Abstract
Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain. There, 160 dry-stone check dams (~ 77.8 check-dams km−2), accumulated sediments during a period that varied from 11 to 23 years. The SR was estimated in former research using a topographical method and a high-resolution Digital Elevation Model (DEM) (average of 0.14 m3 ha−1 year−1). Nine environmental-topographic parameters were calculated and employed as predictors of the SR. The ability of MARS, RF and SVM was evaluated by using a five-fold cross-validation, considering the entire area (ALL), the check dams on the hillslope (HILL) and the valley-bottoms (VALLEY), as well as the three catchments (B, C and D) with the highest number of check dams. The accuracy of the models was assessed by the relative root mean square error (RRMSE) and the mean absolute error (MAE). The results revealed that RF and SVM are able to predict SR with higher and more stable accuracy than MARS. This is evident for the datasets ALL, VALLEY and D, where errors of prediction exhibited by MARS were from 44 to 77% (RRMSE) and from 37 to 62% (MAE) higher than those achieved by RF and SVM, but it also held for the datasets HILL and B where the difference of RRMSE and MAE was 7–10% and 12–17%, respectively.
This is a preview of subscription content, log in via an institution to check access.
taken from the UAV using Pix4Dmapper Pro software
Similar content being viewed by others
References
Alatorre LC, Beguería S, García-Ruiz JM (2010) Regional scale modeling of hillslope sediment delivery: a case study in the Barasona Reservoir watershed (Spain) using WATEM/SEDEM. J Hydrol 391:109–123. https://doi.org/10.1016/j.jhydrol.2010.07.010
Alfonso-Torreño A, Gómez-Gutiérrez Á, Schnabel S, Lavado-Contador JF, de Sanjosé Blasco JJ, Sánchez Fernández M (2019) sUAS, SfM-MVS photogrammetry and a topographic algorithm method to quantify the volume of sediments retained in check-dams. Sci Total Environ 678:369–382. https://doi.org/10.1016/j.scitotenv.2019.04.332
Alfonso-Torreño A, Gómez-Gutiérrez Á, Schnabel S (2021) Dynamics of erosion and deposition in a partially restored valley-bottom gully. Land 10:62. https://doi.org/10.3390/land10010062
Arabameri A, Pradhan B, Pourghasemi HR, Rezaei K, Kerle N (2018) Spatial modelling of gully erosion using GIS and R programing: a comparison among three data mining algorithms. Appl Sci 8:1369. https://doi.org/10.3390/app8081369
Baade J, Franz S, Reichel A (2012) Reservoir siltation and sediment yield in the Kruger National Park, South Africa: a first assessment. Land Degrad Dev 23:586–600. https://doi.org/10.1002/ldr.2173
Bachiller AR, Rodríguez JLG, Sánchez JCR, Gómez DL (2019) Specific sediment yield model for reservoirs with medium-sized basins in Spain: an empirical and statistical approach. Sci Total Environ 681:82–101. https://doi.org/10.1016/j.scitotenv.2019.05.029
Bangash RF, Passuello A, Sanchez-Canales M, Terrado M, López A, Elorza FJ, Ziv G, Acuña V, Schuhmacher M (2013) Ecosystem services in Mediterranean river basin: climate change impact on water provisioning and erosion control. Sci Total Environ 458–460C:246–255. https://doi.org/10.1016/j.scitotenv.2013.04.025
Bellin N, Vanacker V, van Wesemael B, Solé-Benet A, Bakker MM (2011) Natural and anthropogenic controls on soil erosion in the Internal Betic Cordillera (southeast Spain). CATENA 87:190–200. https://doi.org/10.1016/j.catena.2011.05.022
Belmonte Serrato F, Romero Díaz A, Martínez-Lloris M (2005) Erosión en cauces afectados por obras de corrección hidrológica (Cuenca del Río Quípar, Murcia). Papeles De Geografía 41–42:71–83
Boix-Fayos C, de Vente J, Martínez-Mena M, Barberá GG, Castillo V (2008) The impact of land use change and check-dams on catchment sediment yield. Hydrol Process 22:4922–4935. https://doi.org/10.1002/hyp.7115
