
Zurück zum Zitat Zakerinejad R, Märker M (2014) Prediction of Gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: a case study in the Mazayejan Plain, Southwest Iran. Geogr Fis Din Quat 37(1):67–76 Zakerinejad R, Märker M (2014) Prediction of Gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: a case study in the Mazayejan Plain, Southwest Iran. Geogr Fis Din Quat 37(1):67–76
Zurück zum Zitat Zeiler MD, Fergus R (2014) Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014. Proceedings, Part I, 818: 833 Zeiler MD, Fergus R (2014) Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014. Proceedings, Part I, 818: 833
Metadaten
Titel
Modeling gully erosion susceptibility in Phuentsholing, Bhutan using deep learning and basic machine learning algorithms
verfasst von
Sunil Saha
Raju Sarkar
Gautam Thapa
Jagabandhu Roy
Publikationsdatum
01.04.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Environmental Earth Sciences / Ausgabe 8/2021
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-021-09599-2

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