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)

Science of the Total Environment

Volumn 476-477, Issue , 2014, Pages 189-206

  Rodriguez Galiano, Victor ^a   Mendes, Maria Paula ^b   Garcia Soldado, Maria Jose ^c   Chica Olmo, Mario ^c   Ribeiro, Luis ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c UNIVERSITY OF GRANADA   (Spain)

Author keywords

Groundwater; Machine learning techniques; Nitrates; Random Forest; Vulnerability assessment

Indexed keywords

AGRICULTURE; AQUIFERS; DECISION TREES; GROUNDWATER; GROUNDWATER RESOURCES; HYDROGEOLOGY; LEARNING SYSTEMS; MODELS; NITRATES; PATTERN RECOGNITION; POLLUTION; SOIL CONSERVATION; WATER CONSERVATION; WATER MANAGEMENT;

EXPLANATORY VARIABLES; GENERALIZATION ABILITY; GROUNDWATER NITRATE POLLUTIONS; MACHINE LEARNING TECHNIQUES; PATTERN RECOGNITION TECHNIQUES; PREDICTIVE CAPABILITIES; RANDOM FORESTS; VULNERABILITY ASSESSMENTS;

GROUNDWATER POLLUTION;

DRINKING WATER; GROUND WATER; NITRATE;

ACCURACY ASSESSMENT; ALGORITHM; GROUNDWATER POLLUTION; MODELING; NITRATE; POLLUTANT SOURCE; PREDICTION; REGRESSION ANALYSIS; VULNERABILITY; WATERSHED;

AGRICULTURAL MANAGEMENT; ALGORITHM; AQUIFER; ARTICLE; CASE STUDY; CLIMATE CHANGE; MACHINE LEARNING; NONPARAMETRIC TEST; PATTERN RECOGNITION; POLLUTANT; PRIORITY JOURNAL; RANDOM FOREST; SPAIN; WATER POLLUTION; WATER QUALITY; WATER SUPPLY; WATERSHED MANAGEMENT;

ANDALUCIA; GRANADA [ANDALUCIA]; SPAIN; VEGA DE GRANADA;

GROUNDWATER; MACHINE LEARNING TECHNIQUES; NITRATES; RANDOM FOREST; VULNERABILITY ASSESSMENT;

AGRICULTURE; ENVIRONMENTAL MONITORING; GROUNDWATER; MODELS, CHEMICAL; NITRATES; REGRESSION ANALYSIS; SPAIN; WATER POLLUTANTS, CHEMICAL; WATER POLLUTION, CHEMICAL;

EID: 84893025944     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2014.01.001     Document Type: Article

References (105)

1. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Communities 2000, (L327):1-73.
2. Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration. Off J Eur Union 2006, L372(19):19-31.
3. Directive 91/676/EEC Council Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources. Off J Eur Communities 1991, L375:1-13.
4. Abrahart R.J., See L.M., Solomatine D.P. Practical Hydroinformatics. Computational intelligence and technological developments in water applications 2008, 505. Springer, Berlin Heidelberg. R.J. Abrahart, L.M. See, D.P. Solomatine (Eds.).
5. Agterberg F., Bonham-Carter G. Measuring the performance of mineral-potential maps. Nat Resour Res 2005, 14(1):1-17.
6. Alcalá F.J., Custodio E. Spatial average aquifer recharge through atmospheric chloride mass balance and its uncertainty in continental Spain. Hydrol Process 2014, 28(2):218-236.
7. Aller L., Bennett T., Lehr J.H., Petty R.J., Hackett G. DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings 1987.
8. 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. Agric Water Manag 2009, 96(12):1751-1765.
9. Antonakos A.K., Lambrakis N.J. Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. J Hydrol 2007, 333(2-4):288-304.
10. Azamathulla H.M., Wu Fu-Chun, Ghani Aminuddin Ab, Narulkar Sandeep M, Zakaria Nor Azazi, Chang Chun Kiat Comparison between genetic algorithm and linear programming approach for real time operation. J Hydro Environ Res 2008, 2(3):172-181.
11. Babbar-Sebens M., Minsker B. A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: methodology and application to groundwater monitoring design. Environ Model Softw 2010, 25(10):1176-1187.
12. Banerjee P., Singh V.S., Chatttopadhyay K., Chandra P.C., Singh B. Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 2011, 398(3-4):212-220.
13. Baudron P., Alonso-Sarría F., García-Aróstegui J.L., Cánovas-García F., Martínez-Vicente D., Moreno-Brotóns J. Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification. J Hydrol 2013, 499:303-315.
14. Bazi Y., Melgani F. Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 2006, 44:3374-3385.
15. Bellman R. Dynamic programming 2003, Dover Publications, Mineola, NY, [366 pp.].
16. Blum A.L., Langley P. Selection of relevant features and examples in machine learning. Artif Intell 1997, 97(1-2):245-271.
17. Booker D.J., Snelder T.H. Comparing methods for estimating flow duration curves at ungauged sites. J Hydrol 2012, 434-435:78-94.
18. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 1997, 30(7):1145-1159.
19. Breiman L. Classification and regression trees 1984, Chapman & Hall/CRC.
20. Breiman L. Bagging predictors. Mach Learn 1996, 24(2):123-140.
21. Breiman L. Random Forests. Mach Learn 2001, 45(1):5-32.
22. Breiman L., Friedman J., Stone C.J., Olshen R.A. Classification and regression trees 1984, Chapman and Hall/CRC, Belmont, CA.
23. Cameron K.C., Di H.J., Moir J.L. Nitrogen losses from the soil/plant system: a review. Ann Appl Biol 2013, 162(2):145-173.
24. Castillo A. El emblase subterraneo de la Vega de Granada, uno de los mas importantes de Andalucia. Tierra Technol 1995, 9:37-42.
25. Castillo A. El acuífero de la Vega de Granada. Ayer y hoy (1966-2004). Agua, Minería y Medio Ambiente. Libro Homenaje al Professor Rafael Fernández Rubio 2005, IGME.
26. Chica-Olmo M., Luque-Espinar J.A., Rodriguez-Galiano V., Pardo-Igúzquiza E., Chica-Rivas L. Categorical indicator kriging for assessing the risk of groundwater nitrate pollution: the case of Vega de Granada aquifer (SE Spain). Sci Total Environ 2014, 470-471:229-239.
27. Critto A., Carlon C., Marcomini A. Characterization of contaminated soil and groundwater surrounding an illegal landfill (S. Giuliano, Venice, Italy) by principal component analysis and kriging. Environ Pollut 2003, 122(2):235-244.
28. De Stefano C., Fontanella F., Marrocco C., Schirinzi G. A feature selection algorithm for class discrimination improvement. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International 2007, 425-428.
29. Del Frate F., Iapaolo M., Casadio S., Godin-Beekmann S., Petitdidier M. Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles. J Quant Spectrosc Radiat Transf 2005, 92(3):275-291.
30. Dixon B. Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Appl Geogr 2005, 25(4):327-347.
31. Dixon B. A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrogeol J 2009, 17(6):1507-1520.
32. Doerfliger N., Zwahlen F. EPIK: a new method for outlining of protection areas in karstic environment. Karst Waters and Environmental Impacts 1997, 117-123.
33. Duffy C.J., Brandes D. Dimension reduction and source identification for multispecies groundwater contamination. J Contam Hydrol 2001, 48(1-2):151-165.
34. FAO-IGME, 1972. Proyecto piloto de utilización de aguas subterráneas para el desarrollo agrícola de la cuenca del guadalquivir. utilización de las aguas subterráneas para la mejora del regadío de la vega de granada. Inédito.
35. Focazio M.J., Reilly T.E., Rupert M.G., Helsel D.R. Assessing ground-water vulnerability to contamination: providing scientifically defensible information for decision makers. U.S. Geological Survey Circular 1224 2002.
36. Foster S., Hirata R., Gomes D., D'Elia M., Paris M. Ground Water Quality Protection: A Guide for Water Utilities, Municipal Authorities, and Environment Agencies 2002, The World Bank, Washington, D.C.
