Forecasting the monsoon temporally is a major scientific issue in the field of monsoon meteorology. The ensemble of statistics and mathematics has increased the accuracy of forecasting of ISMR up to some extent. But due to the nonlinear nature of ISMR, its forecasting accuracy is still below the satisfactory level. Mathematical and statistical models require complex computing power. Therefore, many researchers have paid attention to apply ANN in ISMR forecasting. In this study, we have used Feed-Forward Back-Propagation neural network algorithm for ISMR forecasting. Based on this algorithm, we have proposed the five neural network architectures designated as BP1, BP2, …, BP5 using three layers of neurons (one input layer, one hidden layer and one output layer). Detail architecture of the neural networks are are provided in this chapter. Time series data set of ISMR is obtained from Pathasarathy (1994) (1871–1994) and IITM (2012) (1995–2010) for the period 1871–2010, for the months of June, July, August and September individually, and for the monsoon season (sum of June, July, August and September). The data set is trained and tested separately for each of the neural network architecture, viz., BP1–BP5. The forecasted results obtained for the training and testing data are then compared with existing model. Results clearly exhibit superiority of our model over the considered existing model. The seasonal rainfall values over India for next 5 years have also been predicted.
Indian summer monsoon rainfall prediction. (2016). In Studies in Fuzziness and Soft Computing (Vol. 330, pp. 127–148). Springer Verlag. https://doi.org/10.1007/978-3-319-26293-2_7