Reference evapotranspiration estimation with k-Nearest Neighbour and Artificial neural network models using different climate input variables in the semi-arid environment

Sevim Seda Yamaç

doi:10.15832/ankutbd.630303

Abstract

The absolute prediction of reference evapotranspiration (ETo) is an important issue for global water balance. Present study demonstrated the performance of k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN) models for prediction of daily ETo using four combinations of climatic data. The kNN and ANN models were studied four combinations of daily climate data during 1996-2015 in the Middle Anatolia region. The findings of ETo estimation with kNN and ANN models were classed with the FAO Penman Monteith equation. The outcomes of ETo values demonstrated that the kNN had higher performances than the ANN in all combinations. The statistical indicators of the kNN model showed ETo values with MSE, RMSE, MAE, NSE and R2 ranging from 0.541-0.031 mm day-1, 0.735-0.175 mm day-1, 0.547-0.124 mm day-1, 0.937-0.997 and 0.900-0.994 in the testing subset. Thus, the kNN can be used for the prediction of reference evapotranspiration with full and limited input meteorological data.

Highlights

Evapotranspiration (ET) can be described as water loss into the atmosphere via plant transpiration and soil evaporation (Landeras et al 2008; Fan et al 2018)
The k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN) with four combinations of climatic input data were evaluate for training and testing subsets
The findings showed that the kNN and ANN models were able to describe the nonlinear relationships between meteorological variables to estimate daily ETo values adequately

Summary

Introduction

Evapotranspiration (ET) can be described as water loss into the atmosphere via plant transpiration and soil evaporation (Landeras et al 2008; Fan et al 2018). Water resources are significantly reduced in semi-arid and arid environments due to the consequences of increasing climate change. In these regions where water shortage is a major problem, it is essential to estimate water loss by ET. Precise prediction of ET is an imperative step for managing water activities, especially in the area which faces water scarcity

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