Can Time and Cost Efficiency Be Enhanced for GWQI Prediction Utilizing Machine Learning Modeling?

Farhan `Ammar Fardush Sham (), Ahmed El-Shafie (), Wan Zurina Binti Wan Jaafar (), S. Adarsh (), Mohsen Sherif () and Ali Najah Ahmed ()
Additional contact information
Farhan `Ammar Fardush Sham: Universiti Malaya (UM)
Ahmed El-Shafie: Universiti Malaya (UM)
Wan Zurina Binti Wan Jaafar: Universiti Malaya (UM)
S. Adarsh: TKM College of Engineering
Mohsen Sherif: National Water & Energy Centre, United Arab Emirates University
Ali Najah Ahmed: Sunway University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 10, No 14, 4989-5004

Abstract: Abstract Groundwater (GW) acts as a key source of freshwater for household, industrial, and agricultural use across the globe. Assessing groundwater quality is important, given the increasing pressures from human activities and climate change. However, collecting groundwater quality data is challenging due to high costs and time-intensive processes. Recently, integrating the Groundwater Quality Index (GWQI) and machine learning (ML) has emerged as a promising approach for managing groundwater quality, though advancements specifically addressing time and cost-efficiency remain limited. The study analysed groundwater data from Erbil Basin, Kurdistan, Iraq, using 13 quality-impacting parameters, resulting in over 66,000 data points. The Weighted Arithmetic Water Quality Index (WAWQI) method provided a comprehensive evaluation by integrating multiple parameters into a single quantitative assessment. Machine learning models, including Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), ensemble methods, and Support Vector Machines (SVM), were employed to enhance prediction accuracy. Seven scenarios explored the effects of excluding specific parameters, such as chloride (Cl⁻), nitrate (NO₃⁻), and sulfate (SO₄²⁻). Results showed exponential GPR performed best in scenarios 1–1 (96.10%), 2–3 (96.32%), and 3 − 1 (93.9%). Linear SVM achieved the highest accuracy in scenarios 1–2 (95.58%) and 2 − 1 (93.24%), while the wide neural network excelled with perfect accuracy in scenario 2–2. Scenario 1–3’s top performance was by exponential GPR with 93.29% accuracy. These findings highlight the potential of ML models in optimizing groundwater quality predictions while addressing cost and time constraints.

Keywords: Groundwater quality; Prediction model; Artificial neural network; Support vector machines; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04190-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:10:d:10.1007_s11269-025-04190-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04190-x

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().