Evapotranspiration Modeling Using Machine Learning Techniques

The accurate estimation of evapotranspiration (ET) is fundamental to water resource management, agriculture and climate studies. Traditionally governed by physically based approaches such as the Penman–Monteith method, ET modelling demands extensive meteorological data and often struggles to accommodate spatial variability and missing inputs. In recent years, machine learning (ML) has emerged as a powerful alternative, offering data-driven frameworks that can assimilate meteorological, remotely sensed and in situ sensor data to predict ET with reduced complexity. Artificial neural networks, support vector machines, gene expression programming and ensemble algorithms have been applied to capture nonlinear relationships among temperature, humidity, radiation and wind speed, often achieving comparable or superior performance to standard equations. Hybrid ensemble models that combine the strengths of multiple algorithms, sometimes integrated with Internet of Things networks, enable daily ET estimation using limited inputs while adjusting predictions according to dynamic environmental conditions. These advances support irrigation scheduling, drought assessment and large-scale hydrological budgeting by enhancing model robustness across diverse climates. Ongoing research focuses on optimising model architectures, incorporating reanalysis data and remote sensing, and developing transferable frameworks that can adapt to data-scarce regions. Collectively, machine learning-based ET modelling represents a global paradigm shift towards more flexible, scalable and accurate water-resource decision-support tools.

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Evapotranspiration Modeling Using Machine Learning Techniques publication trend

The graph below shows the total number of articles in evapotranspiration modeling using machine learning techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Evapotranspiration (ET): The combined process of water loss by soil evaporation and plant transpiration.

Penman–Monteith method: A physically based equation for reference ET estimation requiring meteorological variables such as radiation and humidity.

Artificial Neural Network (ANN): A machine learning model inspired by biological neural networks, used to capture nonlinear relationships among inputs.

Gene Expression Programming (GEP): An evolutionary algorithm that evolves computer programmes or formulas to fit data patterns.

Support Vector Machine (SVM): A kernel-based ML algorithm that can perform regression by finding an optimal hyperplane in transformed feature space.

Hybrid Ensemble Model: A predictive framework that combines multiple ML algorithms to improve overall accuracy and robustness.

References

1. Smart reference evapotranspiration using Internet of Things and hybrid ensemble machine learning approach. Internet of Things (2023).
2. Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar, India. Information Processing in Agriculture (2023).
3. Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration. PLOS ONE (2019).