Machine learning predictive insight of water pollution and groundwater quality in the Eastern Province of Saudi Arabia.
Eastern Province
Environmental monitoring
Groundwater quality
Machine learning
Saudi Arabia
Water pollution
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 Aug 2024
28 Aug 2024
Historique:
received:
27
03
2024
accepted:
19
08
2024
medline:
31
8
2024
pubmed:
31
8
2024
entrez:
28
8
2024
Statut:
epublish
Résumé
This study presents an innovative approach for predicting water and groundwater quality indices (WQI and GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges of scarcity and pollution in arid regions. Recent literature highlights the increasing attention towards WQI based on water pollution index (WPI) and GWQI as essential tools for simplifying complex hydrogeological data, thereby facilitating effective groundwater management and protection. Unlike previous works, the present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) algorithms. This approach marks the first application of a non-parametric kernel for groundwater quality pollution index prediction in Saudi Arabia, offering a significant advancement in the field. Through laboratory analysis and the combination of various machine learning (ML) techniques, this study enhances prediction capabilities, particularly for unmonitored sites in arid and semi-arid regions. The study's objectives include feature engineering based on dependency sensitivity analysis to identify the most influential variables affecting WQI and GWQI, and the development of predictive models using ANFIS, GPR, and DT for both indices. Furthermore, it aims to assess the impact of different data portions on WQI and GWQI predictions, exploring data divisions such as (70% / 30%), (60% / 40%), and (80% / 20%) for training and testing phase, respectively. By filling a critical gap in water resource management, this research offers significant implications for the prediction of water quality in regions facing similar environmental challenges. Through its innovative methodology and comprehensive analysis, this study contributes to the broader effort of managing and protecting water resources in arid and semi-arid areas. The result proved that GPR-M1 exhibited exceptional testing phase accuracy with RMSE = 0.0169 for GWQI. Similarly, for WPI, the ANFIS-M1 achieved high testing predictive skills with RMSE = 0.0401. The results emphasize the critical role of data quality and quantity in training for enhancing model robustness and prediction precision in water quality assessment.
Identifiants
pubmed: 39198674
doi: 10.1038/s41598-024-70610-4
pii: 10.1038/s41598-024-70610-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20031Informations de copyright
© 2024. The Author(s).
Références
El Bilali, A. & Taleb, A. Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. J. Saudi Soc. Agric. Sci. 19, 439–451 (2020).
Busico, G. et al. A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses. Water Res. 171, 115386 (2020).
pubmed: 31865127
doi: 10.1016/j.watres.2019.115386
Li, D. Quantifying water use and groundwater recharge under flood irrigation in an arid Oasis of Northwestern China. Agric. Water Manag. 240, 106326 (2020).
doi: 10.1016/j.agwat.2020.106326
Batarseh, M. et al. Dataset for the physio-chemical Parameters of Groundwater in the Emirate of Abu Dhabi, UAE. Data Br. 38, 107353 (2021).
doi: 10.1016/j.dib.2021.107353
Zimit, A. Y., Jibril, M. M., Azimi, M. S. & Abba, S. I. Hybrid predictive based control of precipitation in a water-scarce region: A focus on the application of intelligent learning for green irrigation in agriculture sector. J. Saudi Soc. Agric. Sci. https://doi.org/10.1016/j.jssas.2023.06.001 (2023).
doi: 10.1016/j.jssas.2023.06.001
Bwambale, E., Abagale, F. K. & Anornu, G. K. Data-driven model predictive control for precision irrigation management. Smart Agric. Technol. 3, 100074 (2023).
doi: 10.1016/j.atech.2022.100074
Emmanuel, A. et al. A review on monitoring and advanced control strategies for precision irrigation. Comput. Electron. Agric. 173, 105441 (2020).
doi: 10.1016/j.compag.2020.105441
Simões, F., Moreira, A., Bisinoti, M., Gimenez, S. & Santos Yabe, M. . Water quality index as a simple indicator of aquaculture effects on aquatic bodies. Ecol. Indic. 8, 476–484 (2008).
doi: 10.1016/j.ecolind.2007.05.002
Zhu, M. et al. A review of the application of machine learning in water quality evaluation. Eco-Environ. Health 1, 107–116 (2022).
pubmed: 38075524
pmcid: 10702893
doi: 10.1016/j.eehl.2022.06.001
Liu, Y., Zhao, T., Ju, W. & Shi, S. Materials discovery and design using machine learning. J. Mater. 3, 159–177 (2017).
