Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

Abstract

This study addressed the increasing challenges of climate change by exploring the use of machine learning (ML) algorithms to predict the reference evapotranspiration (ETo). Accurate ETo prediction is crucial for optimizing irrigation water management. This research aimed to assess the reliability and accuracy of ML algorithms in predicting ETo values. Three ETo calculation methods were employed: Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). The study analyzed ETo and other climate variables using the modified Mann-Kendall test (m-MK) and Theil Sen’s slope estimator methods to identify trends. Multiple ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), XGboost, K-Nearest Neighbor (KNN), Decision Trees (DT), Linear Regression (LR), and Multiple Linear Regression (MLR) were utilized for ETo prediction. The ML algorithms exhibited excellent performance, with coefficients of determination (R²) values ranging from 0.97 to 0.99 for PM, 0.99 for HA, and from 0.91 to 0.92 for BC. The models demonstrated a high value of the Kling-Gupta efficiency (KGE) with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Strong correlations between the predicted and calculated daily ETo were observed with R² values of 0.99, 0.99, and 0.92 for PM, HA, and BC methods, respectively. In conclusion, this study affirmed the accuracy and reliability of ML algorithms to match that of standard ETo prediction equations.

Keywords:

• Climate change
• reference evapotranspiration
• machine learning algorithms
• modified Mann-Kendall test
• Kling-Gupta efficiency

REVIEWING EDITOR:

• Manuel Tejada, Universidad de Sevilla, Spain

SUBJECTS:

• Agriculture & Environmental Sciences
• Agriculture and Food
• Agriculture

Authors’ contributions

Yasser Arafa contributed in the conception and design, Mohamed A. Youssef, M. Hafez and Y. Rashad contributed in the analysis, Troy Peters contributed in the interpretation of the data investigation, Ahmed Abd-ElGawad and Y. M. Rashad contributed in the drafting of the paper, Mohammed A. El-Shirbeny and Mohamed A. Youssef contributed in revising the paper critically for intellectual content and the final revision. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

The authors declare no conflict of interest.

Data availability statement

Data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was funded by King Saud University, Riyadh, Saudi Arabia through the researchers supporting project number (RSPD2024R676).

Notes on contributors

Mohamed A. Youssef

Mohamed A. Youssef (MSc) is a Teaching Assistant at Ain Shams University, his research focuses on the utilization of remote sensing data in agricultural contexts. With specialized expertise, he investigates the application of machine learning techniques to enhance smart irrigation systems. His contributions aim to advance the understanding and implementation of technology-driven solutions for sustainable agriculture.

R. Troy Peters

R. Troy Peters (PhD) is a Professor at Department of Biological Systems Engineering, Washington State University, Pullman, Washington, USA. Troy’s research work is in the Land, Air, Water Resources, and Environmental Engineering (LAWREE) emphasis area. His primary focus is on agricultural irrigation. This includes deficit irrigation, irrigation water hydraulics, irrigation scheduling and management, irrigation automation, sprinkler irrigation efficiency, low energy precision application (LEPA), low elevation spray application (LESA), and crop water use estimation.

Mohammed El-Shirbeny

Mohammed El-Shirbeny (PhD) is a Professor at National Authority for Remote Sensing and Space Sciences. I have extensive experience in the handling of remote sensing data and the application of data science techniques for agricultural water management and climate change-related aspects within the agricultural system. Also, I am developing the Stand-alone remote Sensing Approach to estimate the Reference Evapotranspiration (SARE).

Ahmed M. Abd-ElGawad

Ahmed M. Abd-ElGawad (PhD) is a Professor of Plant Ecology, College of Food and Agricultural Sciences, King Saud University, Saudi Arabia. His interest area is plant ecology, plant-plant interactions, chemical ecology, eco-physiology and phytotoxicity dynamics.

Younes M. Rashad

Younes M. Rashad (PhD) is an associate professor in Plant Protection and Biomolecular Diagnosis Department, Arid Lands Cultivation Research Institute (ALCRI), City of Scientific Research and Technological Applications (SRTA-City), New Borg, El-Arab, Egypt. His research interest include plant biotic and abiotic stresses and mycorrhizal fungi.

Mohamed Hafez

Mohamed Hafez Ph.D. Researcher of Environmental Soil Chemistry at the Department of Soil Science and Soil Ecology, Saint Petersburg State University, Russia. Researcher at Land and Water Technologies department, City of Scientific Research and Technological Applications, Egypt. Dr. Hafez serves as a reviewer for many scientific highs ranked journals. Dr. Hafez member of the Agricultural and Environmental Moscow Society, Russia. He was awarded the best publication in the international soil science conference in Moscow, 2020 and 2021 in a row. Dr. Hafez received the Distinguished Scientific Publication Award for my uncle 2020/2021 in a row. He has over 12 years of experience in research related to Environmental Soil Chemistry Studies. Finally, he has more than 30 scientific articles published in peer-reviewed journals and 6 book chapters.

Yasser Arafa

Yasser Arafa is a Professor at Ain Shams University, his expertise lies in on-farm irrigation, drainage systems engineering, and farm mechanization, with a specialized focus on smart irrigation systems. He is dedicated to advancing precision farming by integrating cutting-edge technologies into my research pursuits. Additionally, he has actively contributed as a team member to various research projects to enhance irrigation practices and agricultural sustainability for over 25 years.