37. Friedl M.A., Brodley C.E., Strahler A.H. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geosci Remote Sens 1999, 37(2):969-977.
38. Gislason P.O., Benediktsson J.A., Sveinsson J.R. Random Forests for land cover classification. Pattern Recogn Lett 2006, 27(4):294-300.
39. Gogu R.C., Dassargues A. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ Geol 2000, 39(6):549-559.
40. Guadalquivir C.H.d Anejo N° 7 - Inventario de Presiones 2012, 66.
41. Guo L., Chehata N., Mallet C., Boukir S. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS J Photogramm Remote Sens 2011, 66(1):56-66.
42. Guyon I., Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003, 3:1157-1182.
43. Hansen L.K., Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 1990, 12(10):993-1001.
44. Herrera M., Torgo L., Izquierdo J., Pérez-García R. Predictive models for forecasting hourly urban water demand. J Hydrol 2010, 387(1-2):141-150.
45. Huan H., Wang J., Teng Y. Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin City of northeast China. Sci Total Environ 2012, 440:14-23.
46. Huang J., Xu J., Liu X., Liu J., Wang L. Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method. Math Comput Model 2011, 54(3-4):995-1004.
47. Huang Z., Nichol S.L., Siwabessy J.P.W., Daniell J., Brooke B.P. Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia. Int J Geogr Inf Sci 2011, 26(2):283-307.
48. IGME Atlas Hidrogeológico de la Provincia de Granada. Diputación Provincial De Granada 1990, Instituto Tecnológico Geominero De España, [http://aguas.igme.es/igme/publica/libro75/lib_75.htm]. IGME (Ed.).
49. Katsifarakis K.L., Karpouzos D.K., Theodossiou N. Combined use of BEM and genetic algorithms in groundwater flow and mass transport problems. Eng Anal Boundary Elem 1999, 23(7):555-565.
50. Khalil A., Almasri M.N., McKee M., Kaluarachchi J.J. Applicability of statistical learning algorithms in groundwater quality modeling. Water Resour Res 2005, 41(5):1-16.
51. Kohfahl C., Sprenger C., Herrera J.B., Meyer H., Chacon F.F., Pekdeger A. Recharge sources and hydrogeochemical evolution of groundwater in semiarid and karstic environments: a field study in the Granada Basin (Southern Spain). Appl Geochem 2008, 23(4):846-862.
52. Kotsiantis S., Pintelas P. Combining bagging and boosting. Int J Comput Intell 2004, 1(4):324-333.
53. Krogh A., Vedelsby J. Neural network ensembles, cross validation, and active learning. Advances in neural information processing systems 1995, 231-238.
54. Liaw A., Wiener M. Classification and regression by Random Forest. R News 2002, 2(3):18-22.
55. Loos M., Elsenbeer H. Topographic controls on overland flow generation in a forest - an ensemble tree approach. J Hydrol 2011, 409(1-2):94-103.
56. Lucas L., Jauzein M. Use of principal component analysis to profile temporal and spatial variations of chlorinated solvent concentration in groundwater. Environ Pollut 2008, 151(1):205-212.
57. Luque-Espinar J.A., Chica-Olmo M., Pardo-Igúzquiza E., García-Soldado M.J. Influence of climatological cycles on hydraulic heads across a Spanish aquifer. J Hydrol 2008, 354(1-4):33-52.
58. Massone H., Londoño M.Q., Martínez D. Enhanced groundwater vulnerability assessment in geological homogeneous areas: a case study from the Argentine Pampas. Hydrogeol J 2010, 18(2):371-379.
59. McGinnis S., Kerans B.L. Land use and host community characteristics as predictors of disease risk. Landsc Ecol 2012, 1-16.
60. McLay C.D.A., Dragten R., Sparling G., Selvarajah N. Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches. Environ Pollut 2001, 115(2):191-204.
61. Menció A., Mas-Pla J. Assessment by multivariate analysis of groundwater-surface water interactions in urbanized Mediterranean streams. J Hydrol 2008, 352(3-4):355-366.
62. Mendes M.P., Ribeiro L. Nitrate probability mapping in the northern aquifer alluvial system of the River Tagus (Portugal) using disjunctive kriging. Sci Total Environ 2010, 408(5):1021-1034.
63. Mingers J. An empirical comparison of selection measures for decision-tree induction. Mach Learn 1989, 3(4):319-342.
64. National Research Council Ground Water Vulnerability Assessment: Predicting Relative Contamination Potential Under Conditions of Uncertainty 1993, The National Academies Press, Washington, D.C.
65. Nolan B.T., Hitt K.J., Ruddy B.C. Probability of nitrate contamination of recently recharged groundwaters in the conterminous United States. Environ Sci Technol 2002, 36(10):2138-2145.
66. Ozdemir A. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 2011, 405(1-2):123-136.
67. Pal M. Random Forest classifier for remote sensing classification. Int J Remote Sens 2005, 26(1):217-222.
68. Pal M., Foody G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 2010, 48(5):2297-2307.
69. Pardo-Igúzquiza E., Chica-Olmo M. Estimation of gradients from sparse data by universal kriging. Water Resour Res 2004, 40(12):W12418.
70. Pathak D.R., Hiratsuka A. An integrated GIS based fuzzy pattern recognition model to compute groundwater vulnerability index for decision making. J Hydro Environ Res 2011, 5(1):63-77.
71. Peters J., De Baets B., Verhoest N.E.C., Samson R., Degroeve S., De Becker P., et al. Random Forests as a tool for ecohydrological distribution modelling. Ecol Model 2007, 207(2-4):304-318.