Bombino G, Gurnell AM, Tamburino V, Zema DA, Zimbone SM (2009) Adjustements in channel form, sediment calibre and vegetation around check-dams in the headwater reaches of mountain torrents, Calabria, Italy. Earth Surf Process Landf 34(7):1011–1021. https://doi.org/10.1002/esp.1791
Borrelli P, Märker M, Schütt B (2015) Modelling post-tree-harvesting soil and sediment deposition potential in the Turano river basin (Italian Central Apennine). Land Degrad Dev 26(4):356–366. https://doi.org/10.1002/ldr.2214
Borselli L, Cassi P, Torri D (2008) Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. CATENA 75:268–277. https://doi.org/10.1016/j.catena.2008.07.006
Bou Kheir R, Wilson J, Deng Y (2007) Use of terrain variables for mapping gully erosion susceptibility in Lebanon. Earth Surf Process Landf 32:1770–1782. https://doi.org/10.1002/esp1501
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. https://doi.org/10.1016/j.catena.2009.04.003
Boyce RC (1975) Sediment routing with sediment delivery ratios. In Present and prospective technology for predicting sediment yields and sources. US Department of Agriculture, Publication ARS-S-40:61–65
Breiman L (2001) Random forests. Mach Learn 45:5–32
Bussi G, Rodríguez-Lloveras X, Francés F, Benito G, Sánchez-Moya Y, Sopeña A (2013) Sediment yield model implementation based on check dam infill stratigraphy in a semiarid Mediterranean catchment. Hydrol Earth Syst Sci 17:3339–3354. https://doi.org/10.5194/hess-17-3339-2013
Bussi G, Francés F, Horel E, López-Tarazón JA, Batalla RJ (2014) Modelling the impact of climate change on sediment yield in a highly erodible Mediterranean catchment. J Soils Sediment 14(12):1921–1937. https://doi.org/10.1007/s11368-014-0956-7
Buyukyildiz M, Kumcu SY (2017) An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water Resour Manag 31:1343–1359. https://doi.org/10.1007/s11269-017-1581-1
Castillo VM, Mosch WM, Conesa García C, Barberá GG, Navarro Cano JA, López-Bermúdez F (2007) Effectiveness and geomorphological impacts of check dams for soil erosion control in a semiarid Mediterranean catchment: El Cárcavo (Murcia, Spain). CATENA 70(3):416–427. https://doi.org/10.1016/j.catena.2006.11.009
Catella M, Paris E, Solari L (2005) Case study: efficiency of slit-check dams in the Mountain region of Versilia Bain. J Hydraul Eng. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:3(145)
Cavalli M, Trevisani S, Comiti F, Marchi L (2013) Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. Geomorphology 188:31–41. https://doi.org/10.1016/j.geomorph.2012.05.007
Cerdà A (2002) The effect of season and parent material on water erosion on highly eroded soils in eastern Spain. J Arid Environ 52:319–337. https://doi.org/10.1006/jare.2002.1009
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Chen W, Xie X, Peng J et al (2018) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA 164:135–149. https://doi.org/10.1016/j.catena.2018.01.012
Çimen M (2008) Estimation of daily suspended sediments using support vector machines. Hydrol Sci J 53:656–666. https://doi.org/10.1623/hysj.53.3.656
Conesa C (2004) Los diques de retención en cuencas de régimen torrencial: diseño, tipos y funciones. Nimbus 16–14:125–132
Conesa García C, García Lorenzo R (2007) Litofacies de relleno y modelo de sedimentación de los diques de retención en el tramo inferior de la Rambla del Cárcavo (Cuenca del Segura). Cuaternario y Geomorfología 21(3–4):77–100
Conoscenti C, Rotigliano E (2020) Predicting gully occurrence at watershed scale: comparing topographic indices and multivariate statistical models. Geomorphology 359:107123. https://doi.org/10.1016/j.geomorph.2020.107123
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. https://doi.org/10.1016/j.geomorph.2013.08.021
