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Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms

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Abstract

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

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Data availability

Data are available from the corresponding author upon reasonable request and with permission of the National Research Foundation, Korea (KNRF).

References

  • Al-Abadi AM (2017) The application of Dempster–Shafer theory of evidence for assessing groundwater vulnerability at Galal Badra basin, Wasit governorate, east of Iraq. Appl Water Sci 7(4):1725–1740

    Article  CAS  Google Scholar 

  • Aller L, Bennett T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: A standardized system for evaluating ground water pollution potential using hydrogeologic settings: Ada, Okla., Robert S. Ken-Environmental Research Laboratory. EPA/600/2-87-035, Volumes 1 and 2

  • Arabameri, A., Rezaei, K., Cerdà, A., Conoscenti, C., & Kalantari, Z. (2019). A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660, 443–458.

  • Azad A, Manoochehri M, Kashi H, Farzin S, Karami H, Nourani V, Shiri J (2019) Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. J Hydrol 571:214–224

    Article  Google Scholar 

  • 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(1):1–16

    Article  CAS  Google Scholar 

  • Balaji L, Saravanan R, Saravanan K, Sreemanthrarupini NA (2021) Groundwater vulnerability mapping using the modified DRASTIC model: the metaheuristic algorithm approach. Environ Monit Assess 193:25. https://doi.org/10.1007/s10661-020-08787-0

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Barzegar R, Moghaddam AA, Adamowski J, Nazemi AH (2019) Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks. Environ Sci Pollut Res 26(8):8325–8339

    Article  CAS  Google Scholar 

  • Bui DT, Khosravi K, Tiefenbacher J, Nguyen H, Kazakis N (2020a) Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Sci Total Environ 721:137612

    Article  CAS  Google Scholar 

  • Bui QT, Nguyen QH, Nguyen XL, Pham VD, Nguyen HD, Pham VM (2020b) Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol 581:124379

    Article  Google Scholar 

  • Busico G, Kazakis N, Cuoco E, Colombani N, Tedesco D, Voudouris K, Mastrocicco M (2020) A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses. Water Res 171:115386

    Article  CAS  Google Scholar 

  • Cao C, Liu F, Tan H, Song D, Shu W, Li W, Xie Z (2018) Deep learning and its applications in biomedicine. Genomics, proteomics & bioinformatics 16(1):17–32

    Article  Google Scholar 

  • Chakraborty, S. (2021). Transfer learning based multi-fidelity physics informed deep neural network. Journal of Computational Physics, 426, 109942

  • Damodaran BB, Flamary R, Seguy V, Courty N (2020) An entropic optimal transport loss for learning deep neural networks under label noise in remote sensing images. Comput Vis Image Underst 191:102863

    Article  Google Scholar 

  • Dixon B, Uddameri V (2016) GIS and geocomputation for water resource science and engineering. John Wiley & Sons

  • Elzain HEEO (2020) Improvement of groundwater contamination vulnerability assessment using the adaptive neuro-fuzzy inference system with metaheuristic optimization algorithms. Ph.D. thesis, Pukyong Nat’l Univ., Korea, p. 120

  • Elzain HE, Chung SY, Park KH, Senapathi V, Sekar S, Sabarathinam C, Hassan M (2021) ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area. J Environ Manag 286:112162

    Article  CAS  Google Scholar 

  • Fijani E, Nadiri AA, Moghaddam AA, Tsai FTC, 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  CAS  Google Scholar 

  • He X, Guan H, Qin J (2015) A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. J Hydrol 527:88–100

    Article  Google Scholar 

  • Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, Kazakis N (2018) Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141

    Article  CAS  Google Scholar 

  • Huan H, Wang J, Teng Y (2012) 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 440:14–23

    Article  CAS  Google Scholar 

  • Jang WS, Engel B, Harbor J, Theller L (2017) Aquifer vulnerability assessment for sustainable groundwater management using DRASTIC. Water 9(10):792