Abba, S. I. et al. Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index. Environ. Sci. Pollut Res. 27, 41524–41539 (2020).
doi: 10.1007/s11356-020-09689-x
Islam, S., Rasul, T., Alam, J., Bin, & Haque, M. A. Evaluation of water quality of the Titas river using NSF water quality index. J. Sci. Res. 3, 151 (2010).
doi: 10.3329/jsr.v3i1.6170
Tiyasha, T., Tung, T. M. & Yaseen, Z. M. Deep learning for prediction of water quality index classification: Tropical catchment environmental assessment. Nat. Resour. Res. 30, 1–20 (2021).
doi: 10.1007/s11053-021-09922-5
Gaya, M. S. et al. Estimation of water quality index using artificial intelligence approaches and multi-linear regression. IAES Int. J. Artif. Intell. 9, 126–134 (2020).
Gaya, M. S. et al. Estimation of Water Quality Index Using Artificial Intelligence Approaches and multi-linear Regression. 9, 126–134 (2020).
Al-Sulttani, A. O. et al. Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access 9, 108527–108541 (2021).
doi: 10.1109/ACCESS.2021.3100490
Alrowais, R. et al. Groundwater Quality assessment for drinking and irrigation purposes at Al-Jouf area in KSA using artificial neural network, GIS, and multivariate statistical techniques. Water 15, 2982 (2023).
doi: 10.3390/w15162982
Abdul Karim, S. A. & Kamsani, N. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia. (2020). https://doi.org/10.1007/978-981-15-3485-0
Kouadri, S., Elbeltagi, A., Islam, A. R. M. & Kateb, S. Performance of machine learning methods in predicting water quality index based on irregular data set: Application on Illizi Region (Algerian Southeast). Appl. Water Sci. 11, 1–20 (2021).
doi: 10.1007/s13201-021-01528-9
Machiwal, D., Jha, M. K., Singh, V. P. & Mohan, C. Assessment and mapping of groundwater vulnerability to pollution: Current status and challenges. Earth Sci. Rev. 185, 901–927 (2018).
doi: 10.1016/j.earscirev.2018.08.009
Sánchez, E. et al. Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecol. Indic. 7, 315–328 (2007).
doi: 10.1016/j.ecolind.2006.02.005
Singh, G., Singh, J., Wani, O. A., Egbueri, J. C. & Agbasi, J. C. Assessment of groundwater suitability for sustainable irrigation: a comprehensive study using indexical, statistical, and machine learning approaches. Groundw. Sustain. Dev. 24, 101059 (2024).
doi: 10.1016/j.gsd.2023.101059
Uddin, M. G., Nash, S., Rahman, A. & Olbert, A. I. Performance analysis of the water quality index model for predicting water state using machine learning techniques. Process. Saf. Environ. Prot. 169, 808–828 (2023).
doi: 10.1016/j.psep.2022.11.073
Li, Z., Liu, H., Zhang, C. & Fu, G. Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data. Water Res. 250, 121018 (2024).
pubmed: 38113592
doi: 10.1016/j.watres.2023.121018
Aldrees, A., Khan, M., Taha, A. T. B. & Ali, M. Evaluation of water quality indexes with novel machine learning and shapley additive explanation (SHAP) approaches. J. Water Process. Eng. 58, 104789 (2024).
doi: 10.1016/j.jwpe.2024.104789
Ding, F. et al. Optimization of water quality index models using machine learning approaches. Water Res. 243, 120337 (2023).
pubmed: 37473509
doi: 10.1016/j.watres.2023.120337
Raheja, H., Goel, A. & Pal, M. Evaluation of groundwater quality for drinking purposes based on machine learning algorithms and GIS. Sustain. Water Resour. Manag. 10, 11 (2024).
doi: 10.1007/s40899-023-00990-4
Sajib, A. M. et al. Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach. Groundw. Sustain. Dev. 23, 101049 (2023).
doi: 10.1016/j.gsd.2023.101049
Singha, S., Pasupuleti, S., Singha, S. S., Singh, R. & Kumar, S. Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276, 130265 (2021).
pubmed: 34088106
doi: 10.1016/j.chemosphere.2021.130265
Kazmi, Z. A. & Sodangi, M. Integrated analysis of the geotechnical factors impeding sustainable building construction—The case of the eastern province of Saudi Arabia. Sustain 13, 6531 (2021).
doi: 10.3390/su13126531
Anton, D. Modern eolian deposits of the eastern province of Saudi Arabia. In Developments in Sedimentology vol. 38 365–378 (Elsevier, 1983).
