Preprint Article Version 1 This version is not peer-reviewed

Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

Version 1 : Received: 6 August 2019 / Approved: 7 August 2019 / Online: 7 August 2019 (11:28:34 CEST)

How to cite: Rashid Niaghi, A.; Hassanijalilian, O.; Shiri, J. Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation. Preprints 2019, 2019080097 (doi: 10.20944/preprints201908.0097.v1). Rashid Niaghi, A.; Hassanijalilian, O.; Shiri, J. Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation. Preprints 2019, 2019080097 (doi: 10.20944/preprints201908.0097.v1).

Abstract

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

Subject Areas

Evapotranspiration, Genetic programming, Support vector machine, Multiple linear regression, Random forest

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