Modelado y predicción del fenómeno el niño en piura, perú mediante redes neuronales artificiales usando matlab

Artificial neural networks have been applied to climatic precipitation data, including surface sea temperatures in different areas classified as El Niño, and speed of trade winds with the purpose of modeling and predicting the climate phenomenon six months in advance to its appearance. The study was done in Piura, Peru. A preliminary analysis of the information is performed to determine the degree of correlation between variables. A model in two phases was later designed. In the first phase, neural networks using MatLab were used to model variables as time series and, in the second phase, a neural network was designed to simulate the nature of rainfall in Piura. The study shows that neural networks represents a highly reliable technique to find a pattern of precipitation and then for predicting the phenomenon with probability of 98.4% in the training step and 100% in the predicting step for the first semester of 2016.