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Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale

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  1. Practical Hydroinformatics, ISBN:9783540798804, 10.1007/978-3-540-79881-1
  2. Aljazzar TH (2010) Adjustment of DRASTIC vulnerability index to assess groundwater vulnerability for nitrate pollution using the advection-diffusion cell. Von der Fakultät für Georessourcen und Materialtechnik der Rheinisch-Westfälischen Technischen Hochschule Aachen Ph.D. thesis, 146 pp
  3. Alley W. M., Flow and Storage in Groundwater Systems, 10.1126/science.1067123
  4. Andrade A.I.A.S.S., Stigter T.Y., Multi-method assessment of nitrate and pesticide contamination in shallow alluvial groundwater as a function of hydrogeological setting and land use, 10.1016/j.agwat.2009.07.014
  5. Anning David W., Paul Angela P., McKinney Tim S., Huntington Jena M., Bexfield Laura M., Thiros Susan A., Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwestern United States, 10.3133/sir20125065
  6. Anuraga T.S., Ruiz L., Kumar M.S. Mohan, Sekhar M., Leijnse A., Estimating groundwater recharge using land use and soil data: A case study in South India, 10.1016/j.agwat.2006.01.017
  7. Barzegar Rahim, Moghaddam Asghar Asghari, Deo Ravinesh, Fijani Elham, Tziritis Evangelos, Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms, 10.1016/j.scitotenv.2017.11.185
  8. Bauder J. W., Sinclair K. N., Lund R. E., Physiographic and Land Use Characteristics Associated with Nitrate-Nitrogen in Montana Groundwater, 10.2134/jeq1993.00472425002200020004x
  9. BGS (2011) Depth to groundwater map. https://www.bgs.ac.uk/downloads/browse.cfm?sec=9&cat=38 . Accessed 19 April 2014
  10. Bonsor HC, MacDonald AM (2011) An initial estimate of depth to groundwater across Africa. British Geological Survey Open Report OR/11/067: 26pp
  11. Boy-Roura Mercè, Nolan Bernard T., Menció Anna, Mas-Pla Josep, Regression model for aquifer vulnerability assessment of nitrate pollution in the Osona region (NE Spain), 10.1016/j.jhydrol.2013.09.048
  12. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
  13. Breiman Leo, 10.1023/a:1010933404324
  14. Breiman Leo, Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author), 10.1214/ss/1009213726
  15. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Boca Raton
  16. Burow Karen R., Nolan Bernard T., Rupert Michael G., Dubrovsky Neil M., Nitrate in Groundwater of the United States, 1991−2003, 10.1021/es100546y
  17. Cameron K.C., Di H.J., Moir J.L., Nitrogen losses from the soil/plant system: a review : Nitrogen losses, 10.1111/aab.12014
  18. Cutler D. Richard, Edwards Thomas C., Beard Karen H., Cutler Adele, Hess Kyle T., Gibson Jacob, Lawler Joshua J., RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY, 10.1890/07-0539.1
  19. Davies D B, Sylvester-Bradley R, The contribution of fertiliser nitrogen to leachable nitrogen in the UK: A review, 10.1002/jsfa.2740680402
  20. Debernardi Laura, De Luca Domenico Antonio, Lasagna Manuela, Correlation between nitrate concentration in groundwater and parameters affecting aquifer intrinsic vulnerability, 10.1007/s00254-007-1006-1
  21. Defourny P, Kirches G, Brockmann C, Boettcher M, Peters M, Bontemps S, et al (2014) Land cover CCI product user guide version 2. 2014
  22. Döll P, Fiedler K (2008) Global-scale modeling of groundwater recharge. Hydrol Earth Syst Sci 12:863–885. https://doi.org/10.5194/hess-12-863-2008,2008
  23. Dubrovsky NM, Burow KR, Clark GM, Gronberg JM, Hamilton PA, Hitt KJ, Mueller DK, Munn MD, Nolan BT, Puckett LJ, Rupert MG, Short TM, Spahr NE, Sprague LA, Wilber WG (2010) The quality of our nation’s waters—nutrients in the nation’s streams and groundwater, 1992–2004. US Geological Survey Circular 1350, 174 pp
