Abstract
Rainfall is a random process and prediction of rainfall requires consistent as well as relevant information of meteorological and environmental data. In this paper, two different artificial neural networks models are proposed for consecutive daily rainfall prediction on the basis of the preceding events of rainfall data. Model designed for rainfall forecast is based on the pattern recognition methodology. In this method relevant spatial and temporal feature of rainfall series in past are extracted. These features are then utilized to predict the rainfall in future. Time lag delay neural network has capability to learn from the past event and predict the next value. Rainfall prediction is done on basis of rainfall on previous day to rainfall for the preceding six days. The proposed network is capable of forecasting daily rainfall one day in advance with accuracy of R2 = 0.96 and NMSE = 0.0005.
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