Improved frost forecast using machine learning methods

Abstract

Frosts are one of the atmospheric phenomena with one of the larger negative effects on the agricultural sector in the southern region of Brazil, therefore, an earlier forecast can minimize their impacts. In the present work, artificial neural networks (ANNs) techniques were applied in order to improve the predicting capabilities of frost events in southern Brazil. In the study, two multilayer perceptron (MLP) ANNs were built, one with ADAM optimizer and the other with SGD. The input parameters MLP-ANNs were numerical predictions of the Eta model. The ANNs were trained using four years (2012–2015), while validation and testing were performed using 2016 and 2017, respectively. An episode of frost that occurred on May 21st, 2018, related to an intense cold air mass, was also utilized to evaluate the performance of the ANNs. The best configurations (topologies and hyperparameters) of the ANNs were identified through experiments, using the highest accuracy obtained during the validation period as a metric. The results of the ANNs with ADAM and SGD optimizers were compared with the predictions of the Eta model. For the case study, an additional comparison against the operational frost index (IG) from the National Institute for Space Research (INPE) was also included. The performance of both ANNs (properly configured) with ADAM and SGD optimizers are comparable one to the other. And both are significantly better compared to the Eta model. The ANNs were able to drastically reduce the underestimation trends of frost events caused by the warm bias of the Eta model. The ANNs also indicated more satisfactory performances when compared to the INPE IG. In general, the ANNs were able to identify deficiencies in Eta predictions, and consequently improve their results. In this sense, the use of ANNs to predict frost events can be a very useful tool in an operational environment.