Evaluation of random forest model to calculate potential evapotranspiration using limited meteorological data in the study area of Ardabil Plain

Articles in Press

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil , Iran

2 Department of Water Science and Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.

Abstract

As the global demand for water resources grows, it is becoming more apparent that reducing water losses, including evapotranspiration, is crucial. While there are many models to predict evapotranspiration, there is no agreement on a universally accepted model for all climate regions. Several soft computational models have been created to circumvent the constraints of empirical models and accurately predict ET. Soft computing models typically necessitate less data and are applicable across various climatic zones. This study aimed to analyze how well two random forest models and multiple linear regression could predict ETo in the Ardabil plain region. Meteorological data from the Iran Meteorological Organization were used to calculate reference evapotranspiration from 2014 to 2016. In constructing the model, data from 4 meteorological stations was combined to generate a random time series, while the fifth station was reserved for evaluating the models. The assessment metrics utilized comprised RMSE, R2, and NSE. The RF model achieved higher accuracy with R2, NSE, and RMSE values of 0.74, 0.743, and 8.20 mm, respectively, compared to the MLR model. The current research demonstrated that random forest models are dependable for forecasting ETo with minimal climate data. In general, using the results of this research and other similar research, it can be said that RF and MLR models simulate potential evapotranspiration with acceptable accuracy, but are sensitive to the number of input parameters.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 25 December 2024

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