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

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.