Optimizing Water Productivity in the Face of Climate Change: The Central Role of Machine Learning Approaches

Document Type : Review

Authors

• Iman Hajirad ¹
• Paria Pourmohammad Paria Pourmohammad ²
• Masoud Pourgholam Amiji ³

¹ Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural

² Department of reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

³ Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj.

Abstract

Keywords

• Deep Learning
• Prediction
• Resilience
• Smart Irrigation

Main Subjects

• آبیاری و زهکشی

Introduction

Climate change, by causing severe fluctuations in precipitation, temperature, and evapotranspiration patterns, has imposed increasing and unpredictable challenges on global water resource management, particularly in arid and semi-arid regions. Under conditions where access to fresh water is severely reduced and food security is threatened, enhancing Water Use Efficiency (WUE) is considered the most effective strategy for adapting to water scarcity and maintaining global food security, serving as the most vital index for evaluating the efficiency of water resource utilization. However, assessing and optimizing WUE under a changing climate is a complex and multi-dimensional problem, as the relationships between climatic variables, soil properties, vegetation cover, and management activities possess a non-linear and dynamic nature. These complexities severely restrict the efficacy of traditional physical and linear statistical models, which rely on stability assumptions, thus highlighting the necessity for advanced analytical tools.

Methodology

This comprehensive review article aims to fill the existing gap in the research literature by systematically analyzing the role of Data-Driven Approaches (DDA), including Machine Learning (ML), Deep Learning (DL), and hybrid models, in optimizing water use efficiency under climatic uncertainty. Relying on big data analytics and the capability to understand hidden and non-linear relationships, DDA emerges as an efficient and forward-looking approach, serving as a suitable alternative to models based on predefined assumptions. The literature review focuses on the application of these models in predicting crop water requirements, estimating evapotranspiration, and evaluating the performance of irrigation systems.

Mechanisms and Key Applications

Data-Driven Approaches operate by extracting knowledge from large and diverse sets of information, encompassing climatic, hydrological, agricultural, remote sensing, and Internet of Things (IoT) sensor data. These models are capable of reducing high uncertainties in water management processes and providing more accurate predictions of vital variables such as soil moisture and actual Evapotranspiration (ET). The role of DDA in optimizing WUE is concentrated in four key areas: 1. Forecasting crop water demand, 2. Estimating and optimizing actual Evapotranspiration (ET), 3. Analyzing the performance of irrigation systems, and 4. Assessing the impacts of climate change on water productivity. These approaches cover three main analytical levels: Descriptive (understanding the current status and historical trends), Predictive (modeling the future behavior of systems), and most importantly, Prescriptive or Decision-Making (providing optimal and automated recommendations for the amount, timing, and method of irrigation in AI-based Smart Irrigation systems).

Model Comparison

Data-driven models are primarily categorized into three groups: Classical Machine Learning (e.g., Random Forest, SVM) which offer good interpretability and simplicity for medium-sized data; Deep Learning (e.g., CNN, RNN) which excel in processing massive data and modeling complex temporal and spatial relationships, yet require extensive computational resources and data, and suffer from interpretation challenges; and Hybrid Models (integrating physical models with ML/DL) which achieve the highest prediction accuracy, generalizability, and efficiency, even under data limitations, by leveraging the strengths of both approaches. The final conclusion emphasizes that combining multi-source data with intelligent algorithms significantly enhances decision-making accuracy in water management, paving the way for the development of smart irrigation systems and integrated water resource management. Data-Driven Approaches, despite challenges like data quality and the low interpretability of deep models, represent an unprecedented opportunity to elevate water resource management from a descriptive level to an intelligent and adaptive level, increasing resilience in the era of climate change.

Author Contributions

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

Not applicable.

Acknowledgements

The authors would like to thank all participants of the present study.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.