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Application of Soft Computing Models for Simulating Nitrate Contamination in Groundwater: Comprehensive Review, Assessment and Future Opportunities

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Abstract

Groundwater is one of the major resources to supply the agriculture and urban water demand. Vulnerability of groundwater resources due to chemical substances is a crucial concern for groundwater quality management. The different nitrogen compounds, especially nitrate, plays an important role in groundwater quality. In last two decades, the efficient approaches called soft computing (SC) models were used for assessing the groundwater pollution. This study aims to assess the applications of various SC models for simulating the groundwater pollution due to nitrate contamination. In this way, the past trends and current applications of those models and essential factors required for assessing the ground water quality are demonstrated. Ultimately, several research gaps and possible future research direction are proposed.

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References

  1. Akkoyunlu A, Akiner ME (2010) Feasibility assessment of data-driven models in predicting pollution trends of Omerli Lake, Turkey. Water Resour Manag 24:3419–3436

    Article  Google Scholar 

  2. Al-Mahallawi K, Mania J, Hani A, Shahrour I (2012) Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas. Environ Earth Sci 65:917–928

    Article  Google Scholar 

  3. Alagha JS, Said MAM, Mogheir Y (2014) Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer. Environ Monit Assess 186:35–45

    Article  Google Scholar 

  4. Almasri MN (2004) Optimal management of nitrate contamination of ground water. Doctoral Dissertation, Utah State University

  5. Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20:851–871

    Article  Google Scholar 

  6. Almasri MN, Kaluarachchi JJ (2005) Multi-criteria decision analysis for the optimal management of nitrate contamination of aquifers. J Environ Manage 74:365–381

    Article  Google Scholar 

  7. Arabgol R, Sartaj M, Asghari K (2016) Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environ Model Assess 21:71–82

    Article  Google Scholar 

  8. Asadi P, Hosseini SM, Ataie-Ashtiani B, Simmons CT (2017) Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination. Environ Model Softw 96:146–157

    Article  Google Scholar 

  9. Baghapour MA, Nobandegani AF, Talebbeydokhti N, Bagherzadeh S, Nadiri AA, Gharekhani M, Chitsazan N (2016) Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran. J Environ Health Sci Eng 14:13

    Article  Google Scholar 

  10. Barzegar R, Moghaddam AA, Baghban H (2016) A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran. Stoch Environ Res Risk Assess 30:883–899

    Article  Google Scholar 

  11. Barzegar R, Moghaddam AA, Deo R, Fijani E, Tziritis E (2018) Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. Sci Total Environ 621:697–712

    Article  Google Scholar 

  12. Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom)

  13. Charulatha G, Srinivasalu S, Maheswari OU, Venugopal T, Giridharan L (2017) Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arab J Geosci 10:128

    Article  Google Scholar 

  14. Choi B-Y, Yun S-T, Kim K-H, Kim J-W, Kim HM, Koh Y-K (2014) Hydrogeochemical interpretation of South Korean groundwater monitoring data using self-organizing maps. J Geochem Explor 137:73–84

    Article  Google Scholar 

  15. Darwishe H, El Khattabi J, Chaaban F, Louche B, Masson E, Carlier E (2017) Prediction and control of nitrate concentrations in groundwater by implementing a model based on GIS and artificial neural networks (ANN). Environ Earth Sci 76:649

    Article  Google Scholar 

  16. Dixon B (2009) A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrogeol J 17:1507–1520

    Article  Google Scholar 

  17. Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309:17–38

    Article  Google Scholar 

  18. Dixon B (2004) Prediction of ground water vulnerability using an integrated GIS-based neuro-fuzzy techniques. J Spat, Hydrol, p 4

    Google Scholar 

  19. Dixon B, Scott HD (1998) Use of fuzzy logic with modified DRASTIC parameters to predict groundwater contamination. Vulnerability use ground surface waters South. Mississippi Val. Reg. AWRC Complet. Rep. 16–51

  20. Dixon B, Scott HD, Brahana JV, Mauromoustakos A (2001) Application of neuro-fuzzy technique + 2: 9 s to predict ground water vulnerability in Northwest Arkansas, Arkansas Water Resources Center, Fayetteville, AR. PUB 183. 66. https://scholarworks.uark.edu/awrctr/57. Accessed 28 Oct 2020

  21. Ehteshami M, Farahani ND, Tavassoli S (2016) Simulation of nitrate contamination in groundwater using artificial neural networks. Model Earth Syst Environ 2:28

