Advertisement

Region-Wise Rainfall Prediction Using MapReduce-Based Exponential Smoothing Techniques

  • S. Dhamodharavadhani
  • R. Rathipriya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

Weather acts an important role in agriculture. Rainfall is the primary source of water that agriculturist depends on to cultivate their crops. Analyzing the historical data and predicting the future. As the size of the dataset becomes tremendous, the process of extracting useful information by analyzing these data has also become repetitive. To defeat this trouble of extracting information, parallel programming models can be used. Parallel Programming model achieves this by partitioning these large data. MapReduce is one of the parallel programming models. In general, Exponential Smoothing is one of the methods used for forecasting a time series data. Here, data is the sum of truth and error where truth can be “approximated” by averaging out previous data. It is used to forecast time series data when there is Level, Trend, Season, and Irregularity (error). In this paper, Simple Exponential Smoothing, Holt’s Linear, and Holt-Winter’s Exponential Smoothing methods are proposed with MapReduce computing model to predict region-wise rainfall. The experimental study is conducted on two different datasets. The first one is Indian Rainfall dataset which comprises of the year, state, and monthly rainfall in mm. The second is Tamil Nadu state rainfall dataset which consists of the year, districts, and monthly rainfall in mm. To validate these methods, MSE accuracy measure is calculated. From the results, Holt-Winter’s Exponential Smoothing shows the better accuracy for rainfall prediction.

Keywords

Rainfall Prediction MapReduce Simple exponential smoothing method Holt’s linear method Holt-Winter’s method 

References

  1. 1.
    Nazim, A., Afthanorhan, A.: A comparison between single exponential smoothing (SES), double exponential smoothing (DES), holt’s (brown) and adaptive response rate exponential smoothing (ARRES) techniques in forecasting Malaysia population. Glob. J. Math. Anal. 2(4), 276–280 (2014)CrossRefGoogle Scholar
  2. 2.
    Arputhamary, B., Arockiam, L.: Performance improved holt-winter’s (PIHW) prediction algorithm for big data. Int. J. Intell. Electron. Syst. 10 (2) (2016)Google Scholar
  3. 3.
    Dielman, T.E.: Choosing smoothing parameters for exponential exponential sums of absolute errors. J. Mod. Appl. Stat. Methods 5(1) (2006)Google Scholar
  4. 4.
    Din, N.S.: Exponential smoothing techniques on time series river water level data. In: Proceedings of the 5th International Conference on Computing and Informatics, ICOCI 2015, p. 196 (2015)Google Scholar
  5. 5.
    Kalekar, P.S.: Time Series Forecasting Using Holt-Winters Exponential Smoothing (2004)Google Scholar
  6. 6.
    Kristoko DWI Hartomo, Subanar, Winarko, E.D.I.: Winters exponential smoothing and z-score. J. Theoret. Appl. Inf. Technol. 73(1), 119–129 (2015)Google Scholar
  7. 7.
    Mick Smith, R.A.: A Comparison of Time Series Model Forecasting Methods on Patent Groups (2015)Google Scholar
  8. 8.
    Ravinder, H.V.: Determining the optimal values of exponential smoothing constants—does solver really work? Am. J. Bus. Educ. 6(3), 347–360 (2013)Google Scholar
  9. 9.
    Sopipan, N.: Forecasting rainfall in Thailand: a case study. Int. J. Environ. Chem. Ecol. Geol. Geophys. Eng. 8(11), 717–721 (2014)Google Scholar
  10. 10.
    Meng, X., Mahoney, M.: Robust regression on MapReduce. In: Proceedings of the 30th International Conference on Machine Learning (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePeriyar UniversitySalemIndia

Personalised recommendations