Imbalanced classification techniques for monsoon forecasting based on a new climatic time series. (August 2018) - Troncoso, A.; Ribera, P.; Asencio-Cortés, G.; Vega, I.; Gallego, D.
Abstract: Monsoons have been widely studied in the literature due to their climatic impact related to precipitation and temperature over different regions around the world. In this work, data mining techniques, namely imbalanced classification techniques, are proposed in order to check the capability of climate indices to capture and forecast the evolution of the Western North Pacific Summer Monsoon. Thus, the main goal is to predict if the monsoon will be an extreme monsoon for a temporal horizon of a month. Firstly, a new monthly index of the monsoon related to its intensity has been generated. Later, the problem of forecasting has been transformed into a binary imbalanced classification problem and a set of representative techniques, such as models based on trees, models based on rules, black box models and ensemble techniques, are applied to obtain the forecasts. From the results obtained, it can be concluded that the methodology proposed here reports promising results according to the quality measures evaluated and predicts extreme monsoons for a temporal horizon of a month with a high accuracy. Highlights: Use of imbalanced classification to address monsoon prediction. To deal with the Western North Pacific Summer Monsoon, which is predominantly an oceanic monsoon and data about its evolution are much scarcer. Use of a new monthly time series from 1900 to 2007, extending the previous one during the second half of the 20th century.