Prediction of water quality in Jordanian dams using data mining algorithms.
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| Název: | Prediction of water quality in Jordanian dams using data mining algorithms. |
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| Autoři: | Halalsheh N; Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan E-mail: Neda@hu.edu.jo., Ibrahim M; Department of Geographic Information Systems and Remote Sensing, Faculty of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, Jordan., Al-Shanableh N; Department of Computer Science, Faculty of Prince Al-Hussein Bin Abdallah II for IT, Al al-Bayt University, Mafraq 25113, Jordan., Al-Harahsheh S; Department of Applied Geology and Environmental Sciences, Faculty of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, Jordan., Al-Mashagbah A; Department of Geographic Information Systems and Remote Sensing, Faculty of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq, Jordan. |
| Zdroj: | Water science and technology : a journal of the International Association on Water Pollution Research [Water Sci Technol] 2025 Nov; Vol. 92 (10), pp. 1379-1395. Date of Electronic Publication: 2025 Nov 03. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: IWA Publishing Country of Publication: England NLM ID: 9879497 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0273-1223 (Print) Linking ISSN: 02731223 NLM ISO Abbreviation: Water Sci Technol Subsets: MEDLINE |
| Imprint Name(s): | Publication: <1998->: London : IWA Publishing Original Publication: Oxford ; New York : Pergamon Press, 1981- |
| Výrazy ze slovníku MeSH: | Water Quality* , Data Mining*/methods , Algorithms* , Environmental Monitoring*/methods, Jordan ; Support Vector Machine ; Seasons |
| Abstrakt: | Competing Interests: The authors declare there is no conflict. The evaluation of water quality constitutes a critical aspect of water management strategies, particularly in arid and semi-arid environments, where the use and protection of sustainable resources are crucial. This study focuses on assessing and predicting water quality in three Jordanian dams using advanced data mining techniques. Physical, chemical, and biological water quality parameters were collected and analyzed over a four-year period. The Weighted Arithmetic Water Quality Index (WA-WQI) was used to evaluate the overall water quality. Various data mining algorithms, including generalized linear models, decision trees, random forests, gradient-boosted trees, and support vector machine (SVM), were employed to predict WQI and understand the seasonal and annual variations. Key findings highlight significant fluctuations in water quality, influenced by parameters such as pH, conductivity, nutrients, and microbial contamination. The study emphasizes the importance of continuous monitoring and predictive modeling for effective water resource management. It also demonstrates the effectiveness of using SVM for water quality prediction in arid regions. The models were evaluated using different performance metrics. The SVM outperformed other employed models. This study provides a critical benchmark and a robust predictive framework for water resource management in Jordan and semi-arid areas, addressing a significant gap in regional environmental monitoring. (© 2025 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC 4.0), which permits copying, adaptation and redistribution for non-commercial purposes, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc/4.0/).) |
| References: | Ababneh A., Al-shanableh N. & Alzyoud M. (2021) A review of algorithms and techniques for analyzing big data, International Journal of Emerging Trends in Engineering Research, 9 (6), 695–702. https://doi.org/10.30534/ijeter/2021/14962021. Abba S. I., Pham Q. B., Saini G., Linh N. T. T., Ahmed A. N., Mohajane M., Khaledian M., Abdulkadir R. A. & Bach Q.-V. (2020) Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index, Environmental Science and Pollution Research, 27 (33), 41524–41539. https://doi.org/10.1007/s11356-020-09689-x. (PMID: 32686045) Al-Addous M., Bdour M., Alnaief M., Rabaiah S. & Schweimanns N. (2023) Water resources in Jordan: a review of current challenges and future opportunities, Water (Switzerland), 15 (21), 3729. https://doi.org/10.3390/w15213729. Albatayneh A. (2024) The significance of renewable energy in a water-scarce world: a case study of Jordan, Air, Soil and Water Research, 17, 1–10. https://doi.org/10.1177/11786221241261827. AlBtoosh J., Abu-Awwad A. & Obeidat N. (2024) A statistical approach to the water scarcity implications on food security, Global Journal of Environmental Science and Management, 10 (SI), 201–218. https://doi.org/10.22034/gjesm.2024.10.SI.13. Aslam B., Maqsoom A., Cheema A. L. I. H., Ullah F., Alharbi A. & Imran M. (2022) Water quality management using hybrid machine learning and data mining algorithms : an indexing approach, IEEE Access, 10 (October), 