    Article  Google Scholar 

  • Jia Z, Bian J, Wang Y, Wan H, Sun X, Li Q (2019) Assessment and validation of groundwater vulnerability to nitrate in porous aquifers based on a DRASTIC method modified by projection pursuit dynamic clustering model. J Contam Hydrol 226:103522

    Article  CAS  Google Scholar 

  • Kadkhodaie F, Moghaddam AA, Barzegar R, Gharekhani M, Kadkhodaie A (2019) Optimizing the DRASTIC vulnerability approach to overcome the subjectivity: a case study from Shabestar plain, Iran. Arab J Geosci 12(16):1–13

    Article  CAS  Google Scholar 

  • Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M (2019) A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. J Hydrol 572:17–31

    Article  Google Scholar 

  • Karimi V, Khatibi R, Ghorbani MA, Bui DT, Darbandi S (2020) Strategies for learning groundwater potential modelling indices under sparse data with supervised and unsupervised techniques. Water Resour Manag 34:2389–2417

    Article  Google Scholar 

  • Kazakis N, Voudouris KS (2015) Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: modifying the DRASTIC method using quantitative parameters. J Hydrol 525:13–25

    Article  CAS  Google Scholar 

  • Kazakis N, Matiatos I, Ntona MM, Bannenberg M, Kalaitzidou K, Kaprara E, Voudouris K (2020) Origin, implications and management strategies for nitrate pollution in surface and ground waters of Anthemountas basin based on a δ15N-NO3− and δ18O-NO3− isotope approach. Sci Total Environ 724:138211

    Article  CAS  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

  • Khatibi R, Ghorbani MA, Naghshara S, Aydin HARUN, Karimi V (2020) A framework for ‘inclusive multiple modelling’ with critical views on modelling practices–applications to modelling water levels of Caspian Sea and Lakes Urmia and Van. J Hydrol 587:124923

    Article  Google Scholar 

  • Khosravi K, Sartaj M, Tsai FTC, Singh VP, Kazakis N, Melesse AM, Pham BT (2018) A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 642:1032–1049

    Article  CAS  Google Scholar 

  • Lee, H., Koo, M. H., & Kim, Y. (2017). Impacts of seasonal pumping on stream‐aquifer interactions in Miryang, Korea. Groundwater, 55(6):906–916

  • Liang J, Li Z, Yang Q, Lei X, Kang A, Li S (2019a) Specific vulnerability assessment of nitrate in shallow groundwater with an improved DRSTIC-LE model. Ecotoxicol Environ Saf 174:649–657

  • Liang J, Xu W, Yue C, Yu K, Song H, Crisalle OD, Qu B (2019b) Multimodal multiobjective optimization with differential evolution. Swarm and evolutionary computation 44:1028–1059

  • Machiwal D, Jha MK, Singh VP, Mohan C (2018) Assessment and mapping of groundwater vulnerability to pollution: current status and challenges. Earth Sci Rev 185:901–927

    Article  Google Scholar 

  • Moayedi H, Mehrabi M, Bui DT, Pradhan B, Foong LK (2020) Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. J Environ Manag 260:109867

    Article  Google Scholar 

  • Moazamnia M, Hassanzadeh Y, Nadiri AA, Sadeghfam S (2020) Vulnerability indexing to saltwater intrusion from models at two levels using artificial intelligence multiple model (AIMM). J Environ Manag 255:109871

    Article  Google Scholar 

  • Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Moghaddam AA (2017) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706

    Article  CAS  Google Scholar 

  • Nadiri AA, Sedghi Z, Khatibi R, Sadeghfam S (2018) Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks. J Environ Manag 227:415–428

    Article  CAS  Google Scholar 

  • 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 

  • Neshat A, Pradhan B (2015) Risk assessment of groundwater pollution with a new methodological framework: application of Dempster–Shafer theory and GIS. Nat Hazards 78(3):1565–1585