72. PPPH-DHG Recursos De Las Masas De Agua Subterráneas- Subunidad del la Vega de Granada 2010.
73. Quinlan J.R. C4.5 programs for machine learning 1993, Morgan Kaurmann, San Mateo, CA, [303 pp.].
74. Ribeiro L. Development and application of a new index of susceptibility of aquifers to contamination from agricultural sources. 7th Symposium of Hydraulics and Water Resources of Portuguese Spoken Countries APRH, Évora 2005, APRH (Ed.).
75. Rodriguez-Galiano V., Chica-Olmo M. Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Appl Geogr 2012, 35:208-218.
76. Rodriguez-Galiano V.F., Chica-Rivas M. Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models. Int J Digital Earth 2012, 10.1080/17538947.2012.748848.
77. 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. Remote Sens Environ 2012, 121:93-107.
78. Rodriguez-Galiano V.F., Ghimire B., Pardo-Igúzquiza E., Chica-Olmo M., Congalton R.G. Incorporating the downscaled Landsat TM thermal band in land-cover classification using Random Forest. Photogramm Eng Remote Sens 2012, 78(2):129-137.
79. 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. ISPRS J Photogramm Remote Sens 2012, 67:93-104.
80. Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sánchez J.P. An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 2012, 67:93-104.
81. Saeys Y., Inza I., Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23(19):2507-2517.
82. Sahoo G.B., Ray C., Mehnert E., Keefer D.A. Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Sci Total Environ 2006, 367(1):234-251.
83. Sanchez L.D., Castillo A.M. Distribución del ión cloruro en el acuífero de la Vega de Granada. Consideraciones Hidrodinámicas Hidrogeología y Recursos Hidráulicos 2001, XXVII:185-194.
84. Sánchez L.D., Castillo A.M. Sobre la calidad físico-química de las aguas superficiales del área metropolitana de Granada 2005.
85. Sesnie S., Gessler P., Finegan B., Thessler S. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sens Environ 2008, 112(5):2145-2159.
86. Shiri J., Kişi Ö. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 2011, 37(10):1692-1701.
87. Shiri J., Kisi O., Yoon H., Lee K.-K., Hossein Nazemi A. Predicting groundwater level fluctuations with meteorological effect implications - a comparative study among soft computing techniques. Comput Geosci 2013, 56:32-44.
88. Soldado M.J. Metodología basada en SIG para el desarrollo de un sistema de un sistema suporte de decisión para la gestión de la calidad de los recursos hídricos subterráneos de la "Vega de Granada" 2009, Universidad de Granada, [357 pp.].
89. Solomatine D., See L.M., Abrahart R.J. Data-Driven Modelling: Concepts, Approaches and Experiences." Chap. 2 In Practical Hydroinformatics 2008, 17-30. Water Science and Technology Library, Springer Berlin Heidelberg. R.J. Abrahart, L.M. See, D.P. Solomatine (Eds.).
90. Sonneveld M.P.W., Brus D.J., Roelsma J. Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils. Environ Pollut 2010, 158(1):92-97.
91. Sorichetta A., Masetti M., Ballabio C., Sterlacchini S. Aquifer nitrate vulnerability assessment using positive and negative weights of evidence methods, Milan, Italy. Comput Geosci 2012, 48:199-210.
92. Sorichetta A., Ballabio C., Masetti M., Robinson G.R., Sterlacchini S. A comparison of data-driven groundwater vulnerability assessment methods. Ground Water 2013, 51(6):866-879.
93. Steele B.M. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sens Environ 2000, 74(3):545-556.
94. Talei A., Chua L.H.C., Wong T.S.W. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall-runoff modeling. J Hydrol 2010, 391(3-4):248-262.
95. Tripathi S., Srinivas V.V., Nanjundiah R.S. Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 2006, 330(3-4):621-640.
96. UN-Water Policy brief on water quality. UN-Water 2011, 22. UN-Water (Ed.).
97. Vías J., Andreo B., Ravbar N., Hötzl H. Mapping the vulnerability of groundwater to the contamination of four carbonate aquifers in Europe. J Environ Manag 2010, 91(7):1500-1510.
98. Vincenzi S., Zucchetta M., Franzoi P., Pellizzato M., Pranovi F., De Leo G.A., et al. Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice Lagoon, Italy. Ecol Model 2011, 222(8):1471-1478.
99. Vowinckel E., Clawges R., Buxton D., Stedfast D., Louis J. Evaluation of the vulnerability of water from public supply wells to contamination by pesticide 1996, [FS-195-96].
100. Vrba J., Zaporozec A. Guidebook on mapping groundwater vulnerability. Int Contrib Hydrogeol 1994, 16.
101. Wang J., He J., Chen H. Assessment of groundwater contamination risk using hazard quantification, a modified DRASTIC model and groundwater value, Beijing Plain, China. Sci Total Environ 2012, 432:216-226.
102. Yoon H., Jun S.-C., Hyun Y., Bae G.-O., Lee K.-K. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 2011, 396(1-2):128-138.
103. Yu S., De Backer S., Scheunders P. Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery. Pattern Recogn Lett 2002, 23(1-3):183-190.
104. Zare A.H., Bayat V.M., Daneshkare A.P. Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models. Int Agrophys 2011, 25(2):187-192.
105. Zhao C., Liu C., Xia J., Zhang Y., Yu Q., Eamus D. Recognition of key regions for restoration of phytoplankton communities in the Huai River basin, China. J Hydrol 2012, 420-421:292-300.