  24. ESRI (1969) ArcGIS, www.arcgis.com/home . Accessed 23 June 2015
  25. Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181
  26. Foster Stephen, Pulido-Bosch Antonio, Vallejos Ángela, Molina Luis, Llop Armando, MacDonald Alan M., Impact of irrigated agriculture on groundwater-recharge salinity: a major sustainability concern in semi-arid regions, 10.1007/s10040-018-1830-2
  27. Fram Miranda S., Belitz Kenneth, Probability of Detecting Perchlorate under Natural Conditions in Deep Groundwater in California and the Southwestern United States, 10.1021/es103103p
  28. Friedl M.A., Brodley C.E., Strahler A.H., Maximizing land cover classification accuracies produced by decision trees at continental to global scales, 10.1109/36.752215
  29. Gassiat Claire, Gleeson Tom, Luijendijk Elco, The location of old groundwater in hydrogeologic basins and layered aquifer systems : THE LOCATION OF OLD GROUNDWATER, 10.1002/grl.50599
  30. Gemitzi A., Petalas C., Pisinaras V., Tsihrintzis V. A., Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace, Greece), 10.1002/hyp.7143
  31. Genuer Robin, Poggi Jean-Michel, Tuleau-Malot Christine, Variable selection using random forests, 10.1016/j.patrec.2010.03.014
  32. Gislason Pall Oskar, Benediktsson Jon Atli, Sveinsson Johannes R., Random Forests for land cover classification, 10.1016/j.patrec.2005.08.011
  33. Gleeson Tom, Moosdorf Nils, Hartmann Jens, van Beek L. P. H., A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, 10.1002/2014gl059856
  34. Golkarian Ali, Naghibi Seyed Amir, Kalantar Bahareh, Pradhan Biswajeet, Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS, 10.1007/s10661-018-6507-8
  35. Greene Earl A., LaMotte Andrew E., Cullinan Kerri-Ann, Ground-water vulnerability to nitrate contamination at multiple thresholds in the mid-Atlantic region using spatial probability models, 10.3133/sir20045118
  36. Graham Michael H., CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION, 10.1890/02-3114
  37. Grömping Ulrike, Variable Importance Assessment in Regression: Linear Regression versus Random Forest, 10.1198/tast.2009.08199
  38. Gurdak Jason J., Qi Sharon L., Vulnerability of Recently Recharged Groundwater in Principle Aquifers of the United States To Nitrate Contamination, 10.1021/es300688b
  39. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell (10):993–1001
  40. Hanson CR (2002) Nitrate concentrations in Canterbury ground water – a review of existing data. Report no. R02/17. Environment Canterbury Technical Report, 87 pp
  41. Hao Aibing, Zhang Yilong, Zhang Eryong, Li Zhenghong, Yu Juan, Wang Huang, Yang Jianfeng, Wang Yao, Review: Groundwater resources and related environmental issues in China, 10.1007/s10040-018-1787-1
  42. Hartmann Jens, Moosdorf Nils, The new global lithological map database GLiM: A representation of rock properties at the Earth surface : TECHNICAL BRIEF, 10.1029/2012gc004370
  43. Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning, 2nd edn. Springer
  44. Hengl Tomislav, de Jesus Jorge Mendes, MacMillan Robert A., Batjes Niels H., Heuvelink Gerard B. M., Ribeiro Eloi, Samuel-Rosa Alessandro, Kempen Bas, Leenaars Johan G. B., Walsh Markus G., Gonzalez Maria Ruiperez, SoilGrids1km — Global Soil Information Based on Automated Mapping, 10.1371/journal.pone.0105992
  45. Hoyos I. C. Perez, Krakauer N., Khanbilvardi R., Random forest for identification and characterization of groundwater dependent ecosystems, 10.2495/wrm150081
