1261Accesses
110Citations
Abstract
One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.
Résumé
Un outil important pour la gestion des ressources en eau dans les zones arides et semi-arides est la cartographie du potentiel des eaux souterraines. Dans cette étude, quatre modèles d’exploration de données, y compris le modèle K du voisin le plus proche (KNN), l’analyse discriminante linéaire (LDA), la Régression multivariée par spline adaptative (MARS), et l’analyse discriminante quadrique (QDA) ont été utilisés pour la cartographie du potentiel des eaux souterraines afin d’obtenir des cartes du potentiel en eau souterraine plus précise (GPM). A cet effet, 14 facteurs d’influence sur les eaux souterraines ont été considérés, tels que l’altitude, l’angle de pente, l’aspect de la pente, le plan de courbure, le profil de courbure, la longueur de la pente, l’indice d’humidité topographique indice d’humidité (TWI), l’indice de puissance d’un cours d’eau, la distance aux rivières, la densité des rivières, la distance aux failles, la densité de failles, l’utilisation des terres, et la lithologie. A partir des 842 sources de la zone d’étude, dans la région de Khalkhal en Iran, 70 % (589 sources) ont été considérés pour l’apprentissage et 30 % (253 sources) ont été utilisés comme ensemble de données de validation. Ensuite, les modèles KNN, LDA, MARS et QDA ont été appliqués à l’aide du logiciel de statistique R et les résultats ont été cartographiés comme GPM. Enfin, la courbe des caractéristiques de fonctionnement du récepteur (ROC) a été mise en œuvre pour évaluer la performance des modèles. Selon les résultats, les surfaces sous la courbe des ROCs ont été calculées ; elles sont de 81.4, 80.5, 79.6 et 79.2 % pour MARS, QDA, KNN et LDA respectivement. Ainsi, on peut conclure que les performances de KNN et de LDA sont acceptables et que les performances de MARS et QDA sont excellentes. En outre les résultats indiquaient une forte contribution des facteurs tels que l’altitude, TWI, l’angle de la pente, et la densité des failles alors que le plan de courbure et l’utilisation des terres ont été considérés comme les facteurs les moins importants.
Resumen
Una herramienta importante para la gestión de los recursos hídricos en zonas áridas y semiáridas es la cartografía del potencial de agua subterránea. En este estudio, se utilizaron cuatro modelos de minería de datos, incluyendo los K de vecinos más cercanos (KNN), análisis discriminante lineal (LDA), splines de regresión adaptativa multivariante (MARS) y el análisis discriminante cuadrático (QDA) para el mapeo del potencial del agua subterránea y para mejorar y precisar los mapas de potencial de agua subterránea (GPM). Para este propósito, se consideraron 14 factores de influencia en el agua subterránea, tales como altura, ángulo de inclinación, orientación de la pendiente, plano de curvatura, curvatura del perfil, longitud de la pendiente, índice topográfico de humedad (TWI), índice de energía de la corriente, distancia entre los ríos, densidad de drenaje, distancia entre fallas, densidad de fallas, uso de la tierra, y litología. A partir de 842 manantiales en la zona de estudio, en la región Khalkhal de Irán, el 70 % (589 manantiales) se consideran para entrenamiento y el 30 % (253 manantiales) se utilizaron como un conjunto de datos de validación. A continuación, se aplicaron los modelos KNN, LDA, MARS y QDA en el software estadístico R y los resultados fueron asignados como los GPM. Por último, la característica de funcionamiento de la curva receptora (ROC) se implementó para evaluar el desempeño de los modelos. Según los resultados, se calculó el área bajo la curva de ROC como 81.4, 80.5, 79.6, y 79.2 % para MARS, QDA, KNN, y LDA, respectivamente. Por lo tanto, se puede concluir que los desempeños de KNN y LDA eran aceptables y los desempeños de MARS y QDA eran excelentes. Además, los resultados representan una alta contribución de altitud, TWI, ángulo de inclinación, y densidad de fallas, mientras que se observa que el plano de curvatura y el uso de la tierra son los factores menos importantes.
