Precipitation forecast model for the city of Managua using artificial neuronal networks in the context of environmental management

Abstract

This paper proposes a numerical model of precipitation forecast for the city of Managua, based on data obtained by the weather stations: Managua, La Primavera and Casa Colorada (El Crucero), information provided by the Instituto Nacional de Estudios Territoriales (INETER), from the period corresponding to 1950-2014 and other climatic variables such as the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), South Oscillation Index (SOI), Anomaly Index of the Monthly Average of the Temperature of the Surface of the Sea (SST) in the Tropical North Atlantic, region 5.5º N - 23.5º N and 57.5º W - 15º W (TNA) and the Oceanic Index of the Niño (ONI), through teleconnections. For the elaboration of the model, the selection and correlation of variables was carried out through statistical methods and to find the relationship between these variables, a multilayer perceptron was selected, which is an Artificial Neural Network whose architecture in this case is composed of; an input layer, a hidden layer and an output layer. This network has been trained through supervised learning through the backpropagation algorithm. This network will be used to predict future weather conditions in the city, which will help to make decisions about the management and planning of climate-sensitive activities to deal with possible natural disasters.