  46. Hengl Tomislav, de Jesus Jorge Mendes, MacMillan Robert A., Batjes Niels H., Heuvelink Gerard B. M., Ribeiro Eloi, Samuel-Rosa Alessandro, Kempen Bas, Leenaars Johan G. B., Walsh Markus G., Gonzalez Maria Ruiperez, SoilGrids1km — Global Soil Information Based on Automated Mapping, 10.1371/journal.pone.0105992
  47. Jung Youn-Young, Koh Dong-Chan, Park Won-Bae, Ha Kyoochul, Evaluation of multiple regression models using spatial variables to predict nitrate concentrations in volcanic aquifers : Impact of Spatial Variables on Nitrate Contamination of Groundwater, 10.1002/hyp.10633
  48. Kazemi Gholam A., Lehr Jay H., Perrochet Pierre, Groundwater Age : Kazemi/Groundwater Age, ISBN:9780471929512, 10.1002/0471929514
  49. Khalil Abedalrazq, Almasri Mohammad N., McKee Mac, Kaluarachchi Jagath J., Applicability of statistical learning algorithms in groundwater quality modeling : GROUNDWATER MODELING BY LEARNING MACHINES, 10.1029/2004wr003608
  50. Mfumu Kihumba Antoine, Ndembo Longo Jean, Vanclooster Marnik, Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo, 10.1007/s10040-015-1337-z
  51. Kulabako N.R., Nalubega M., Thunvik R., Study of the impact of land use and hydrogeological settings on the shallow groundwater quality in a peri-urban area of Kampala, Uganda, 10.1016/j.scitotenv.2007.03.035
  52. Lapworth D. J., Nkhuwa D. C. W., Okotto-Okotto J., Pedley S., Stuart M. E., Tijani M. N., Wright J., Urban groundwater quality in sub-Saharan Africa: current status and implications for water security and public health, 10.1007/s10040-016-1516-6
  53. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22
  54. Liu Chen-Wuing, Wang Yeuh-Bin, Jang Cheng-Shin, Probability-based nitrate contamination map of groundwater in Kinmen, 10.1007/s10661-013-3319-8
  55. Loosvelt Lien, Peters Jan, Skriver Henning, Lievens Hans, Van Coillie Frieke M.B., De Baets Bernard, Verhoest Niko E.C., Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification, 10.1016/j.jag.2012.05.011
  56. Luo Yongming, Qiao Xianliang, Song Jing, Christie Peter, Wong Minghung, Use of a multi-layer column device for study on leachability of nitrate in sludge-amended soils, 10.1016/s0045-6535(03)00486-7
  57. MacDonald AM, Calow RC, MacDonald DM, Darling WG, Dochartaigh BÉÓ (2009) What impact will climate change have on rural groundwater supplies in Africa. Hydrol Sci J 64(690–703). 18pp
  58. MacDonald AM, Taylor RG, Bonsor HC (2013) Groundwater in Africa – is there sufficient water to support the intensification of agriculture from “Land Grabs”? Hand book of land and water grabs in Africa, 9pp
  59. Mair Alan, El-Kadi Aly I., Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA, 10.1016/j.jconhyd.2013.07.004
  60. Margat J (2010) Ressources et utilisation des eaux souterraines en Afrique. Managing Shared Aquifer Resources in Africa, Third International Conférence Tripoli 25–27 may 2008. International Hydrological Programme, Division of Water Sciences, IHP-VII Series on groundwater No.1, UNESCO, p 26–34
  61. Masterson, JP, Hess KM, Walter DA, LeBlanc DR (2002) Simulated changes in the sources of ground water for public-supply wells, ponds, streams, and coastal areas on Western Cape Cod, Massachusetts. US Geological Survey Water Resources Investigations Report 02–4143
  62. Mattern Samuel, Vanclooster Marnik, Estimating travel time of recharge water through a deep vadose zone using a transfer function model, 10.1007/s10652-009-9148-1
  63. Mattern Samuel, Raouafi Walid, Bogaert Patrick, Fasbender Dominique, Vanclooster Marnik, Bayesian Data Fusion (BDF) of Monitoring Data with a Statistical Groundwater Contamination Model to Map Groundwater Quality at the Regional Scale, 10.4236/jwarp.2012.411109