    Article  Google Scholar 

  22. Elhatip H, Kömür MA (2008) Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks. Environ Geol 53:1157–1164

    Article  Google Scholar 

  23. Fijani E, Nadiri AA, Moghaddam AA, Tsai FT-C, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran. J Hydrol 503:89–100

    Article  Google Scholar 

  24. Foddis ML, Montisci A, Uras G, Matzeu A, Seddaiu G, Carletti A (2012) Prediction of nitrate concentration in groundwater using an artificial neural network (ANN) approach. In: Soil and water engineering. International conference of agricultural engineering-CIGR-AgEng 2012: agriculture and engineering for a healthier life, Valencia, Spain, 8–12 July 2012. CIGR-EurAgEng

  25. Fuentes I, Casanova M, Seguel O, Nájera F, Salazar O (2014) Morphophysical pedotransfer functions for groundwater pollution by nitrate leaching in Central Chile. Chil J Agric Res 74:340–348

    Article  Google Scholar 

  26. Garcet JDP, Ordonez A, Roosen J, Vanclooster M (2006) Metamodelling: theory, concepts and application to nitrate leaching modelling. Ecol Model 193:629–644

    Article  Google Scholar 

  27. Gautam RK, Panigrahi S (2003) Image processing techniques and neural network models for predicting plant nitrate using aerial images. In: Proceedings of the international joint conference on neural networks, 2003. IEEE, pp 1031–1036

  28. Gemitzi A, Petalas C, Pisinaras V, Tsihrintzis VA (2009) Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace, Greece). Hydrol Process Int J 23:372–383

    Article  Google Scholar 

  29. Hamamin DF, Nadiri AA (2018) Supervised committee fuzzy logic model to assess groundwater intrinsic vulnerability in multiple aquifer systems. Arab J Geosci 11:176

    Article  Google Scholar 

  30. Hong Y-S, Rosen MR (2001) Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network. Urban Water 3:193–204

    Article  Google Scholar 

  31. Hosseini SM, Mahjouri N (2014) Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater. Environ Monit Assess 186:3685–3699

    Article  Google Scholar 

  32. Hu ZY, Huang GH, Chan CW (2003) A fuzzy process controller for in situ groundwater bioremediation. Eng Appl Artif Intell 16:131–147

    Article  Google Scholar 

  33. Huang J, Xu J, Liu X, Liu J, Wang L (2011) Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method. Math Comput Model 54:995–1004

    Article  Google Scholar 

  34. Jalala S, Hani A, Shahrour I (2011) Characterizing the socio-economic driving forces of groundwater abstraction with artificial neural networks and multivariate techniques. Water Resour Manag 25:2147–2175

    Article  Google Scholar 

  35. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  36. Jebastina N, Arulraj GP (2018) Spatial prediction of nitrate concentration using GIS and ANFIS modelling in groundwater. Bull Environ Contam Toxicol 101:403–409

    Article  Google Scholar 

  37. Kaluli JW, Madramootoo CA, Djebbar Y (1998) Modeling nitrate leaching using neural networks. Water Sci Technol 38:127–134

    Article  Google Scholar 

  38. Keskin TE, Düğenci M, Kaçaroğlu F (2015) Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environ Earth Sci 73:5333–5347

    Article  Google Scholar 

  39. Khalil A, Almasri MN, McKee M, Kaluarachchi JJ (2005) Applicability of statistical learning algorithms in groundwater quality modeling. Water Resour Res. https://doi.org/10.1029/2004WR003608

    Article  Google Scholar 

  40. Kheradpisheh Z, Talebi A, Rafati L, Ghaneian MT, Ehrampoush MH (2015) Groundwater quality assessment using artificial neural network: a case study of Bahabad plain, Yazd, Iran. Desert 20:65–71

    Google Scholar 

  41. Konar A (2018) Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain. CRC Press, Boca Raton

    Google Scholar 

  42. Li J, Yoder RE, Odhiambo LO, Zhang J (2004) Simulation of nitrate distribution under drip irrigation using artificial neural networks. Irrig Sci 23:29–37

    Article  Google Scholar 

  43. Maiti S, Erram VC, Gupta G, Tiwari RK, Kulkarni UD, Sangpal RR (2013) Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks. Environ Monit Assess 185:3445–3465

    Article  Google Scholar 

  44. Markus M, Hejazi MI, Bajcsy P, Giustolisi O, Savic DA (2010) Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois. J Hydroinform 12:251–261

