Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

Abstract

Crop evapotranspiration (ETc) is a complex and non-linear process difficult to measure and estimate accurately. This complexity can be solved applying the machine learning techniques with different meteorological input variables. This study investigated the performance of k-Nearest Neighbour (kNN), Artificial Neural Networks (ANN) and Adaptive Boosting (AdaBoost) models to predict daily potato ETc using four scenarios of available meteorological data as: air temperature (scenario 1), air temperature and solar radiation (scenario 2), air temperature, solar radiation and wind speed (scenario 3), and air temperature, solar radiation, wind speed and relative humidity (scenarios 4). The analysis was based on the results of experimental trials carried out in Southern Italy in 2009 and 2010 and focussed on the potato crop cultivation under optimal water supply. The results of ETc estimation with different machine learning techniques were compared with ETc obtained from the soil water balance model, based on the FAO Penman Monteith approach, and gravimetric measurements of soil water content in the crop root zone. The best performances were observed with the kNN model with R2 of 0.813, 0.968 and 0.965, slope of regression 0.947, 0.980 and 0.991, modelling efficiency (EF) of 0.848, 0.970 and 0.972, root mean square error (RMSE) of 0.790, 0.351 and 0.355 mm day−1, mean absolute error (MAE) of 0.563, 0.263 and 0.274 mm day−1 and mean squared error (MSE) of 0.623, 0.123 and 0.126 mm day−1 for scenarios 1, 2 and 3, respectively. When all meteorological variables were available (scenario 4), the ANN model produced slightly better statistical indicators. Therefore, the kNN model could be recommended for the estimation of ETc when limited meteorological data are available. Otherwise, the ANN model should be applied.