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Four Optimization Meta-heuristic Approaches in Evaluating Groundwater Quality (Case study: Shiraz Plain)

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Abstract

Estimating and predicting groundwater quality characteristics so that managers may make management decisions is one of the critical goals of water resource planners and managers. The complexity of groundwater networks makes it difficult to predict either the time or the location of groundwater. Many models have been created in this area, offering better management to preserve water quality. Most of these models call for input parameters that are either seldom accessible or expensively and laboriously measured. A better option among them is the Artificial Neural Network (ANN) Model, which draws inspiration from the human brain. This study uses Na + , Mg2 + , Ca2 + , Na%, K + , SO42 − , Cl − , pH, and HCO3 − quality parameters to estimate the Sodium Adsorption Ratio (SAR). The Shiraz Plain's groundwater quality was simulated using four optimization meta-heuristic methods, including biography-based optimization (BBO), black hole attack (BHA), sequential forward selection (SFS), and multi-verse optimization (MVO). These methods excel in adaptability, convergence speed, feature selection, diversity of solutions, and robustness to complex and noisy datasets, ultimately leading to more accurate and efficient predictive models than earlier methods. A statistical period of 16 years (2002–2018) was used to collect the groundwater quality data for the Shiraz plain to accomplish this purpose. The findings showed that the SFS-MLP approach was more accurate than the other methods with training and testing dataset values of R2 = 0.9996 and 0.99923, RMSE = 0.04929 and 0.072, and MAE = 0.039357 and 0.048968, respectively. Additionally, the findings demonstrated that the SFS-MLP approach has a high capacity and accuracy for predicting and modeling groundwater quality. This study's findings also show that intelligence models and optimization algorithms may be used to mimic groundwater quality parameters effectively.

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Data Availability

The data supporting this study's findings are available from the corresponding author upon reasonable request.

References

  • Adimalla N, Chen J, Qian H (2020) Spatial characteristics of heavy metal contamination and potential human health risk assessment of urban soils: a case study from an urban region of South India. Ecotoxicol Environ Saf 194:110406

    Article  Google Scholar 

  • Ahmadi Dehrashid A, Dong H, Fatahizadeh M, Gholizadeh Touchaei H, Gör M, Moayedi H, Salari M, Thi QT (2024) A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-024-02690-7

    Article  Google Scholar 

  • Alfarrah N, Walraevens K (2018) Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions. Water 10(2):143

    Article  Google Scholar 

  • Al-Shurman M, Yoo SM, Park S (2004) Black hole attack in mobile ad hoc networks. Proceedings of the 42nd annual Southeast regional conference.

  • Badeenezhad A, Tabatabaee HR, Nikbakht H-A, Radfard M, Abbasnia A, Baghapour MA, Alhamd M (2020) Estimation of the groundwater quality index and investigation of the affecting factors their changes in Shiraz drinking groundwater. Iran Groundwater Sustain Develop 11:100435

    Article  Google Scholar 

  • Barbieri M, Ricolfi L, Vitale S, Muteto PV, Nigro A, Sappa G (2019) Assessment of groundwater quality in the buffer zone of limpopo national park, gaza province, southern Mozambique. Environ Sci Pollut Res 26:62–77

    Article  Google Scholar 

  • Cherkaoui B, Beni-hssane A, Erritali M (2020) Variable control chart for detecting black hole attack in vehicular ad-hoc networks. J Ambient Intell Humaniz Comput 11:5129–5138

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Bobée B (1999) Prévision hydrologique par réseaux de neurones artificiels: état de l’art. Can J Civ Eng 26(3):293–304

    Article  Google Scholar 

  • Dai W (2021) Safety evaluation of traffic system with historical data based on Markov process and deep-reinforcement learning. J Comput Method Eng Appl 1(1):1–14

    Google Scholar 

  • Dai H, Ju J, Gui D, Zhu Y, Ye M, Cui J, Hu BX (2024a) A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling. J Hydrology. https://doi.org/10.1016/j.jhydrol.2024.130903

    Article  Google Scholar 

  • Dai H, Liu Y, Guadagnini A, Yuan S, Yang J, Ye M (2024b) Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty. Water Resour Res 60(2):e2023WR0360976

    Article  Google Scholar 

  • Dai W (2022) Evaluation and improvement of carrying capacity of a traffic system. Innov Appl Eng Technol https://doi.org/10.58195/iaet.v1i1.001

  • Dai W (2023) Design of traffic improvement plan for line 1 Baijiahu station of Nan**g metro. Innov Appl Eng Technol https://doi.org/10.58195/iaet.v2i1.133

  • Deng H, Li W, Agrawal DP (2002) Routing security in wireless ad hoc networks. IEEE Commun Mag 40(10):70–75

    Article  Google Scholar 

  • El Bilali A, Taleb A (2020) Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. J Saudi Soc Agric Sci 19(7):439–451. https://doi.org/10.1016/j.jssas.2020.08.001

    Article  Google Scholar 

  • El Bilali A, Taleb A, Brouziyne Y (2021a) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manage 245:106625. https://doi.org/10.1016/j.agwat.2020.106625

    Article  Google Scholar 

  • El Bilali A, Taleb A, Nafii A, Alabjah B, Mazigh N (2021b) Prediction of sodium adsorption ratio and chloride concentration in a coastal aquifer under seawater intrusion using machine learning models. Environ Technol Innov 23:101641. https://doi.org/10.1016/j.eti.2021.101641

    Article  Google Scholar 

  • Fu Y, Zhou M, Guo X, Qi L (2019) Artificial-molecule-based chemical reaction optimization for flow shop scheduling problem with deteriorating and learning effects. Ieee Access 7:53429–53440

    Article  Google Scholar 

  • Haggerty R, Sun J, Yu H, Li Y (2023) Application of machine learning in groundwater quality modeling-A comprehensive review. Water Res 233:119745

    Article  Google Scholar 

  • Ibrahim H, Yaseen ZM, Scholz M, Ali M, Gad M, Elsayed S, Khadr M, Hussein H, Ibrahim HH, Eid MH (2023) Evaluation and prediction of groundwater quality for irrigation using an integrated water quality indices, machine learning models and GIS approaches: a representative case study. Water 15(4):694

    Article  Google Scholar 

  • Jha MK, Shekhar A, Jenifer MA (2020) Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Res 179:115867

    Article  Google Scholar 

  • Jia B, Zhou G (2023) (2023) Estimation of global karst carbon sink from 1950s to 2050s using response surface methodology. Geo-Spatial Inform Sci. https://doi.org/10.1080/10095020.2023.2165974

    Article  Google Scholar 

  • Joseph VR (2022) Optimal ratio for data splitting. Stat Anal Data Mining: ASA Data Sci J 15(4):531–538

    Article  MathSciNet  Google Scholar 

  • Kang Q, Feng S, Zhou M, Ammari AC, Sedraoui K (2017) Optimal load scheduling of plug-in hybrid electric vehicles via weight-aggregation multi-objective evolutionary algorithms. IEEE Trans Intell Transp Syst 18(9):2557–2568

    Article  Google Scholar 

  • Khan N, Malik A, Nehra K (2021) Groundwater hydro-geochemistry, quality, microbiology and human health risk assessment in semi-arid area of Rajasthan, India: a chemometric approach. Environ Monit Assess 193:1–36

    Article  Google Scholar 

  • Khashei-Siuki A, Kouchkzadeh M, Ghahraman B (2011) Predicting dryland wheat yield from meteorological data using expert system, Khorasan Province. Iran J Agric Sci Technol 13(4):627–640

    Google Scholar 

  • Kong C, Li H, Zhang L, Zhu H, Liu T (2019) Link prediction on dynamic heterogeneous information networks. International Conference on Computational Data and Social Networks.

  • Kong C, Li H, Zhu H, **u Y, Liu J, Liu T (2019). Anonymized user linkage under differential privacy. Soft Computing in Data Science: 5th International Conference, SCDS 2019, Iizuka, Japan, August 28–29, 2019, Proceedings 5.

  • Kong C, Liu J, Li H, Liu Y, Zhu H, Liu T (2019) Drug abuse detection via broad learning. Web Information Systems and Applications: 16th International Conference, WISA 2019, Qingdao, China, September 20–22, 2019, Proceedings 16,

  • Kong C, Zhu H, Li H, Liu J, Wang Z, Qian Y (2019) Multi-agent negotiation in real-time bidding. 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

  • Kumar S, Bharti V, Singh K, Singh T (2010) Quality assessment of potable water in the town of Kolasib, Mizoram (India). Environ Earth Sci 61:115–121

    Article  Google Scholar 

  • Kumar R, Singh S, Kumar R, Sharma P (2022) Groundwater quality characterization for safe drinking water supply in sheikhpura district of Bihar, India: a geospatial approach. Front Water 4:848018

    Article  Google Scholar 

  • Li J, Pang Z, Liu Y, Hu S, Jiang W, Tian L, Yang G, Jiang Y, Jiao X, Tian J (2023) Changes in groundwater dynamics and geochemical evolution induced by drainage reorganization: evidence from 81Kr and 36Cl dating of geothermal water in the Weihe Basin of China. Earth Planet Sci Lett 623:118425

    Article  Google Scholar 

  • Ma H (2010) An analysis of the equilibrium of migration models for biogeography-based optimization. Inf Sci 180(18):3444–3464

    Article  Google Scholar 

  • MacArthur RH, Wilson EO (2001) The theory of island biogeography (Vol. 1). Princeton university press.

  • Masoudi R, Mousavi SR, Rahimabadi PD, Panahi M, Rahmani A (2023) Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard. Environ Monit Assess 195(2):319

    Article  Google Scholar 

  • Meng X, Li J, Zhou M, Dai X, Dou J (2016) Population-based incremental learning algorithm for a serial colored traveling salesman problem. IEEE Trans Syst, Man, Cyber: Syst 48(2):277–288

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  • Mishra A, Nadkarni K, Patcha A (2004) Intrusion detection in wireless ad hoc networks. IEEE Wirel Commun 11(1):48–60

    Article  Google Scholar 

  • Moayedi H, Dehrashid AA (2023) A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility map**. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-28133-4

    Article  Google Scholar 

  • Moayedi H, Canatalay PJ, Ahmadi Dehrashid A, Cifci MA, Salari M, Le BN (2023a) Multilayer perceptron and their comparison with two nature-inspired hybrid techniques of biogeography-based optimization (BBO) and backtracking search algorithm (BSA) for assessment of landslide susceptibility. Land 12(1):242

    Article  Google Scholar 

  • Moayedi H, Salari M, Dehrashid AA, Le BN (2023b) Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-023-02429-w

    Article  Google Scholar 

  • Mohammed MA, Kaya F, Mohamed A, Alarifi SS, Abdelrady A, Keshavarzi A, Szabó NP, Szűcs P (2023) Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices. Front Earth Sci 11:1274142

    Article  Google Scholar 

  • Naghibi SA, Khodaei B, Hashemi H (2022) An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas. J Hydrol 608:127627. https://doi.org/10.1016/j.jhydrol.2022.127627

    Article  Google Scholar 

  • Organization WH (2006) The Guidelines: a framework for safe drinking water. Guidelines for Drinking-water Quality. 3rd ed. Geneva; WHO, 22–36.

  • Qiu Y, Wang J (2024). A machine learning approach to credit card customer segmentation for economic stability. Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tian**, China.

  • Qiu Y (2019) Estimation of tail risk measures in finance: approaches to extreme value mixture modeling Johns Hopkins university.

  • Rácz A, Bajusz D, Héberger K (2021) Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification. Molecules 26(4):1111

    Article  Google Scholar 

  • Raschka S, Mirjalili V (2017). Python machine learning: Machine learning and deep learning with python. Scikit-Learn, and TensorFlow. Second edition ed, 3.

  • Raschka S, Mirjalili V (2019). Python machine learning: Machine learning and deep learning with python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.

  • Salami E, Salari M, Ehteshami M, Bidokhti N, Ghadimi H (2016) Application of artificial neural networks and mathematical modeling for the prediction of water quality variables (case study: southwest of Iran). Desalin Water Treat 57(56):27073–27084

    Article  Google Scholar 

  • Salari M, Rakhshandehroo G, Ehetshami M (2017) Investigating the spatial variability of some important groundwater quality factors based on the geostatistical simulation (case study: Shiraz plain). Desalin Water Treat 65:163–174

    Article  Google Scholar 

  • Salari M, Shahid ES, Afzali SH, Ehteshami M, Conti GO, Derakhshan Z, Sheibani SN (2018) Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water. Food Chem Toxicol 118:212–219

    Article  Google Scholar 

  • Shafiee S, Lied LM, Burud I, Dieseth JA, Alsheikh M, Lillemo M (2021) Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Comput Electron Agric 183:106036

    Article  Google Scholar 

  • Shahida ES, Salarib M, Rastegarc M, Nikbakht S, Sheibania ME (2021) Artificial neural network and mathematical approach for estimation of surface water quality parameters (case study: California, USA). Sat 100:2

    Google Scholar 

  • Sharma, Y., Mukherjee, A., Srivastava, J., Mahato, M., & Singh, T. (2008). Prediction of various parameters of a river for assessment of water quality by an intelligent technique. Chemical Product and Process Modeling, 3(1).

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Singh G, Rishi MS, Herojeet R, Kaur L, Sharma K (2020) Evaluation of groundwater quality and human health risks from fluoride and nitrate in semi-arid region of northern India. Environ Geochem Health 42:1833–1862

    Article  Google Scholar 

  • Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265

    Article  Google Scholar 

  • Song N, Qian L, Li X (2005) Wormhole attacks detection in wireless ad hoc networks: A statistical analysis approach. 19th IEEE international parallel and distributed processing symposium.

  • Sun B, Guan Y, Chen J, Pooch UW (2003) Detecting black-hole attack in mobile ad hoc networks.

  • Tian G, Hao N, Zhou M, Pedrycz W, Zhang C, Ma F, Li Z (2019) Fuzzy grey choquet integral for evaluation of multicriteria decision making problems with interactive and qualitative indices. IEEE Trans Syst, Man, Cyber: Syst 51(3):1855–1868

    Google Scholar 

  • Tung TM, Yaseen ZM (2020) A survey on river water quality modelling using artificial intelligence models: 2000–2020. J Hydrol 585:124670

    Article  Google Scholar 

  • Verma A, Singh T (2013) Prediction of water quality from simple field parameters. Environ Earth Sci 69:821–829

    Article  Google Scholar 

  • Wang XP, Huang YS (2008) Predicting risks of capital flow using artificial neural network and levenberg marquardt algorithm. 2008 International Conference on Machine Learning and Cybernetics.

  • Wenjun D, Fatahizadeh M, Touchaei HG, Moayedi H, Foong LK (2023) Application of six neural network-based solutions on bearing capacity of shallow footing on double-layer soils. Steel Compos Struct 49(2):231–244

    Google Scholar 

  • **ng K, Han L, Zhou M, Wang F (2011) Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy. IEEE Trans Syst, Man, Cyber, Part B Cyber 42(3):603–615

    Article  Google Scholar 

  • Zhang K, Li Y, Yu Z, Yang T, Xu J, Chao L, Ni J, Wang L, Gao Y, Hu Y (2022) **n’anjiang nested experimental watershed (XAJ-NEW) for understanding multiscale water cycle: scientific objectives and experimental design. Engineering 18:207–217

    Article  Google Scholar 

  • Zhao J, Liu S, Zhou M, Guo X, Qi L (2018) Modified cuckoo search algorithm to solve economic power dispatch optimization problems. IEEE/CAA J Auto Sinica 5(4):794–806

    Article  MathSciNet  Google Scholar 

  • Zhao Z, Liu S, Zhou M, Guo X, Qi L (2019) Decomposition method for new single-machine scheduling problems from steel production systems. IEEE Trans Autom Sci Eng 17(3):1376–1387

    Google Scholar 

  • Zhao Y, Dai W, Wang Z, Ragab AE (2024) Application of computer simulation to model transient vibration responses of GPLs reinforced doubly curved concrete panel under instantaneous heating. Mater Today Commun 38:107949

    Article  Google Scholar 

  • Zheng Y-J, Ling H-F, Xue J-Y (2014) Disaster rescue task scheduling: An evolutionary multiobjective optimization approach. IEEE Trans Emerg Top Comput 6(2):288–300

    Article  Google Scholar 

  • Zhou G, Wang Z, Li Q (2022) Spatial negative co-location pattern directional mining algorithm with join-based prevalence. Remote Sens 14(9):2103

    Article  Google Scholar 

  • Zhou G, Su S, Xu J, Tian Z, Cao Q (2023) Bathymetry retrieval from spaceborne multispectral subsurface reflectance. IEEE J Select Topics Appl Earth Observ Remote Sens 16:2547–2558

    Article  Google Scholar 

  • Zhu H, Wang B (2021). Negative siamese network for classifying semantically similar sentences. 2021 International Conference on Asian Language Processing (IALP).

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Acknowledgements

We would like to thank all the participants, without whom this study would be impossible.

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Authors and Affiliations

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

    Hossein Moayedi

  2. School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  3. Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran

    Marjan Salari

  4. School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam

    T. N. G. Nguyen

  5. Department of Climatology Faculty of Natural Resources, , University of Kurdistan, Sanandaj, Iran

    Atefeh Ahmadi Dehrashid

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  1. Hossein Moayedi
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Contributions

Hossein Moayedi; Conceptualization, methodology, writing-original draft preparation; Marjan Salari: Investigation, visualization, writing-original draft preparation, TNG Nguyen; Visualization, Formal analysis, Atefeh Ahmadi Dehrashid; Writing-review and editing. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Marjan Salari.

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Moayedi, H., Salari, M., Nguyen, T.N.G. et al. Four Optimization Meta-heuristic Approaches in Evaluating Groundwater Quality (Case study: Shiraz Plain). Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01501-x

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