A generalized machine learning approach for cost-effective monitoring of irrigation suitability: A demonstration case in El Fahs aquifer (Tunisia)

Abstract

This study develops and evaluates the performance of machine learning (ML) regression models, namely the Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbors (kNN) and eXtreme Gradient Boosting (XgBoost), for estimating the Irrigation Water Quality Index (IWQI) based on groundwater samples collected from El Fahs aquifer in Tunisia. The groundwater data are used as predictors for training and testing the machine learning models. Results indicate that sodium concentration has the most influence on the IWQI estimations for all ML models. The best-performing algorithms are found to be the SVR and XgBoost. Different datasets are also collected from existing studies, and merged to generate a single dataset that is used to develop generalized machine learning models. Despite regional variations, the generalized models performed adequately when tested against the unseen data, particularly the data collected from El Fahs case site. This work highlights the potential of using ML models, together with established metrics, as screening tools for predicting and classifying the irrigation suitability of groundwater based on available literature, even when metrics are not universally applicable. The findings of this study can be used by the water authorities in data scarce environments as cost-effective monitoring tools to assess water suitability for irrigation purposes. • A regressive machine learning approach was adopted to assess groundwater quality. • Site-specific and generalized models were used to evaluate irrigation water quality. • SVR and XgBoost identified as best-performing algorithms for IWQI prediction. • Sodium is the most influential parameter in IWQI estimation in El Fahs aquifer. • Dual focus on regional and global scales enhances versatility for IWQI assessment.

Topics & Concepts