Generalizability of machine learning models and empirical equations for the estimation of reference evapotranspiration from temperature in a semiarid region

Abstract

The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ET o ). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artifi cial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ET o using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated.

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

“…Artificial neural network (ANN) is one of the most widely used and reliable approaches in various sectors of the food and agricultural industries. The ANN algorithm is a supervised machine learning algorithm that simulates the human brain’s classification function for regression-type applications [ 18 , 19 ]. An ANN structure consists of an input layer, an output layer, one or more processing layers referred to as hidden layer(s), and a collection of processing elements referred to as neurons.…”