  64. Mendes MP, Rodriguez-Galiano V, Luque-Espinar JA, Ribeiro L, Chica- Olmo M (2016) Applying random forest to assess the vulnerability of groundwater to pollution by nitrates. geoENV 2016. The 11th International Conference onGeostatistics for Environmental Applications. Lisbon, Portugal. geoENV2016BookofAbstractsMPM
  65. Moreno Roberto, Zamora Ricardo, Molina Juan Ramón, Vasquez Angélica, Herrera Miguel Ángel, Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent), 10.1016/j.ecoinf.2011.07.003
  66. Murtaugh Paul A., Performance of several variable-selection methods applied to real ecological data, 10.1111/j.1461-0248.2009.01361.x
  67. Naghibi Seyed Amir, Ahmadi Kourosh, Daneshi Alireza, Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping, 10.1007/s11269-017-1660-3
  68. Nelson A (2004) Population Density for Africa in 2000, 4th edn. Retrieved 1/27/2011 from UNEP/GRID Sioux Falls. https://databasin.org/datasets/4d59b959e8b040688037d2fe83a3f369 . Accessed 19 April 2015
  69. Nolan Bernard T., Hitt Kerie J., Vulnerability of Shallow Groundwater and Drinking-Water Wells to Nitrate in the United States, 10.1021/es060911u
  70. Nolan Bernard T., Hitt Kerie J., Ruddy Barbara C., Probability of Nitrate Contamination of Recently Recharged Groundwaters in the Conterminous United States, 10.1021/es0113854
  71. Nolan Bernard T., Fienen Michael N., Lorenz David L., A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA, 10.1016/j.jhydrol.2015.10.025
  72. Nolan BT, Gronberg JM, Faunt CC, Eberts SM, Belitz K (2014) Modeling nitrate at domestic and public-supply well depths in the Central Valley, California. Environ Sci Technol 48(10):5643–5651. https://doi.org/10.1021/es405452q.
  73. Norouz H, Negar AM, Attaallah N (2016) Determining vulnerable areas of Malekan Plain aquifer for nitrate, using random forest method. Journal of Environmental Studies, vol 41, no 4 (76), pp 923–942. http://www.sid.ir/En/Journal/ViewPaper.aspx?ID=550917 . Accessed online 2 August 2018
  74. Oliveira Sandra, Oehler Friderike, San-Miguel-Ayanz Jesús, Camia Andrea, Pereira José M.C., Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest, 10.1016/j.foreco.2012.03.003
  75. Oppel Steffen, Meirinho Ana, Ramírez Iván, Gardner Beth, O’Connell Allan F., Miller Peter I., Louzao Maite, Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds, 10.1016/j.biocon.2011.11.013
  76. Ouedraogo Issoufou, Vanclooster Marnik, A meta-analysis and statistical modelling of nitrates in groundwater at the African scale, 10.5194/hess-20-2353-2016
  77. Ouedraogo I, Vanclooster M (2016b) Shallow groundwater poses pollution problem for Africa. SciDev.Net, 4 pp, http://hdl.handle.net/2078.1/169630
  78. Ouedraogo Issoufou, Defourny Pierre, Vanclooster Marnik, Mapping the groundwater vulnerability for pollution at the pan African scale, 10.1016/j.scitotenv.2015.11.135
  79. Pal M., Random forest classifier for remote sensing classification, 10.1080/01431160412331269698
  80. Park No-Wook, Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets, 10.1007/s12665-014-3442-z
  81. Pearson S (2015) Identifying Groundwater Vulnerability from Nitrate Contamination: Comparison of the DRASTIC model and Environment Canterbury’s method. Degree of Master of Applied Science (Environmental Management). Lincoln University. 58 pp
  82. Peters Jan, Baets Bernard De, Verhoest Niko E.C., Samson Roeland, Degroeve Sven, Becker Piet De, Huybrechts Willy, Random forests as a tool for ecohydrological distribution modelling, 10.1016/j.ecolmodel.2007.05.011
  83. Potter Philip, Ramankutty Navin, Bennett Elena M., Donner Simon D., Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production, 10.1175/2009ei288.1