    Article  Google Scholar 

  • Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm and evolutionary computation 44:546–558

    Article  Google Scholar 

  • Panagopoulos GP, Antonakos AK, Lambrakis NJ (2006) Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS. Hydrogeol J 14(6):894–911

    Article  CAS  Google Scholar 

  • Sahoo M, Sahoo S, Dhar A, Pradhan B (2016) Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context. J Hydrol 541:1303–1315

    Article  Google Scholar 

  • Serra J, do Rosário Cameira M, Cordovil CM, Hutchings NJ (2021) Development of a groundwater contamination index based on the agricultural hazard and aquifer vulnerability: application to Portugal. Sci Total Environ 772:145032

    Article  CAS  Google Scholar 

  • Shen S, Sadoughi M, Li M, Wang Z, Hu C (2020) Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Appl Energy 260:114296

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  Google Scholar 

  • Tomer T, Katyal D, Joshi V (2019) Sensitivity analysis of groundwater vulnerability using DRASTIC method: a case study of National Capital Territory, Delhi, India. Groundw Sustain Dev 9:100271

    Article  Google Scholar 

  • Tripathy, R. K., & Bilionis, I. (2018). Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification. Journal of computational physics, 375, 565-588

  • Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Pham BT (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena 188:104451

    Article  Google Scholar 

  • Venkatramanan S, Chung SY, Kim TH, Kim BW, Selvam S (2016) Geostatistical techniques to evaluate groundwater contamination and its sources in Miryang City, Korea. Environ Earth Sci 75(11):1–14

    Article  CAS  Google Scholar 

  • Voutchkova DD, Schullehner J, Rasmussen P, Hansen B (2020) A high-resolution nitrate vulnerability assessment of sandy aquifers (DRASTIC-N). J Environ Manag 277:111330

    Article  CAS  Google Scholar 

  • Wang N, Zhang D, Chang H, Li H (2020) Deep learning of subsurface flow via theory-guided neural network. J Hydrol 584:124700

    Article  Google Scholar 

  • World Health Organization. (2008). Guidelines for drinking-water quality: second addendum. Vol. 1, Recommendations

  • Yang XS (2020) Nature-inspired optimization algorithms. Academic Press

  • Yu X, Cui T, Sreekanth J, Mangeon S, Doble R, Xin P, Gilfedder M (2020) Deep learning emulators for groundwater contaminant transport modelling. J Hydrol 590:125351

    Article  Google Scholar 

  • Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1D1A3A03103683).

Funding

This research received the full funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Korea.

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Authors and Affiliations

  1. Department of Environmental & Earth Sciences, Pukyong National University, Busan, 48513, Korea

    Hussam Eldin Elzain & Sang Yong Chung

  2. Department of Disaster Management, Alagappa University, Tamil Nadu, 630003, India

    Venkatramanan Senapathi

  3. Department of Geology, V. O. Chidambaram College, Thoothukudi, 628008, India

    Selvam Sekar

  4. Department of Civil Engineering, Dong-A University, Busan, 49315, Korea

    Namsik Park

  5. College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Ahmed Abdulhamid Mahmoud

Authors
  1. Hussam Eldin Elzain
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  2. Sang Yong Chung
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  3. Venkatramanan Senapathi
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  4. Selvam Sekar
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  5. Namsik Park
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  6. Ahmed Abdulhamid Mahmoud
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Contributions

HEE: Conceptualization, original draft writing—review and editing, program running. SYC: Data collection in the field, writing—review and editing, supervision. VS: Data curation and editing. SS: review and editing. NP: Data validation and editing. AAM: Data validation, review, and program running.

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Correspondence to Sang Yong Chung.

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Responsible Editor: Xianliang Yi

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Elzain, H.E., Chung, S.Y., Senapathi, V. et al. Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms. Environ Sci Pollut Res 28, 57030–57045 (2021). https://doi.org/10.1007/s11356-021-14522-0

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