A generalized machine learning approach for cost-effective monitoring of irrigation suitability: A demonstration case in El Fahs aquifer (Tunisia)
Journal
Groundwater for Sustainable Development
Date Issued
November 1, 2024
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.
Funding
The present work was conducted in the framework of AI-OBSERVER project titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence”, that has received funding from the European Union's Horizon Europe Framework Programme HORIZON-WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No. 101079468. The authors acknowledge that a significant part of this work is also conducted in the framework of the AGREEMAR project which is funded by National Funding Agencies from Germany (Bundesministerium für Bildung und Forschung – BMBF), Cyprus (Research & Innovation Foundation – RIF), Portugal (Fundação para a Ciência e a Tecnologia – FCT), Spain (Ministerio de Ciencia e Innovación – MCI) and Tunisia (Ministère de l’Enseignement Supérieur et de la Recherche Scientifique – MESRS) under the Partnership for Research and Innovation in the Mediterranean Area (PRIMA). The PRIMA programme is supported under Horizon 2020 by the European Union's Framework for Research and Innovation.