Time Lag recurrent Neural Network model for Rainfall prediction using El Niño indices

Indian summer monsoon rainfall is a process which is dependent on number of environmental and geological parameter. This makes it very hard to precisely predict the monsoon rainfall. As India is agriculture based country, a long range monsoon rainfall prediction is crucial for proper planning and organization of agriculture policy. Severe hydrological events, such as droughts, may result in decline of agricultural output, affecting both inhabitants and national economy of the country. El Niño and Southern Oscillation (ENSO) play an important role in the success or collapse of Indian monsoon development. The year-to-year variability in monsoon rainfall could cause extreme droughts and floods in the country. El Niño is an oscillation of the ocean-atmosphere system in the tropical Pacific having important consequences for weather around the globe. Understanding the relationship between ENSO and Indian monsoon rainfall is crucial to reduce negative impact or to take benefit of positive conditions. In this paper, a focused time lag recurrent neural network model has been proposed in order to determine the temporal relationship between ENSO and Indian summer monsoon rainfall.