Comparing the performance of machine learning methods in modeling daily reference ET and its spatial distribution (case study: Zanjan province)

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Document Type : Research Paper

Authors

1 , Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

2 Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

Abstract

Iran is one of the world's semi-arid and arid regions, it has serious water resource limitations. Two important independent processes of the hydrological cycle are the combined processes of subsoil evaporation and transpiration from plant. The purpose of this research is to compare the performance of machine learning methods including multiple linear regression (MLR), gene expression programming (GEP) and random forest (RF) in modeling daily reference evapotranspiration and its spatial distribution in Zanjan province.In the current research, the meteorological data of ten years (2009–2019) from the synoptic stations of Zanjan, Mahneshan, Khodabande and Khoramdare were used. , to compare the performance of the machine learning methods with each other, the evaluation criteria including the Root Mean Square Error (RMSE), Coefficient of determination (R2), Scatter Index (SI) and Wilmot Index (WI) were calculated and time series, dispersion and violin charts were drawn.On the other hand, the geostatistical technique was used for estimating the reference evapotranspiration in unmeasured points for zoning because of the limited number of available synoptic stations in Zanjan province. According to the values of statistical indicators, the results showed that all three models have high accuracy and less error in estimating daily reference evapotranspiration, but the random forest (RF) model was chosen as the best model with a small difference. The results of zoning using the inverse distance weighting (IDW) method of this model showed that the rate of evapotranspiration is higher in the northwest of Zanjan province, especially in Mahenshan Station with (4.020-4.273) mm/day.

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History

  • Receive Date: 21 November 2023
  • Revise Date: 30 April 2024
  • Accept Date: 15 May 2024
  • Publish Date: 15 May 2024

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