Estimate Reference Evapotranspiration Using Machine Learning Methods

Marwa Dorai, Marwa Dorai, Mehrez Abdellaoui, Bouthaina Douh, Ali Douik

2025

Abstract

Agriculture, a fundamental pillar of human civilisation, not only provides the food we need to survive, but is also a major driver of global economic growth. Yet this critical sector is increasingly threatened by the escalating impacts of climate change, particularly through the exacerbation of water scarcity in key agricultural regions. Changing climate patterns are disrupting rainfall cycles, leading to more frequent droughts and reduced water availability. As the global population grows exponentially and demand rises, farmers require water for irrigation to meet these needs. This growing resource scarcity underscores the urgent need for innovative, sustainable agricultural solutions to adapt to these challenges. To secure the future of water resources and safeguard agricultural productivity, it is crucial to proactively implement cutting-edge technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). In this context, we present a novel approach for estimating reference evapotranspiration ET0 with the aim of minimising water waste and improving the efficiency of irrigation water management. The study was carried out in a real-world setting where several sensors were installed to measure various parameters, including temperature, soil moisture and rainfall. The station is connected to a server application from which a dataset was generated after data cleaning and pre-processing. The parameters obtained from the dataset were classified in terms of their correlation with the output value ET0. Regression was then performed using various machine learning (ML) tools to predict water stress. The developed algorithms resulted in good performances in terms of coefficient of determination R2 and loss function RMSE. These performances exceed those of existing methods from the state of the art.

Paper Citation

in Harvard Style

Dorai M., Abdellaoui M., Douh B. and Douik A. (2025). Estimate Reference Evapotranspiration Using Machine Learning Methods. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 343-350. DOI: 10.5220/0013131800003890

in Bibtex Style

@conference{icaart25,
author={Marwa Dorai and Mehrez Abdellaoui and Bouthaina Douh and Ali Douik},
title={Estimate Reference Evapotranspiration Using Machine Learning Methods},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={343-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013131800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Estimate Reference Evapotranspiration Using Machine Learning Methods
SN - 978-989-758-737-5
AU - Dorai M.
AU - Abdellaoui M.
AU - Douh B.
AU - Douik A.
PY - 2025
SP - 343
EP - 350
DO - 10.5220/0013131800003890
PB - SciTePress