摘要
干旱和半干旱地区水资源管理的一个重要工具就是地下水潜力绘图。在本研究中,四个数据开采模型包括k-最近邻居(KNN)、线性判别式分析(LDA)、多元变量适配回归曲线(MARS)及二次曲面判别式分析(QDA)用于地下水潜力绘图,以获得更好的、更精确的地下水潜力图(GPMs)。为此,考虑到了14个地下水影响因子,诸如高度、斜坡角度、斜坡方向、平面曲率、剖面曲率、斜坡长度、地形湿润指数(TWI)、河流功率指数、与河流的距离、河流密度、与断层的距离、断层密度、土地利用及岩性。在伊朗Khalkhal地区研究区有842个泉,70 %(589个泉)用于培训,30 %(253个泉)用做验证数据集。然后,在R统计软件中应用KNN, LDA, MARS和 QDA模型,所获结果绘制成地下水潜力图。最后,完成接收器操作特征(ROC)曲线以评估模型的性能。根据结果,计算得出接收器操作特征(ROC)曲线下的面积分别为多元变量适配回归曲线(MARS)模型为81.4 %、二次曲面判别式分析(QDA)模型为80.5 %、k-最近邻居(KNN)模型为79.6%以及线性判别式分析(LDA)模型为79.2 %。因此,可以得出这样的结论,就是KNN 和 LDA的性能可以接受,MARS 和 QDA的性能格外好。另外,结果着重描述了高度分布、TWI、斜坡角度和断层密度,而平面曲率和土地利用可以看出是影响力最小的因素。
Resumo
Uma ferramenta importante para a gestão de recursos hídricos em zonas áridas e semi-áridas é o mapeamento do potencial das águas subterrâneas. Neste estudo, foram utilizados quatro modelos de mineração de dados para o mapeamento do potencial de águas subterrâneas, incluindo K-vizinhos mais próximos (KNN), análise discriminante linear (LDA), regressãomultivariada de splines adaptativas (RMSA) e análise discriminante quadrica (ADQ) a fim de obter mapas potenciais de águas subterrâneas (MPAS) melhores e mais precisos. Para isso, foram considerados 14 fatores de influência para águas subterrâneas, tais como altitude, ângulo de inclinação, orientação do declive, curvatura plana, curvatura do perfil, comprimento do declive, índice topográfico de umidade (ITU), índice de potência do escoamento, distância entre rios, densidade rio, distância das falhas, densidade de falhas, uso da terra e litologia. A partir de 842 nascentes na área de estudo, na região de Khalkhal no Irã, 70 % (589 nascentes) foram consideradas para o treinamento e 30 % (253 nascentes) foram utilizadas como um conjunto de dados de validação. Em seguida, os modelos KNN, LDA, RMSA e ADQ foram aplicados no software estatístico R e os resultados foram mapeados como MPAS. Então, a curva de características de operação do receptor (COR) foi implementada para avaliar o desempenho dos modelos. De acordo com os resultados, a área sob a curva de COR foi calculada como 81.4, 80.5, 79.6, e 79.2 % para o RMSA, ADQ, KNN, e LDA, respectivamente. Assim, pode-se concluir que as realizações de KNN e LDA foram aceitáveis e as realizações de RMSA e ADQ foram excelentes. Além disso, os resultados representaram alta contribuição da altitude, ITU, ângulo de inclinação e densidade de falhas, enquanto curvatura plana e uso da terra foram vistos como os fatores menos importantes.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Adiat KAN, Nawawi MNM, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool: a case of predicting potential zones of sustainable groundwater resources. J Hydrol 440:75–89
Al-Abadi AM (2015) Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models. Appl Water Sci. doi:10.1007/s13201-015-0283-1
Arnous MO (2016) Groundwater potentiality mapping of hard-rock terrain in arid regions using geospatial modelling: example from Wadi Feiran basin, South Sinai. Egypt Hydrogeol J. doi:10.1007/s10040-016-1417-8
Anderson T (2003) An introduction to multivariate statistical analysis. Wiley, Chichester, UK
Baecher G, Christian J (2003) Reliability and statistics in geotechnical engineering. Wiley, Chichester, UK
Baeza C, Corominas J (2001) Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Proc Land 26:1127–1263
Betrie GD, Tesfamariam S, Morin KA, Sadiq R (2013) Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques. Environ Monit Assess 185(5):4171–4182. doi:10.1007/s10661-012-2859-7
Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69
Bonham-Carter G (1994) Geographic information systems for geoscientists modelling with GIS. Pergamon
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831
Chowdhury A, Jha MK, Chowdary VM (2010) Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur District, West Bengal using RS, GIS and MCDM techniques. Environ Earth Sci 59(6):1209–1222
Chenini I, Ben Mammou A (2010) Groundwater recharges study in arid region: an approach using GIS techniques and numerical modeling. Comput Geosci 36(6):801–817
Chezgi J, Pourghasemi HR, Naghibi SA, Moradi HR, Kheirkhah Zarkesh M (2015) Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province, Iran. Geocarto Int 31(6):628–646
Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gutiérrez AG, Rotigliano E, Agnesi V (2014) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Bence River basin (western Sicily, Italy). Geomorphology 242(49):64
Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology 111:79–87
Dai FC, Lee CF (2001) Frequency–volume relation and prediction of rainfall-induced landslides. Eng Geol 59(3):253–266
