Assessment of Machine Learning and Remote Sensing in Quantifying Reference Evapotranspiration

Document Type : Research Paper

Authors

Department of Water Engineering, Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili, Ardabil, Iran.

10.22059/jwim.2025.383457.1182

Abstract

Estimating crop water requirements and designing irrigation systems effectively depends heavily on determining evapotranspiration. Machine learning models have been developed to estimate evapotranspiration (ET) and circumvent the limitations of empirical models. In recent years, ET estimate has been improved and refined through the use of remote sensing technology. Assessment of remote sensing and machine learning for determining reference evapotranspiration This study examined the effectiveness of three models for estimating reference evapotranspiration in the Ardabil plain: random forest (RF), multiple linear regression (MLR), and support vector machine (SVM). From 2006 to 2023, synoptic stations and remote sensing provided meteorological data for the model. The FAO Penman-Monteith method was used to calculate ETo, the target parameter, within a five-synoptic station range. The time series of input and target parameters were recorded at the four synoptic stations during the model's construction and training phases. A random time series and a combination of all the data were then used in the model's final evaluation phase, which only used the data from the fifth station. R2, NSE, and RMSE were the evaluation statistics that were employed. The RF model's statistical index results were 0.7, 0.558, and 10.76, the SVM's were 0.71, -1, and 13.6, and the MLR's were 0.71, -0.688, and 21. Comparing the outcomes, it was found that the RF model was more accurate than the others. The current study demonstrated that, for areas lacking statistics, the random forest model can be a dependable and reasonably accurate model for predicting ETo using RS data.

Keywords

Main Subjects

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Water and Irrigation Management
Volume 15, Issue 1
May 2025
Pages 180-205

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