  84. Prasad Anantha M., Iverson Louis R., Liaw Andy, Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction, 10.1007/s10021-005-0054-1
  85. Puckett Larry J., Tesoriero Anthony J., Dubrovsky Neil M., Nitrogen Contamination of Surficial Aquifers—A Growing Legacy†, 10.1021/es1038358
  86. R Development Core Team (2015) A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.r-project.org/ . Last accessed 6 March 2015)
  87. Ramasamy N, Krishnan P, Bernard JC, Ritter WF(2003) Modeling Nitrate Concentration in Ground Water Using Regression and Neural Networks. Department of Food and Resource Economics. College of Agriculture and Natural Resources. University of Delaware(ORES SP03–01). 10pp
  88. RANKINEN K., SALO T., GRANLUND K., Simulated nitrogen leaching, nitrogen mass field balances and their correlation on four farms in south-western Finland during the period 2000 2005, 10.2137/145960607784125348
  89. Ransom Katherine M., Nolan Bernard T., A. Traum Jonathan, Faunt Claudia C., Bell Andrew M., Gronberg Jo Ann M., Wheeler David C., Z. Rosecrans Celia, Jurgens Bryant, Schwarz Gregory E., Belitz Kenneth, M. Eberts Sandra, Kourakos George, Harter Thomas, A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA, 10.1016/j.scitotenv.2017.05.192
  90. Applied Regression Analysis, ISBN:0387984542, 10.1007/b98890
  91. Ritter Axel, Muñoz-Carpena Rafael, Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments, 10.1016/j.jhydrol.2012.12.004
  92. Rodriguez-Galiano Victor F., Chica-Rivas Mario, Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models, 10.1080/17538947.2012.748848
  93. Rodriguez-Galiano V.F., Chica-Olmo M., Abarca-Hernandez F., Atkinson P.M., Jeganathan C., Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture, 10.1016/j.rse.2011.12.003
  94. Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J.P., An assessment of the effectiveness of a random forest classifier for land-cover classification, 10.1016/j.isprsjprs.2011.11.002
  95. Rodriguez-Galiano Victor, Mendes Maria Paula, Garcia-Soldado Maria Jose, Chica-Olmo Mario, Ribeiro Luis, Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain), 10.1016/j.scitotenv.2014.01.001
  96. Saffigna P. G., Keeney D. R., Nitrate and Chloride in Ground Water Under Irrigated Agriculture in Central Wisconsina, 10.1111/j.1745-6584.1977.tb03162.x
  97. Sahoo S., Russo T. A., Elliott J., Foster I., Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. : MACHINE LEARNING GROUNDWATER MODEL, 10.1002/2016wr019933
  98. Sajedi-Hosseini Farzaneh, Malekian Arash, Choubin Bahram, Rahmati Omid, Cipullo Sabrina, Coulon Frederic, Pradhan Biswajeet, A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination, 10.1016/j.scitotenv.2018.07.054
  99. Schweigert Peter, Pinter Nicholas, van der Ploeg Rienk R., Regression analyses of weather effects on the annual concentrations of nitrate in soil and groundwater, 10.1002/jpln.200321291
  100. Sesnie Steven E., Gessler Paul E., Finegan Bryan, Thessler Sirpa, Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments, 10.1016/j.rse.2007.08.025
  101. Sieling K., Kage H., N balance as an indicator of N leaching in an oilseed rape – winter wheat – winter barley rotation, 10.1016/j.agee.2006.01.011
  102. Sophocleous M (2004) Groundwater recharge. In: Silveira L, Wohnlich S, Usunoff EL (eds), Groundwater. Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford, UK. http://www.eolss.net . Accessed 9 September 2015
  103. Spalding R. F., Exner M. E., Occurrence of Nitrate in Groundwater—A Review, 10.2134/jeq1993.00472425002200030002x
  104. Steele B, Combining Multiple Classifiers An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping, 10.1016/s0034-4257(00)00145-0
  105. Stevenson FJ, Cole MA (1999) Cycles of soil carbon, nitrogen, phosphorus, sulfur, micronutrients, 2nd edn. Wiley, Hoboken
  106. Stigter T.Y., Ribeiro L., Dill A.M.M. Carvalho, Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land, 10.1016/j.jhydrol.2008.05.009
  107. Strobl Carolin, Boulesteix Anne-Laure, Zeileis Achim, Hothorn Torsten, 10.1186/1471-2105-8-25
  108. Teng Yanguo, Hu Bin, Zheng Jieqiong, Wang Jinsheng, Zhai Yuanzheng, Zhu Chen, Water quality responses to the interaction between surface water and groundwater along the Songhua River, NE China, 10.1007/s10040-018-1738-x
  109. Tesoriero Anthony J., Voss Frank D., Predicting the Probability of Elevated Nitrate Concentrations in the Puget Sound Basin: Implications for Aquifer Susceptibility and Vulnerability, 10.1111/j.1745-6584.1997.tb00175.x
  110. Thayalakumaran T, Charlesworth PB, Bristow K, van Bemmelen RJ, & Jaffres J (2004) Nitrate and ferrous iron concentrations in the lower Burdekin aquifers: assessing denitrification potential. In B. Singh (Ed), SuperSoil 2004 Conference 3rd Australian New Zealand Soils Conference (pp. 1-9). Sydney: The Regional Institute Ltd. https://researchoutput.csu.edu.au/en/publications/nitrate-and-ferrous-iron-concentrations-in-the-lower-burdekin-aqu , https://www.researchgate.net/publication/228513222_Nitrate_and_ferrous_iron_concentrations_in_the_lower_Burdekin_aquifers_assessing_denitrification_potenti . Accessed 17 Feb 2016
  111. Trambauer P., Dutra E., Maskey S., Werner M., Pappenberger F., van Beek L. P. H., Uhlenbrook S., Comparison of different evaporation estimates over the African continent, 10.5194/hess-18-193-2014
  112. UNECA, AU, AfDB (2000) The Africa Water Vision 2025: Equitable and Sustainable Use of Water for Socioeconomic Development. http://www.afdb.org/fileadmin/uploads/afdb/Documents/Generic-Documents/african%20water%20vision%202025%20to%20be%20sent%20to%20wwf5.pdf . Accessed 11 February 2016
  113. UNEP (1986) Final Report: UNEP/FAO World and Africa GIS Data Base; December 1984. http://www.grid.unep.ch/data/summary.php?dataid=GNV38&category=atmosphere&dataurl=http://www.grid.unep.ch/data/download/gnv038.zip&browsen=http://www.grid.unep.ch/data/download/gnv038.gif . Accessed 17 June 2015
  114. UNEP/DEWA (2014) Sanitation and Groundwater Protection – a UNEP Perspective. http://www.bgr.bund.de/EN/Themen/Wasser/Veranstaltungen/symp_sanitat-gwprotect/present_mmayi_pdf.pdf?__blob=publicationFile&v=2 . Accessed 14 August 2014
  115. Ward Mary H., deKok Theo M., Levallois Patrick, Brender Jean, Gulis Gabriel, Nolan Bernard T., VanDerslice James, Workgroup Report: Drinking-Water Nitrate and Health—Recent Findings and Research Needs, 10.1289/ehp.8043
  116. Wheeler David C., Nolan Bernard T., Flory Abigail R., DellaValle Curt T., Ward Mary H., Modeling groundwater nitrate concentrations in private wells in Iowa, 10.1016/j.scitotenv.2015.07.080
  117. Wick Katharina, Heumesser Christine, Schmid Erwin, Groundwater nitrate contamination: Factors and indicators, 10.1016/j.jenvman.2012.06.030
  118. Groundwater Pollution in Africa, ISBN:9780415411677, 10.1201/9780203963548
  119. Yost Andrew C., Petersen Steven L., Gregg Michael, Miller Richard, Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon, 10.1016/j.ecoinf.2008.08.004
  120. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856
Bibliographic reference Ouedraogo, Issoufou ; Defourny, Pierre ; Vanclooster, Marnik. Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. In: Hydrogeology Journal, (2018)
Permanent URL http://hdl.handle.net/2078.1/208690