    Article  Google Scholar 

  45. Moasheri SA, Tabatabaie SM (2013) Estimating the groundwater nitrate by using artificial neural network and optimizing it by genetic algorithm. Int J Agric 3:699

    Google Scholar 

  46. Modrogan C, Diaconu E, Orbulet OD, Miron AR (2010) Forecasting study for nitrate ion removal using reactive barriers. Rev Chim (Bucharest) 61:6

    Google Scholar 

  47. Mousavi SF, Amiri MJ (2012) Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system. Soil Water Res 7:73–83

    Article  Google Scholar 

  48. Muhammetoglu A, Yardimci A (2006) A fuzzy logic approach to assess groundwater pollution levels below agricultural fields. Environ Monit Assess 118:337–354

    Article  Google Scholar 

  49. Nadiri AA, Gharekhani M, Khatibi R (2018) Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning. Water Resour Manag 32:3023–3040

    Article  Google Scholar 

  50. Nadiri AA, Gharekhani M, Khatibi R, Moghaddam AA (2017) Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res 24:8562–8577

    Article  Google Scholar 

  51. Nadiri AA, Norouzi H, Khatibi R, Gharekhani M (2019) Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a modelling strategy in two levels. J Hydrol 574:744–759

    Article  Google Scholar 

  52. Nakagawa K, Amano H, Kawamura A, Berndtsson R (2017) Classification of groundwater chemistry in Shimabara, using self-organizing maps. Hydrol Res 48:840–850

    Article  Google Scholar 

  53. Nolan BT, Fienen MN, Lorenz DL (2015) A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. J Hydrol 531:902–911

    Article  Google Scholar 

  54. 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 48:5643–5651

    Article  Google Scholar 

  55. Nolan BT, Malone RW, Gronberg JA, Thorp KR, Ma L (2011) Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA. Environ Sci Technol 46:901–908

    Article  Google Scholar 

  56. Nor ASM, Faramarzi M, Yunus MAM, Ibrahim S (2014) Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sens J 15:497–504

    Article  Google Scholar 

  57. Nourani V, Andalib G, Dąbrowska D (2017) Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds. J Hydrol 548:170–183

    Article  Google Scholar 

  58. Omari H, Abdallaoui A, Laafou S (2016) Multilayer perceptron neural networks with error back-propagation algorithm for the prediction of nitrate concentrations in groundwater. Int J Multi-Discip Sci 2:1–7

    Google Scholar 

  59. Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H (2017) Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J Civ Eng 21:134–140

    Article  Google Scholar 

  60. Ouedraogo I, Defourny P, Vanclooster M (2019) Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. Hydrogeol J 27:1081–1098

    Article  Google Scholar 

  61. RadFard M, Seif M, Hashemi AHG, Zarei A, Saghi MH, Shalyari N, Morovati R, Heidarinejad Z, Samaei MR (2019) Protocol for the estimation of drinking water quality index (DWQI) in water resources: artificial neural network (ANFIS) and Arc-Gis. MethodsX 6:1021–1029

    Article  Google Scholar 

  62. Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Mollaefar E, Tiefenbacher J, Cipullo S, Ahmad B Bin (2019) Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. Sci Total Environ 688:855–866

    Article  Google Scholar 

  63. Ramasamy N, Krishnan P, Ritter WF, Bernard JC (2003) Modeling nitrate concentration in ground water using regression and neural networks. APEC Staff Papers. http://udspace.udel.edu/handle/19716/116. Accessed 28 Oct 2020

  64. Ray C, Klindworth KK (2000) Neural networks for agrichemical vulnerability assessment of rural private wells. J Hydrol Eng 5:162–171

    Article  Google Scholar 

  65. Rizeei HM, Azeez OS, Pradhan B, Khamees HH (2018) Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models. Environ Monit Assess 190:633

    Article  Google Scholar 

  66. Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) 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). Sci Total Environ 476:189–206

    Article  Google Scholar 

  67. Rodriguez-Galiano VF, Luque-Espinar JA, Chica-Olmo M, Mendes MP (2018) Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci Total Environ 624:661–672

    Article  Google Scholar 

  68. Sadeghfam S, Hassanzadeh Y, Nadiri AA, Zarghami M (2016) Localization of groundwater vulnerability assessment using catastrophe theory. Water Resour Manag 30:4585–4601

    Article  Google Scholar 

  69. Sahoo GB, Ray C, Wade HF (2005) Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks. Ecol Model 183:29–46

    Article  Google Scholar 

  70. Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B (2018) A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ 644:954–962

