International Conference on Computer and Knowledge Engineering

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Forecasting El Niño Six Months in Advance Utilizing Augmented Convolutional Neural Network

Authors :

Mohammad Naisipour¹ Iraj Saeedpanah² Arash Adib³ Mohammad Hossein Neisi Pour⁴

1- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran. 2- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran. 3- Department of Civil Engineering Civil Engineering and Architecture Faculty Shahid Chamran University of Ahvaz, Iran 4- Department of Computer Engineering. Sharif University of Technology. Tehran, Iran

Keywords :

ACNN،El Niño،Forecast،SST،Augmentation

Abstract :

The ability of predicting climate phenomena enables international organization and governments to manage natural disasters such as droughts. El Niño Sothern Oscillation (ENSO) is one the most influential and crucial phenomenon follows with large scale climatic events and can be used for predicting droughts and floods in different parts of the earth. Due to such a great importance, a new Convolutional Neural Network method based on augmented data (ACNN) for predicting ENSO on a relatively long period is developed in this research. The method is developed based on CNN to forecast ENSO six month earlier. Sea Surface Temperature (SST) anomaly maps are given to the model as the predictors and Niño 3.4 Index is the predictand. The method applies convolutional tensors to extract features from the maps, and delivers them to a fully connected neural network to discover connections between Niño Index and the features. A tricky augmentation process is used to increase the number of input data to compensate lack of observations. The model represents reliable prediction as it compared with observations for a long period to ensure the validity and reliability of the method. The relatively low computation cost of the method makes it a great tool for predicting ENSO and its following consequences even for related institutions in low income countries.