Dasarathy BB (1990) Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Washington, DC
Davoodi Moghaddam D, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran. Arab J Geosci 8(2):913–929
Development Core Team R (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46
Elmahdy SI, Mostafa Mohamed M (2014) Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area, United Arab Emirates. Geocarto Int 29(4):433–450
Eker AM, Dekmen M, Cambazoglu S, Duzgun SHB, Akgun H (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29(1):132–158
Felicisimo A, Cuartero A, Remondo J, Quiros E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189
Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2):175–189
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141
Geology Survey of Iran (GSI) (1997) Geological survey and mineral exploration of Iran.http://www.gsi.ir/Main/Lang_en/index.html. Accessed December 2014
Gretchen G, Moisen GG, Frescino TS (2002) Comparing five modelling techniques for predicting forest characteristics. Ecol Model 157:209–225
Gupta M, Srivastava PK (2010) Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water Int 35(2):233–245. doi:10.1080/02508061003664419
Gutiérrez ÁG, Schnabel S, Lavado Contador JF (2009a) Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Model 220(24):3630–3637. doi:10.1016/j.ecolmodel.2009.06.020
Gutiérrez ÁG, Schnabel S, Felicisimo AM (2009b) Modelling the occurrence of gullies in rangelands of southwest Spain. Earth Surf Process Landf 34:1894–1902
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184
Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis. Prentice Hall, New York
Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, Chichester, UK
Iranian Water Resources Institute (2013) Weather and climate report, Tehran province.http://www.thrw.ir/. Accessed 25 June 2013
Karbalaee F (2010) Water crisis in Iran. Paper presented at the Proceedings of the International Conference on Chemistry and Chemical Engineering (ICCCE), Gdansk, Poland, 1–3 Aug. 2010
Keith TZ (2006) Multiple regressions and beyond. Pearson, Boston
Kumar MG, Bali R, Agarwal AK (2009) Integration of remote sensing and electrical sounding data for hydrogeological exploration: a case study of Bakhar watershed. India Hydrol Sci J 54:949–960
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(2):188–196. doi:10.1016/j.ecolmodel.2006.05.022
Lee S, Kim YS, Oh HJ (2012) Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. J Environ Manag 96(1):91–105. doi:10.1016/j.jenvman.2011.09.016
Madrucci V, Taioli F, de Araújo CC (2008) Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, São Paulo State. Brazil J Hydrol 357:153–173
Manap MA, Nampak H, Pradhan B, Lee S, Soleiman WNA, Ramli MF (2012) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci. doi:10.1007/s12517-012-0795-z
Marmion M, Luoto M, Heikkinen RK, Thuiller W (2009) The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecol Model 220(24):3512–3520. doi:10.1016/j.ecolmodel.2008.10.019
McRoberts RE, N sset E, Gobakken T (2015) Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. Remote Sens Environ 163:13–22. doi:10.1016/j.rse.2015.02.026
Mitchell T (1997) Machine learning. McGraw-Hill, New York
Mogaji KA, Lim HS, Abdullah K (2015) Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster Shafer model. Arab J Geosci 8(5):3235–3258. doi:10.1007/s12517-014-1391-1
Moisen GG, Frescino TS (2002) Comparing five modelling techniques for prediction forest characteristics. Ecol Model 157:209–225
Mondal MS, Pandey AC, Garg RD (2008) Groundwater prospects evaluation based on hydrogeomorphological mapping using high resolution satellite images: a case study in Uttarakhand. J Indian Soc Remote Sens 36:69–76
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Processes 5(1):3–30
Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour Res 22(8):1350–1360
Moosavi V, Niazi Y (2015) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114. doi:10.1007/s10346-014-0547-0
Moradi Dashtpagerdi M, Nohegar A, Vagharfard H, Honarbakhsh A, Mahmoodinejad V, Noroozi A, Ghonchehpoor D (2013) Application of spatial analysis techniques to select the most suitable areas for flood spreading. Water Resour Manag 27(8):3071–3084
Moradi Dashtpagerdi M, Kousari MR, Vagharfard H, Ghonchepour D, Esmailzadeh Hosseini M, Ahani H (2014) An investigation of drought magnitude trend during 1975–2005 in arid and semi-arid regions of Iran. Environ Earth Sci 73(3):1231–1244
Muñoz J, Felicísimo AM (2004) Comparison of statistical methods commonly used in predictive modelling. J Veg Sci 15:285–292
Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2015) Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Sci Inform 8(1):171–186
Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29(14):5217–5236
Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(1):44
Oh HJ, Lee S, Soedradjat G (2010) Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environ Earth Sci 60:1317–1328
Oh HJ, Kim YS, Choi JK, Park E, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172
Ozdemir A (2011a) GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol 411:290–308
Ozdemir A (2011b) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136
Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197
Paraskevas T, Constantinos L, Dimitrios R, Ioanna L (2015) Landslide susceptibility assessments using the k-Nearest Neighbor algorithm and expert knowledge. Case study of the basin of Selinounda River, Achaia County, Greece. Presented at SafeChania 2015, The knowledge triangle in the Civil Protection Service Center of Mediterranean Architecture, Chania, Crete, Greece, 10–14 June 2015
Pourghasemi HR, Moradi HR, Fatemi Aghda SM, Gokceoglu C, Pradhan B (2013) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (north of Tehran, Iran). Arab J Geosci 7(5):1857–1878
Pourghasemi HR, Beheshtirad M (2014) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed. Iran Geocarto Int. doi:10.1080/10106049.2014.966161
Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J 22(3):643–662
Pradhan B (2009) Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci 1:120–129
Rahmati O, Pourghasemi HR, Melesse AM (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena 137(October):360–372. doi:10.1016/j.catena.2015.10.010
Rahmati O, Melesse AM (2016) Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran. Sci Total Environ. doi:10.1016/j.scitotenv.2016.06.176
Ramos-Canon AM, Prada-Sarmiento LF, Trujillo-Vela MG, Macias JP, Santos-R AC (2015a) Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia. Landslides. doi:10.1007/s10346-015-0593-2
Ramos-Canon AM, Prada-Sarmiento LF, Trujillo-Vela MG, Macias JP, Santos-R AC (2015b) Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia. Landslides. doi:10.1007/s10346-015-0593-2
Rekha VB, Thomas AP (2007) Integrated remote sensing and GIS for groundwater potentially mapping in Koduvan Àr-Sub-watershed of Meenachil River basin, Kottayam District, Kerala. Mahatma Gandhi University, Kerala, India
Samui P, Kurup P (2012) Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction. Soft Comput 16(8):1347–1351. doi:10.1007/s00500-012-0815-7
Tahmassebipoor N, Rahmati O, Noormohamadi F, Lee S (2016) Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9(1):79
Tayyebi A, Pijanowski BC (2014) Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int J Appl Earth Obs Geoinf 28:102–116
Tomppo EO, Gagliano C, De Natale F, Katila M, McRoberts RE (2009) Predicting categorical forest variables using an improved k-nearest neighbour estimator and Landsat imagery. Remote Sens Environ 113(3):500–517. doi:10.1016/j.rse.2008.05.021
Todd DK, Mays LW (2005) Groundwater hydrology, 3rd edn. Wiley, Hobokon, NJ, 636 pp
Tuffery S (2011) Data mining and statistics for decision-making. Wiley, Chichester, UK. doi:10.1002/9780470979174
Venkatesh YV, Kumar Raja S (2003) On the classification of multispectral satellite images using the multilayer perceptron. Pattern Recogn 36:2161–2175
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
Yang CC, Prasher SO, Lacroix R, Kim SH (2004) Application of multivariate adaptive regression splines (MARS) to simulate soil temperature. Trans Am Soc Agric Eng 47(3):881–887
Yeh HF, Lee CH, Hsu KC, Chang PH (2009) GIS for the assessment of the groundwater recharge potential zone. Environ Geol 58:185–195
Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M (2016) GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci 75(8):665. doi:10.1007/s12665-016-5424-9
Acknowledgements
We are grateful to the editor Dr. Martin Appold and three anonymous reviewers for useful insights which improved the manuscript.
Author information
Authors and Affiliations
Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Seyed Amir Naghibi
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran
Mostafa Moradi Dashtpagerdi
- Seyed Amir Naghibi
Search author on:PubMed Google Scholar
- Mostafa Moradi Dashtpagerdi
Search author on:PubMed Google Scholar
Corresponding author
Correspondence toSeyed Amir Naghibi.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Naghibi, S.A., Moradi Dashtpagerdi, M. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features.Hydrogeol J25, 169–189 (2017). https://doi.org/10.1007/s10040-016-1466-z
Received:
Accepted:
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative