Modeling of Groundwater Quality Index Using Multilayer Perceptron (MLP) Neural Network in Al-Mahawil District, Iraq

Fatima Abed Alabass  Alhusaini; Kadhim  Naief Kadhim; Hadeel  Ali Abdulhussein  Al Saleh

doi:10.30572/2018/KJE/170335

Authors

Fatima Abed Alabass Alhusaini Department of Civil Engineering, College of Engineering, University of Babylon, Babylon, Iraq
Kadhim Naief Kadhim Department of Civil Engineering, College of Engineering, University of Babylon, Babylon, Iraq
Hadeel Ali Abdulhussein Al Saleh Department of Civil Engineering, College of Engineering, University of Babylon, Babylon, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170335

Keywords:

Groundwater Quality Index (GWQI), Artificial Intelligence, Multilayer Perceptron (MLP), Neural Networks, Prediction, MATLAB

Abstract

Monitoring water quality is essential for environmental protection and sustainable water resource management. This study employed a Multilayer Perceptron (MLP) neural network to model the Groundwater Quality Index (GWQI) based on key physicochemical parameters. The model was developed using MATLAB’s Neural Network Toolbox (nftool), with data divided into training, validation, and testing sets. Performance was evaluated using MSE, RMSE, MAE, and the correlation coefficient (R). The MLP model achieved good predictive accuracy:(MSE = 2.3, R ≈ 1.0) in training;( MSE = 7.6983, R = 0.99992) in validation; and (MSE = 15.8, R = 0.997) in testing. These results confirm the model’s ability to capture nonlinear relationships and generalize well without overfitting. The stability of the model is supported by its error distribution and training convergence. A comparison with previous studies shows improved performance, reinforcing the potential of neural networks in predicting water quality and supporting data-driven environmental decision-making

Downloads

Download data is not yet available.

References

Abdelhadi, A., Zainudin, S. and Sani, N.S. (2022) 'A regression model to predict key performance indicators in higher education enrollments', International Journal of Advanced Computer Science and Applications, 13(1) https://doi.org/10.14569/ijacsa.2022.0130156.

Ahmad, W., Zubair, A., Abbasi, H.N. and Nasir, M.I. (2021) 'Water study of physical, chemical and heavy metals parameters in River Indus and its tributaries, Sindh, Pakistan', Pakistan Journal of Scientific & Industrial Research Series A: Physical Sciences, 64(2), pp. 103-109. https://doi.org/10.52763/PJSIR.PHYS.SCI.64.2.2021.103.109.

Al-Aarajy, K.H., Sultan, M.A. and Hassan, Z.H. (2024) 'Determination of the groundwater potential zones in Babylon using remote sensing and GIS techniques', The Iraqi Geological Journal, 31 March, pp. 261-275. https://doi.org/10.46717/igj.57.1c.17ms-2024-3-29.

Al-Hessnawi, J.K., Jawad, Z.S. and Fadhil, F.A. (2018) 'Spatial analysis of natural properties in Mahaweel District', Journal of University of Babylon, 26(8).

Al-Saleh, H. (2021) 'Applying ANN-based PSO algorithm for the prediction of DO and PO4 in Al-Hillah River', International Review of Civil Engineering (IRECE)*, 12(6), pp. 371-381. https://doi.org/10.15866/irece.v12i6.20092.

Al Saleh, H.A. (2021) 'Comparison study of water quality produced from a conventional water treatment plant and a number of compact units', Journal of Ecological Engineering, 22(5), pp. 258-262. https://doi.org/10.12911/22998993/135864.

Al Saleh, H.A. and Nehaba, S.S. (2024) 'Evaluating groundwater vulnerability and assessing its quality for sustainable management', South African Journal of Chemical Engineering, 50, pp. 291-298. https://doi.org/10.1016/j.sajce.2024.09.001.

Andrade Foronda, D. and Colinet, G. (2023) 'Prediction of soil salinity/sodicity and salt-affected soil classes from soluble salt ions using machine learning algorithms', Soil Systems, 7(2), Article 47. https://doi.org/10.3390/soilsystems7020047.

Borgohain, L., Gogoi, M., Dutta, J. and Medhi, S. (2025) 'Emerging trends and technologies for conservation and sustainable approach in groundwater management', in Emerging Trends and Technologies in Water Management and Conservation*, pp. 175-202. https://doi.org/10.4018/979-8-3693-6920-3.ch006.

Chabuk, A., Jahad, U.A., Majdi, A., Isam, M., Al-Ansari, N., Majdi, H., Laue, J. and Abed, S.A. (2022) 'Creating the distribution map of groundwater for drinking uses using physio-chemical variables; Case study: Al-Hilla City, Iraq', Water, Air, & Soil Pollution, 233(6), 218. https://doi.org/10.1007/s11270-022-05660-3.

Chai, Y., Xiao, C., Li, M. and Liang, X. (2020) 'Hydrogeochemical characteristics and groundwater quality evaluation based on multivariate statistical analysis', Water, 12(10), Article 2792. https://doi.org/10.3390/W12102792.

Dawood, A.S., Jabbar, M.T., Al-Tameemi, H.J. and Baer, E.M.D. (2022) 'Application of water quality index and multivariate statistical techniques to assess and predict groundwater quality with aid of geographic information system', Journal of Ecological Engineering, 23(6), pp. 189-204. https://doi.org/10.12911/22998993/148195.

El Yousfi, Y., Himi, M., El Ouarghi, H., Aqnouy, M., Benyoussef, S., Gueddari, H., Ait Hmeid, H., Alitane, A., Chaibi, M., Zahid, M., Essahlaoui, N., Hitouri, S., Essahlaoui, A. and Elaaraj, A. (2022) 'Assessment and prediction of the water quality index for the groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco)', Sustainability, 15(1), 402. https://doi.org/10.3390/su15010402.

Fu, X., Dong, Z., Gan, S., Wang, Z. and Wei, A. (2021) 'Groundwater quality evaluation for potable use and associated human health risk in Gaobeidian City, North China Plain', Journal of Chemistry, 2021(1), Article ID 3008567. https://doi.org/10.1155/2021/3008567.

Gemail, K.S. and Abd-Elaty, I. (2023) 'Unveiling the hidden depths: A review for understanding and managing groundwater contamination in arid regions', Groundwater Quality and Geochemistry in Arid and Semi-Arid Regions, 15 December, pp. 3-5. https://doi.org/10.1007/698_2023_1049.

Ibrahim, M.A., Mohammed-Ridha, M.J., Hussein, H.A. and Faisal, A.A. (2020) 'Artificial neural network modeling of the water quality index for the Euphrates river in Iraq', *The Iraqi Journal of Agricultural Science, 51(6), pp. 1572-1580. https://doi.org/10.36103/IJAS.V51I6.1184.

Kadhim, K.N. and Noor, A. (2018) 'Geospatial technology for groundwater quality parameters assessment in Al-Kifl District, Babylon-Iraq'.

Karuppannan, S. and Kawo, N.S. (2019) 'Groundwater quality assessment using geospatial techniques and WQI in north east of Adama Town, Oromia region, Ethiopia', Hydrosphere Analysis, 3(1), pp. 22-36. https://doi.org/10.21523/GCJ3.19030103.

Kurwadkar, S., Kanel, S.R. and Nakarmi, A. (2020) 'Groundwater pollution: Occurrence, detection, and remediation of organic and inorganic pollutants', Water Environment Research, 92(10), pp. 1659-1668. https://doi.org/10.1002/WER.1415.

Miller, T., Cembrowska-Lech, D., Kisiel, A. and Krzemińska, A. (2023) 'Advancing water quality monitoring through artificial neural networks: Present insights and future opportunities in scientific exploration', Inter Conf, 32(151), pp. 399-409. https://doi.org/10.51582/interconf.19-20.04.2023.041.

Mohammed, M.A., Khleel, N.A., Szabó, N.P. and Szűcs, P. (2023) 'Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan', Modeling Earth Systems and Environment, 9(2), pp. 2501-2516. https://doi.org/10.1007/s40808-022-01638-6.

Müller, J., Park, J., Sahu, R.K., Varadharajan, C., Arora, B., Faybishenko, B. and Agarwal, D. (2021) 'Surrogate optimization of deep neural networks for groundwater predictions', Journal of Global Optimization, 81(1), pp. 203-231. https://doi.org/10.1007/S10898-020-00912-0.

Nadiri, A.A., Barzegar, R., Sadeghfam, S. and Rostami, A.A. (2022) 'Developing a data-fused water quality index based on artificial intelligence models to mitigate conflicts between GQI and GWQI', Water, 14(19), 3185. https://doi.org/10.3390/w14193185.

Qiu, X. (2023) 'The application of artificial intelligence - artificial neural networks - in wastewater treatment', E3S Web of Conferences, 393, 03003. https://doi.org/10.1051/e3sconf/202339303003.

Rashid, A., Ayub, M., Ullah, Z., Ali, A., Sardar, T., Iqbal, J., Gao, X., Bundschuh, J., Li, C., Khattak, S.A. and Ali, L. (2023) 'Groundwater quality, health risk assessment, and source distribution of heavy metals contamination around chromite mines', International Journal of Environmental Research and Public Health, 20(3), Article 2113. https://doi.org/10.3390/ijerph20032113.

Saadali, R., Dadach, M., Bouguettaya, S. and Benjeddi, S. (2022) 'Evaluation of the groundwater quality in Amizour Plain (North Algeria) through the application of heavy metal pollution index', Advanced Research in Life Sciences, 6(1), pp. 40-47. https://doi.org/10.2478/arls-2022-0037.

Sanad, H., Moussadek, R., Zouahri, A., Oueld Lhaj, M., Mouhir, L. and Dakak, H. (2025) 'AI and machine learning-based spatial modeling of groundwater quality indices and hydrogeochemistry for accurate prediction of seawater intrusion and irrigation sustainability in coastal agroecosystems'. https://doi.org/10.21203/rs.3.rs-7705609/v1.

World Health Organization (2017) Guidelines for drinking-water quality: Fourth edition incorporating the first addendum. Geneva: World Health Organization.

Zhu, Z. (2024) 'Systematic optimization of overfitting problem in machine learning', Highlights in Science, Engineering and Technology, 111, pp. 353-359. https://doi.org/10.54097/3tkzrj84.