Benaafi, M. & Abdullatif, O. Sedimentological, mineralogical, and geochemical characterization of sand dunes in Saudi Arabia. Arab. J. Geosci. 8, 11073–11092 (2015).
doi: 10.1007/s12517-015-1970-9
Yassin, M. A., Usman, A. G., Abba, S. I., Ozsahin, D. U. & Aljundi, I. H. Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia. Results Eng. 20, 101434 (2023).
doi: 10.1016/j.rineng.2023.101434
Al-Naeem, A. Evaluation of groundwater of Al-Hassa Oasis, Eastern Region Saudi Arabia. Res. J. Environ. Sci. 5, 624–642 (2011).
doi: 10.3923/rjes.2011.624.642
Alhawas, I. & Hassaballa, A. A. Representation of the spatial association between salinity and water chemical properties in Al-Hassa Oasis. Int. J. Agric. Biol. Eng. 13, 168–174 (2020).
Buragohain, M. & Mahanta, C. A novel approach for ANFIS modelling based on full factorial design. Appl. Soft Comput. J. 8, 609–625 (2008).
doi: 10.1016/j.asoc.2007.03.010
Kisi, O. et al. Modeling groundwater quality parameters using hybrid neuro-fuzzy methods. Water Resour. Manag. 33, 847–861 (2019).
doi: 10.1007/s11269-018-2147-6
Chen, W. H. et al. Data-driven robust model predictive control framework for stem water potential regulation and irrigation in water management. Control Eng. Pract. 113, 104841 (2021).
doi: 10.1016/j.conengprac.2021.104841
Fang, S., Hu, R., Yuan, X., Liu, S. & Zhang, Y. Resolution enhancement for lung 4D-CT based on transversal structures by using multiple gaussian process regression learning. Phys. Med. 78, 187–194 (2020).
pubmed: 33038644
doi: 10.1016/j.ejmp.2020.09.011
Yoo, K., Shukla, S. K., Ahn, J. J., Oh, K. & Park, J. Decision tree-based data Mining and rule Induction for Identifying Hydrogeological Parameters that Influence Groundwater Pollution Sensitivity. J. Clean. Prod.122, 277–286 (2016).
doi: 10.1016/j.jclepro.2016.01.075
Abba, S. I. et al. Drinking water resources suitability assessment based on pollution index of groundwater using improved explainable artificial intelligence. Sustainability 15, 15655 (2023).
doi: 10.3390/su152115655
Yaseen, Z. M. The next generation of soil and water bodies heavy metals prediction and detection: New expert system based edge cloud server and federated learning technology. Environ. Pollut. 313, 120081 (2022).
pubmed: 36075340
doi: 10.1016/j.envpol.2022.120081
Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. Hydrologic and water quality models: Performance measures and evaluation criteria. Trans. ASABE 58, 1763–1785 (2015).
doi: 10.13031/trans.58.10715
Lu, H. & Ma, X. Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249, 126169 (2020).
pubmed: 32078849
doi: 10.1016/j.chemosphere.2020.126169
Abba, S. I., Benaafi, M., Usman, A. G. & Aljundi, I. H. Inverse groundwater salinization modeling in a Sandstone’s aquifer using stand-alone models with an improved non-linear ensemble machine learning technique. J. King Saud Univ. Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2022.08.002 (2022).
doi: 10.1016/j.jksuci.2022.08.002
Baig, N., Abba, S. I., Usman, J., Benaafi, M. & Aljundi, I. H. Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane. Environ. Sci. Adv. 2, 1446–1459 (2023).
doi: 10.1039/D3VA00124E
Usman, J. et al. Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory. Chemosphere 331, 138726 (2023).
pubmed: 37116721
doi: 10.1016/j.chemosphere.2023.138726