Optimum-Path Forest-Based Rainfall Estimation

Abstract

Meteorological conditions are crucial for the agricultural production. Rainfall, in particular, can be cited as the most influential by having direct relation with hydric balance. Meteorological satellites that cover the whole earth have been extensively used for the development of statistical and artificial intelligence models for rainfall estimation. However, some of these techniques have flaws and need to be revisited. The Optimum-Path Forest (OPF) classifier is a novel of graph-based approach for supervised pattern recognition that have been demonstrated to be superior than Artificial Neural Networks using Multilayer Perceptron (ANN-MLP) and similar to Support Vector Machines (SVM), but much faster. We introduce here the OPF classifier for rainfall estimation using satellite images and their comparison against ANN-MLP and SVM. Another round of experiments were also executed with different metrics to show the robustness of our image descriptor. We are also the first to derive the OPF classifier complexity analysis.