119692–119705. https://doi.org/10.1007/s41748-025-00623-0. Brown R. M., McClelland N. I., Deininger R. A. & Tozer R. G. A. (1970) A-water-quality-index-do-we-dare-BROWN-R-M-1970. In: Water Sewage Works, 10 (117), 339–343. Chadli K. (2023) Assessment of surface water quality in the Sebou watershed (Morocco) using a nonparametric approach and machine learning techniques, Arabian Journal of Geosciences, 16, Article 517. https://doi.org/10.1007/s12517-023-11623-7. Chidiac S., El P., Ouaini N., El Y., Desiree R. & Azzi E. (2023) A comprehensive review of water quality indices (WQIs): History, models, attempts and perspectives. Reviews in Environmental Science and Biotechnology, 22 (2), 349–395. https://doi.org/10.1007/s11157-023-09650-7. (PMID: 37234131) Das A. (2025) Prediction of urban surface water quality scenarios using Water Quality Index (WQI), multivariate techniques, and Machine Learning (ML) models in water resources, in Baitarani River Basin, Odisha: potential benefits and associated challenges, Earth Systems and Environment, 1–37. https://doi.org/10.1007/s41748-025-00623-0. Deng T., Chau K. W. & Duan H. F. (2021) Machine learning based marine water quality prediction for coastal hydro-environment management, Journal of Environmental Management, 284, 112051. https://doi.org/10.1016/j.jenvman.2021.112051. (PMID: 33515839) Dinar A. (2024) Challenges to water resource management: the role of economic and modeling approaches, Water (Switzerland), 16 (4), 610. https://doi.org/10.3390/w16040610. Elhadad E., Ibrahim M. & Al-fawwaz A. (2021) Effects of pollution on hydrogeochemistry and water quality of the Damietta branch (Nile River, Egypt), Water Science and Technology, 84 (6), 1509–1517. https://doi.org/10.2166/wst.2021.327. (PMID: 34559084) Fayyad U. & Uthurusamy R. (1996) Data mining and knowledge discovery in databases, Communications of the ACM, 39 (11), 24–26. https://doi.org/10.1145/240455.240463. Fayyad U., Piatetsky-Shapiro G. & Smyth P. (1996) From data mining to knowledge discovery in databases, AI Magazine, 17 (3), 37–54. https://doi.org/https://doi.org/10.1609/aimag.v17i3.1230. García-Ávila F., Zhindón-Arévalo C., Valdiviezo-Gonzales L., Cadme-Galabay M., Gutiérrez-Ortega H., Flores L., Cadme-Galabay M., Gutiérrez-Ortega H. & Flores L. (2022) A comparative study of water quality using two quality indices and a risk index in a drinking water distribution network, Environmental Technology Reviews, 11, 49–61. https://doi.org/10.1080/21622515.2021.2013955. Hadadin N. (2015) Dams in Jordan current and future perspective, Canadian Journal of Pure and Applied Sciences, 9 (1), 3279–3290. Halalsheh N., Alshboul O., Shehadeh A., Al Mamlook R. E., Al-Othman A., Tawalbeh M., Saeed Almuflih A. & Papelis C. (2022) Breakthrough curves prediction of selenite adsorption on chemically modified zeolite using boosted decision tree algorithms for water treatment applications, Water (Switzerland), 14 (16), 2519. https://doi.org/10.3390/w14162519. Humpal D., Al-Naser H., Irani K., Sitton J., Renshaw K. & Gleitsmann B. (2012) Review of Water Policies in for Strategic Priorities. United States Agency for International Development (USAID) , April , 114. Available at: https://www.researchgate.net/publication/311806724_A_Review_of_Water_Policies_in_Jordan_and_Recommendations_for_Strategic_Priorities. Hussein E. E., Jat Baloch M. Y., Nigar A., Abualkhair H. F., Aldawood F. K. & Tageldin E. (2023) Machine learning algorithms for predicting the water quality index, Water (Switzerland), 15 (20), 1–16. https://doi.org/10.3390/w15203540. Ibrahim M. M. F. (2014) The use of geoinformatics in investigating the impact of agricultural activities between 1990 and 2010 on land degradation in NE of Jordan. Logos . Ibrahim M. & Elhaddad E. (2021) Surface water quality monitoring and pollution of Ismailia canal, Egypt, using gis-techniques, Fresenius Environmental Bulletin, 30 (1), 70–79. JISM (2015) Jordanian Institute of Standards and Metrology. Jordan water quality standards. Amman, Jordan. Jordan Ministry of Water and Irrigation (2022) Jordan Water Sector Facts and Figures . chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.mwi.gov.jo/ebv4.0/root_storage/ar/eb_list_page/jordan_water_sector_-_facts_and_figures_2022.pdf. Jordan Ministry of Water and Irrigation (2023) The National Water Strategy 2023–2040. Amman, Jordan. Kumar R. & Sharma R. C. (2019) Assessment of the water quality of Glacier-fed lake Neel Tal of Garhwal Himalaya, India Assessment of the water quality of Glacier-fed lake Neel Tal of Garhwal, Water Science, 33 (1), 22–28. https://doi.org/10.1080/11104929.2019.1631554. Kumar R., Rajput J. S. & Saxena P. A. K. (2025) Assessment of Water Quality of Tighra Reservoir using Weighted Arithmetic Water Quality Index (WA-WQI) . February . https://doi.org/10.38124/ijisrt/IJISRT24MAY1308. Kundzewicz Z. W., Mata L. J., Arnell N. W., Döll P., Jimenez B., Miller K., Oki T., Şen Z. & Shiklomanov I. (2009) The implications of projected climate