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. https://doi.org/10.1016/j.geomorph.2014.09.020
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 29:724–736. https://doi.org/10.1002/ldr.2772
Cucchiaro S, Cavalli M, Vericat D, Crema S, Llena M, Beinat A, Marchi L, Cazorzi F (2019) Geomorphic effectiveness of check dams in a debris-flow catchment using multi-temporal topographic surveys. CATENA 174:78–83. https://doi.org/10.1016/j.catena.2018.11.004
De Vente J, Poesen J (2005) Predicting soil erosion and sediment yield at the basin scale: scale issues and semi-quantitative models. Earth-Sci Rev 71:95–125. https://doi.org/10.1016/j.earscirev.2005.02.002
De Vente J, Poesen J, Verstraeten G, Van Rompaey A, Govers G (2008) Spatially distributed modelling of soil erosion and sediment yield at regional scales in Spain. Glob Planet Change 60:393–415. https://doi.org/10.1016/j.gloplacha.2007.05.002
Delmas M, Cerdan O, Mouchel JM, Garcin M (2009) A method for developing a large-scale sediment yield index for European river basins. J Soils Sediment 9:613–626. https://doi.org/10.1007/s11368-009-0126-5
Francke T, López-Tarazón JA, Schröder B (2008) Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests. Hydrol Process 22:4892–4904. https://doi.org/10.1002/hyp.7110
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141
Garosi Y, Sheklabadi M, Porghasemi HR, Besalatpour AA, Conoscenti C, Van Oost K (2018) Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma 330:65–78. https://doi.org/10.1016/j.geoderma.2018.05.027
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. https://doi.org/10.1016/j.scitotenv.2019.02.093
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. https://doi.org/10.1016/j.scitotenv.2019.02.436
Gómez-Gutiérrez Á, Schnabel S, Felicísimo ÁM (2009a) Modelling the occurrence of gullies in rangelands of southwest Spain. Earth Surf Process Landf 34:1894–1902. https://doi.org/10.1002/esp1881
Gómez-Gutiérrez Á, Schnabel S, Lavado Contador F (2009b) Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Model 220:3630–3637. https://doi.org/10.1016/j.ecolmodel.2009.06.020
Gómez-Gutiérrez Á, Schnabel S, De Sanjosé JJ, Lavado Contador JF (2012) Exploring the relationships between gully erosion and hydrology in rangelands of SW Spain. Z Geomorphol Suppl Issues 56:27–44. https://doi.org/10.1127/0372-8854/2012/S-00071
Gómez-Gutiérrez Á, Conoscenti C, Angileri SE, Rotigliano E, Schnabel S (2015) Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Nat Hazards 79:291–314. https://doi.org/10.1007/s11069-015-1703-0
Grauso S, Pagano A, Fattoruso G, De Bonis P, Onori F, Regina P, Tebano C (2008) Relations between climatic-geomorphological parameters and sediment yield in a mediterranean semi-arid area (Sicily, southern Italy). Environ Geol 54:219–234. https://doi.org/10.1007/s00254-007-0809-4
Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14:259–278. https://doi.org/10.5194/nhess-14-259-2014
Herguido Sevillano E, Lavado Contador JF, Pulido M, Schnabel S (2017) Spatial patterns of lost and remaining trees in the Iberian wooded rangeland. Appl Geogr 87:170–183. https://doi.org/10.1016/j.apgeog.2017.08.011
Hovius N (1998) Controls on sediment supply by large rivers, relative role of eustasy, climate and tectonismin continental rocks. Society of Sedimentray Geology. Special Publication 59:3–16. https://doi.org/10.2110/pec.98.59.0002
Javernick L, Brasington J, Caruso B (2014) Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology 213:166–182. https://doi.org/10.1016/j.geomorph.2014.01.006
Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013
Kavzoglu T, Colkesen I, Sahin EK (2019) Machine learning techniques in landslide susceptibility mapping: a survey and a case study. In: Pradhan S, Vishal V, Singh T (eds) Landslides: theory, practice and modelling. Advances in natural and technological hazards research, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-77377-3_13
