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Indian Summer Monsoon Rainfall Prediction

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Applications of Soft Computing in Time Series Forecasting

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 330))

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Abstract

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, \(\ldots \), 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.

I still believe in the possibility of a model of reality, that is to say, of a theory, which represents things themselves and not merely the probability of their occurrence.

By Einstein (1879–1955)

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Notes

  1. 1.

    References are: (Rajeevan et al. 2004; Goswami and Srividya 1996; Goswami and Kumar 1997).

  2. 2.

    http://www.tropmet.res.in/,2012.

  3. 3.

    References are:(Krishnamurti et al. 1990; Hastenrath and Greischar 1993; Annamalai 1995; Sahai et al. 2000).

  4. 4.

    References are:(Bishop 1995; Shimshoni and Intrator 1998; Sharkey 1999).

  5. 5.

    References are: (Iyengar and Raghukanth 2003; Kishtawal et al. 2003; Rajeevan et al. 2004; Prasad et al. 2010; Kumar et al. 2012; Sinha et al 2012; Singh et al. 2012).

  6. 6.

    References are: (Kang et al. 2004; Wang et al 2005; Barnston et al. 2010; Sinha et al 2012).

  7. 7.

    References are: (Mooley and Munot 1993; Kumar et al 1999; Singh et al. 2012).

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  1. Department of Computer Science and Engineering, Thapar University, Patiala, India

    Pritpal Singh

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Singh, P. (2016). Indian Summer Monsoon Rainfall Prediction. In: Applications of Soft Computing in Time Series Forecasting. Studies in Fuzziness and Soft Computing, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-319-26293-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-26293-2_7

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