change for freshwater resources and their management resources and their management, Hydrological Sciences Journal, 53, 3–10. https://doi.org/10.1623/hysj.53.1.3. Mohammadpour R., Shaharuddin S. & Chang C. K. (2015) Prediction of water quality index in constructed wetlands using support vector machine, Environmental Science and Pollution Research, 22, 6208–6219. https://doi.org/10.1007/s11356-014-3806-7. (PMID: 25408070) MWI (2023) National Water Strategy 2023–2040. Ministry of Water and Irrigation. Amman, Jordan. Available at: https://www.mwi.gov.jo/EBV4.0/Root_Storage/AR/EB_Ticker/National_Water_Strategy_2023-2040_Summary-English_-ver2.pdf. Nortcliff S., Carr G., Potter R. B. & Darmame K. (2008) Jordan's water resources : challenges for the future Jordan's water resources : challenges for the future, Geographical Paper, 185, 1–24. Patel D., Mehta D. J., Azamathulla H., & M., Shaikh M. M., Jha S., & Rathnayake U. (2023) Application of the Weighted Arithmetic Water Quality Index in Assessing Groundwater Quality : A Case Study of the South. Sadat-Noori S. M., Ebrahimi K. & Liaghat A. M. (2014) Groundwater quality assessment using the Water Quality Index and GIS in Saveh-Nobaran aquifer, Iran, Environmental Earth Sciences, 71 (9), 3827–3843. https://doi.org/10.1007/s12665-013-2770-8. Sakaa B., Elbeltagi A., Boudibi S., Chaffaï H., Reza A., Islam T., Kulimushi L. C., Choudhari P., Hani A., Brouziyne Y. & Wong Y. J. (2022) Algorithm for support vector machine in Saf-Saf river basin Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf - Saf river basin. Environmental Science and Pollution Research , July . https://doi.org/10.1007/s11356-022-18644-x. Singh A. (2024) Effective management of water resources problems in irrigated agriculture through simulation modeling, Water Resources Management, 38 (8), 2869–2887. https://doi.org/10.1007/s11269-024-03796-x. Tirkey P., Bhattacharya T. & Chakraborty S. (2013) Water quality indices- important tools for water quality assessment, International Journal of Advances in Chemistry, 1 (1), 15–28. Tyagi S., Sharma B., Singh P. & Dobhal R. (2013) Water quality assessment in terms of water quality index, American Journal of Water Resources, 1 (3), 34–38. https://doi.org/10.12691/ajwr-1-3-3. Wheater H., Mathias S. & Li X. (2010) Groundwater modelling in arid and semi-arid areas. In: Groundwater Modelling in Arid and Semi-Arid Areas, New York, NY, USA: Cambridge University Press. https://doi.org/10.1017/CBO9780511760280. Yoon J., Klassert C., Selby P., Lachaut T., Knox S., Avisse N. & Harou J. (2021) A coupled human – natural system analysis of freshwater security under climate and population change, Environmental Sciences, 118 (14), e2020431118. https://doi.org/10.1073/pnas.2020431118/-/DCSupplemental.y. |
| Contributed Indexing: | Keywords: algorithms; data mining; heat-map; prediction; water quality |
| Entry Date(s): | Date Created: 20251128 Date Completed: 20251128 Latest Revision: 20251128 |
| Update Code: | 20251129 |
| DOI: | 10.2166/wst.2025.158 |
| PMID: | 41313656 |
| Databáze: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: The authors declare there is no conflict.<br />The evaluation of water quality constitutes a critical aspect of water management strategies, particularly in arid and semi-arid environments, where the use and protection of sustainable resources are crucial. This study focuses on assessing and predicting water quality in three Jordanian dams using advanced data mining techniques. Physical, chemical, and biological water quality parameters were collected and analyzed over a four-year period. The Weighted Arithmetic Water Quality Index (WA-WQI) was used to evaluate the overall water quality. Various data mining algorithms, including generalized linear models, decision trees, random forests, gradient-boosted trees, and support vector machine (SVM), were employed to predict WQI and understand the seasonal and annual variations. Key findings highlight significant fluctuations in water quality, influenced by parameters such as pH, conductivity, nutrients, and microbial contamination. The study emphasizes the importance of continuous monitoring and predictive modeling for effective water resource management. It also demonstrates the effectiveness of using SVM for water quality prediction in arid regions. The models were evaluated using different performance metrics. The SVM outperformed other employed models. This study provides a critical benchmark and a robust predictive framework for water resource management in Jordan and semi-arid areas, addressing a significant gap in regional environmental monitoring.<br /> (© 2025 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC 4.0), which permits copying, adaptation and redistribution for non-commercial purposes, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc/4.0/).) |
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| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2025.158 |