    Article  Google Scholar 

  71. Sharafati A, Haghbin M, Motta D, Yaseen ZM (2019) The application of soft computing models and empirical formulations for hydraulic structure scouring depth simulation: a comprehensive review, assessment and possible future research direction. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-019-09382-4

    Article  Google Scholar 

  72. Sharma V, Negi SC, Rudra RP, Yang S (2003) Neural networks for predicting nitrate-nitrogen in drainage water. Agric Water Manag 63:169–183

    Article  Google Scholar 

  73. Shekofteh H, Afyuni M, Hajabbasi MA, Iversen BV, Nezamabadi-pour H, Abassi F, Sheikholeslam F (2013) Nitrate leaching from a potato field using adaptive network-based fuzzy inference system. J Hydroinform 15:503–515

    Article  Google Scholar 

  74. Sirat M (2013) Neural network assessment of groundwater contamination of US Mid-continent. Arab J Geosci 6:3149–3160

    Article  Google Scholar 

  75. Suen J-P, Eheart JW (2003) Evaluation of neural networks for modeling nitrate concentrations in rivers. J Water Resour Plan Manag 129:505–510

    Article  Google Scholar 

  76. Vadiati M, Asghari-Moghaddam A, Nakhaei M, Adamowski J, Akbarzadeh AH (2016) A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices. J Environ Manag 184:255–270

    Article  Google Scholar 

  77. Vapnik V (1995) The nature of statistical learning theory. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3264-1

    Book  MATH  Google Scholar 

  78. Wagh V, Panaskar D, Muley A, Mukate S, Gaikwad S (2018) Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India. Groundw Sustain Dev 7:436–445

    Article  Google Scholar 

  79. Wagh VM, Panaskar DB, Muley AA (2017) Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Model Earth Syst Environ 3:36

    Article  Google Scholar 

  80. Wang MX, Liu GD, Wu WL, Bao YH, Liu WN (2006) Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN. Environ Geol 50:637–644

    Article  Google Scholar 

  81. Wang Y, Huang G-B, Saratchandran P, Sundararajan N (2005) Time series study of GGAP-RBF network: predictions of Nasdaq stock and nitrate contamination of drinking water. In: Proceedings. 2005 IEEE international joint conference on neural networks, 2005. IEEE, pp 3127–3132

  82. Wu R, Painumkal JT, Volk JM, Liu S, Louis SJ, Tyler S, Dascalu SM, Harris FC (2017) Parameter estimation of nonlinear nitrate prediction model using genetic algorithm. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 1893–1899

  83. Yang Y, Wang C, Guo H, Sheng H, Zhou F (2012) An integrated SOM-based multivariate approach for spatio-temporal patterns identification and source apportionment of pollution in complex river network. Environ Pollut 168:71–79

    Article  Google Scholar 

  84. Yesilnacar MI, Sahinkaya E, Naz M, Ozkaya B (2008) Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environ Geol 56:19–25

    Article  Google Scholar 

  85. Yi Q-X, Huang J-F, Wang F-M, Wang X-Z, Liu Z-Y (2007) Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environ Sci Technol 41:6770–6775

    Article  Google Scholar 

  86. Zaqoot HA, Hamada M, Miqdad S (2018) A comparative study of Ann for predicting nitrate concentration in groundwater wells in the southern area of Gaza Strip. Appl Artif Intell 32:727–744

    Article  Google Scholar 

  87. Zare AH, Bayat VM, Akhavan S, Mohammadi M (2011) Estimation of nitrate in Hamedan-Bahar Plain groundwater using artificial neural network and the effect of data resolution on prediction accuracy. J Env Stud 37(58):129–140

    Google Scholar 

  88. Zare AH, Bayat VM, Daneshkare AP (2011) Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models. Int Agrophys 25:187–192

    Google Scholar 

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Authors and Affiliations

  1. Young Researchers and Elites club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Masoud Haghbin

  2. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

    Ahmad Sharafati

  3. Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam

    Ahmad Sharafati

  4. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ahmad Sharafati

  5. Geospatial Analytics Lab, School of Geosciences, University of South Florida, Tampa, USA

    Barnali Dixon

  6. Department of Botany, Government Degree College Ramban, Jammu, India

    Vinod Kumar

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  1. Masoud Haghbin
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Haghbin, M., Sharafati, A., Dixon, B. et al. Application of Soft Computing Models for Simulating Nitrate Contamination in Groundwater: Comprehensive Review, Assessment and Future Opportunities. Arch Computat Methods Eng 28, 3569–3591 (2021). https://doi.org/10.1007/s11831-020-09513-2

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