Abstract
The assessment of climate change, especially in terms of rainfall variability, is of giant concern all over the world at present. Contemplating the high spatiotemporal variation in rainfall distribution, the prior estimation of precipitation is necessary at finer scales too. This study aims to develop an ARIMA model for prediction of monthly rainfall over Khordha district, Odisha, India. Due to the unavailability of recent rainfall data, monthly rainfall records were collected for 1901–2002. The rainfall during 1901–82 was used to train the model and that of 1983–2002 was used for testing and validation purposes. The model selection was made using Akaike information criterion (AIC) and Bayesian information criterion (BIC), and ARIMA (1, 2, 1) (1, 0, 1)12 was found to be the best fit model. The efficiency was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2). The model forecasts produced an excellent match with observed monthly rainfall data. The outstanding accuracy of the model for predicting monthly rainfall for such a long duration of 20 years justifies its future application over the study region, thereby aiding to a better planning and management.
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Acknowledgements
The authors are thankful to the organizations (India Meteorological Department and IIT Bombay) for providing valuable data and facilities. We also acknowledge all those persons who have supported for this work.
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Swain, S., Nandi, S., Patel, P. (2018). Development of an ARIMA Model for Monthly Rainfall Forecasting over Khordha District, Odisha, India. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_34
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DOI: https://doi.org/10.1007/978-981-10-8636-6_34
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