Keesstra SD (2007) Impact of natural reforestation on floodplain sedimentation in the Dragonja basin, SW Slovenia. Earth Surf Proc Land 32(1):49–65. https://doi.org/10.1002/esp.1360
Keesstra SD, van Dam O, Verstraeten G, van Huissteden J (2009) Changing sediment dynamics due to natural reforestation in the dragonja catchment, SW Slovenia. Catena 78(1):60–71. https://doi.org/10.1016/j.catena.2009.02.021
Keestra SD, Maroulis J, Argaman E, Voogt A, Wittenberg L (2014) Effects of controlled fire on hydrology and erosion under simulated rainfall. Cuad Investig Geogr 40(2):269–293. https://doi.org/10.18172/cig.2532
Kuhn S, Egert B, Neumann S, Steinbeck C (2008) Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction. BMC Bioinform 9:1–19. https://doi.org/10.1186/1471-2105-9-400
Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199:188–196. https://doi.org/10.1016/j.ecolmodel.2006.05.022
Ma Z, Mei G, Piccialli F (2020) Machine learning for landslides prevention: a survey. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05529-8
Martinello C, Cappadonia C, Conoscenti C, Agnesi V, Rotigliano E (2020) Optimal slope units partitioning in landslide susceptibility mapping. J Maps. https://doi.org/10.1080/17445647.2020.1805807
Martínez-Murillo JF, López-Vicente M (2018) Effect of salvage logging and check dams on simulated hydrological connectivity in a burned area. Land Degrad Dev 29:701–712. https://doi.org/10.1002/ldr.2735
Martín-Rosales W, Pulido-Bosch A, Gisbert J, Vallejos A (2003) Sediment yield estimation and check dams in a semiarid area (Sierra de Gádor, southern Spain), in Erosion Prediction in Ungauged Basins: integrating methods and techniques. IAHS Publ 279:51–58
Mekonnen M, Keesstra SD, Baartman JE, Ritsema CJ, Melesse AM (2015) Evaluating sediment storage dams: structural off-site sediment trapping measures in northwest Ethiopia. Cuad Investig Geogr 41(1):7–22. https://doi.org/10.18172/cig.2643
Middelkoop H, Daamen K, Gellens D, Grabs W, Kwadijk JCJ, Lang H, Parmet BWAH, Schädler B, Schulla J, Wilke K (2001) Impact of climate change on hydrological regimes and water resources management in the Rhine Basin. Clim Change 49:105–128. https://doi.org/10.1023/A:1010784727448
Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AH (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomat Nat Hazards Risk 8:1080–1102. https://doi.org/10.1080/19475705.2017.1294113
Mutua BM, Klik A, Loiskandl W (2006) Modelling soil erosion and sediment yield at a catchment scale: the case of Masinga catchment, Kenya. Land Degrad Dev 17(5):557–570. https://doi.org/10.1002/ldr.753
Nadal-Romero E, Martínez-Murillo JF, Vanmaercke M, Poesen J (2011) Scale-dependency of sediment yield from badland areas in Mediterranean environments. Prog Phys Geogr Earth Environ 35:297–332. https://doi.org/10.1177/0309133311400330
Naimi B, Hamm N, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling. Ecography (cop) 37:191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x
Nguyen KA, Chen W, Lin B-S, Seeboonruang U (2020) Using machine learning-based algorithms to analyze erosion rates of a watershed in northern Taiwan. Sustainability 12(5):2022. https://doi.org/10.3390/su12052022
Nunes JP, Seixas J, Pacheco NR (2008) Vulnerability of water resources vegetation productivity and soil erosion to climate change in Mediterranean watersheds. Hydrol Process 22:3115–3134. https://doi.org/10.1002/hyp.6897
Peterson BG, Carl P (2020) PerformanceAnalytics: econometric tools for performance and risk analysis. R package version 2.0.4. https://CRAN.R-project.org/package=PerformanceAnalytics
Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province. Iran J Earth Syst Sci 122:349–369. https://doi.org/10.1007/s12040-013-0282-2
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. https://doi.org/10.1016/j.gsf.2020.03.005
Pulido M, Schnabel S, Lavado Contador JF, Lozano-Parra J, González F (2018) The impacto of heavy grazing on soil quality and pasture production in rangelands of SW Spain. Land Degrad Dev 29(2):219–230. https://doi.org/10.1002/ldr.2501
Quiñonero-Rubio JM, Nadeu E, Boix-Fayos C, Vente J (2016) Evaluation of the effectiveness of forest restoration and check dams to reduce catchment sediment yield. Land Degrad Dev 27(4):1018–1031. https://doi.org/10.1002/ldr.2331
R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
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 82:1–28. https://doi.org/10.1007/s11069-016-2239-7
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. https://doi.org/10.1016/j.geomorph.2017.09.006
Restrepo JD, Kjerfve B, Hermelin M, Restrepo JC (2006) Factors controlling sediment yield in a major south American drainage basin: the Magdalena river, Colombia. J Hydrol 316:213–232. https://doi.org/10.1016/j.jhydrol.2005.05.002
Romero A (2008) Los diques de corrección hidrológica como instrumentos de cuantificación de la erosión. Cuad Investig Geogr 34:89–99. https://doi.org/10.18172/cig.1208
Rotigliano E, Martinello C, Hernandéz MA, Agnesi V, Conoscenti C (2019) Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies. Environ Earth Sci 78:210. https://doi.org/10.1007/s12665-019-8214-3
Rubio-Delgado J, Guillén J, Corbacho JA, Gómez-Gutiérrez Á, Baeza A, Schnabel S (2017) Comparison of two methodologies used to estimate erosion rates in Mediterranean ecosystems: 137Cs and exposed tree roots. Sci Total Environ 605–606:541–550. https://doi.org/10.1016/j.scitotenv.2017.06.248
Sarangi A, Madramootoo C, Enright P, Prasher S, Patel R (2005) Performance evaluation of ANN and geomorphology-based models for runoff and sediment yield prediction for a Canadian watershed. Curr Sci 89:2022–2033
Schnabel S, Ceballos Barbancho A, Gómez-Gutiérrez Á (2010) Erosión hídrica en la dehesa extremeña. In: Schnabel S, Lavado Contador JF, Gómez-Gutiérrez Á, García Marín R (eds) Aportaciones a la Geografía Física de Extremadura con especial referencia a las dehesas, Asociación Profesional para la Ordenación del Territorio, el Ambiente y el Desarrollo Sostenible, España, p 153–185
Schnabel S, Dahlgren RA, Moreno-Marcos G (2013) Soil and water dynamics. In: Campos P, Oviedo JS, Díaz M, Montero G (eds) Mediterranean oak woodland working landscapes. Dehesas of Spain and Ranchlands of California, Springer, Dordrecht, pp 91–122
Smith MW, Vericat D (2015) From experimental plots to experimental landscapes: topography, erosion and deposition in sub-humid badlands from Structure-from-Motion photogrammetry. Earth Surf Proc Land 40(12):1656–1671. https://doi.org/10.1002/esp.3747
Sougnez N, van Wesemael B, Vanacker V (2011) Low erosion rates measured for steep, sparsely vegetated catchments in southeast Spain. CATENA 84:1–11. https://doi.org/10.1016/j.catena.2010.08.010
Tamene L, Park SJ, Dikau R, Vlek PLG (2006) Analysis of factors determining sediment yield variability in the highlands of northern Ethiopia. Geomorphology 76:76–91. https://doi.org/10.1016/j.geomorph.2005.10.007
Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125:91–101. https://doi.org/10.1016/j.catena.2014.10.017
Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology. https://doi.org/10.1016/j.geomorph.2015.06.001
Ullman S (1979) The interpretation of structure from motion. Proc R Soc B 203:405-426. https://doi.org/10.1098/rspb.1979.0006
Valentin C, Poesen J, Li Y (2005) Gully erosion: impacts, factors and control. CATENA 63:132–153. https://doi.org/10.1016/j.catena.2005.06.001
Van Rompaey AJJ, Verstraeten G, Van Oost K, Govers G, Poesen J (2001) Modelling mean annual sediment yield using a distributed approach. Earth Surf Process Landf 26:1221–1236. https://doi.org/10.1002/esp.275
Vanmaercke M, Poesen J, Verstraeten G, de Vente J, Ocakoglu F (2011) Sediment yield in Europe; spatial patterns and scale dependency. Geomorphology 130(3–4):142–161. https://doi.org/10.1016/j.geomorph.2011.03.010
Vapnik V (1998) Statistical learning theory. Wiley, New York
Vargas-Cuervo G, Rotigliano E, Conoscenti C (2019) Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: an application to the events occurred in Mocoa (Colombia) on 1 April 2017. Geomorphology 339:31–43. https://doi.org/10.1016/j.geomorph.2019.04.023
Verstraeten G, Poesen J (2001) Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate. Geomorphology 40:123–144. https://doi.org/10.1016/S0169-555X(01)00040-X
Verstraeten G, Poesen J (2002) Regional scale variability in sediment and nutrient delivery from small agricultural watersheds. J Environ Qual 31(3):870–879. https://doi.org/10.2134/jeq2002.8700
Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39. https://doi.org/10.1016/j.ecolmodel.2011.12.007
Wei T, Simko V (2017) R package “corrplot”: visualization of a Correlation Matrix (Version 0.84). Available from https://github.com/taiyun/corrplot
Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) “Structure-from-Motion” photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
Wheaton JM, Brasington J, Darby SE, Sear DA (2010) Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surf Proc Landf 35:136–156. https://doi.org/10.1002/esp.1886
Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin Colorado. J Hydrol 225:67–91. https://doi.org/10.1016/S0022-1694(99)00136-5
Wing J, Kuhn M (2018) caret: classification and regression training. https://cran.r-project.org/package=caret
Woodget AS, Carbonneau PE, Visser F, Maddock IP (2015) Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf Proc Landf 40:47–64. https://doi.org/10.1002/esp.3613
Yilmaz B, Aras E, Nacar S, Kankal M (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840. https://doi.org/10.1016/j.scitotenv.2018.05.153
Zhao G, Kondolf GM, Mu X, Han M, He Z, Rubin Z, Wang F, Gao P, Sun W (2017) Sediment yield reduction associated with land use changes and check dams in a catchment of the Loess Plateau, China. CATENA 148:126–137. https://doi.org/10.1016/J.CATENA.2016.05.010
Zimmermann A, Francke T, Elsenbeer H (2012) Forests and erosion: insights from a study of suspended-sediment dynamics in an overland flow-prone rainforest catchment. J Hydrol 428–429:170–181. https://doi.org/10.1016/j.jhydrol.2012.01.039
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 68:87–95. https://doi.org/10.1016/j.catena.2006.03.015
Funding
This research was possible due to the financial support of the Spanish Ministry of Economy and Competitiveness (Project CGL2014-54822-R). Alberto Alfonso-Torreño was supported by a predoctoral fellowship (PD16004) from Junta de Extremadura and European Social Fund. Chiara Martinello was supported by a predoctoral fellowship from the Italian Ministry of Education, Universities and Research (MIUR). All authors have contributed equally to the realization of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of a Topical Collection in Environmental Earth Sciences on “Geosphere Anthroposphere Interlinked Dynamics: Geocomputing and New Technologies”, guest edited by Sebastiano Trevisani, Marco Cavalli, and Fabio Tosti.
Rights and permissions
About this article
Cite this article
Conoscenti, C., Martinello, C., Alfonso-Torreño, A. et al. Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs. Environ Earth Sci 80, 380 (2021). https://doi.org/10.1007/s12665-021-09695-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-021-09695-3