Published November 10, 2017
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RAINFALL FORECASTOF ANDHRA PRADESH USING ARTIFICIAL NEURAL NETWORKS
- 1. Department of Statistics, Sri Venkateswara University, Tirupati, Andhra Pradesh
- 2. Department of Mathematics, NIT Hamirpur, Himachal Pradesh
- 3. Department of H&S, S.V Engineering College, Tirupati, Andhra Pradesh
Description
Forecasting monthly mean rainfall of Andhra Pradesh (India)using seasonal autoregressive integrated moving average (SARIMA) modeland artificial neural networks (ANN)has been discussed.In this paper, we have given the prediction values according to SARIMA and neural network models, in whichwe found that the ARIMA (1,0,0)(2,0,0)[12] for actual dataand ARIMA (3,0,0)(2,0,0)[12] for rainfall differenceshas been fitted.The significance test has been made by using lowest AIC and BIC values.
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References
- 1. Afzali, M., Afzali, A. & Zahedi, G. (2011). Ambient Air Temperature Forecasting Using Artificial Neural Network Approach, International Conference on Environmental and Computer Science, IPCBEE Vol.19, IACSIT Press, Singapore. 2. Alfaro, E. (2004). A Method for Prediction of California Summer Air Surface Temperature, Eos, Vol. 85, No. 51, 21 December 2004. 3. Anisimov O.A., (2001). Predicting Patterns of Near-Surface Air Temperature Using Empirical Data, Climatic Change, Vol. 50, No. 3, 297-315. 4. Box, G. E. P., Jenkins, G. M. & Reinsel, G. C. (1994). Time Series Analysis Forecasting and Control, 3rd ed., Englewood Cliffs, N.J. Prentice Hall. 5. Brunetti, M., Buffoni, L., Maugeri, M. & Nanni, T., (2000). Trends of minimum and maximum daily temperatures in Italy from 1865 to 1996. Theor. Appl. Climatol., 66, 49–60. 6. FAN Ke, (2009). Predicting Winter Surface Air Temperature in Northeast China, Atmospheric and Oceanic Science Letters, Vol. 2, No. 1, 14−17. 7. Faraway, J. & Chatfield, C. (1998). Time series forecasting with neural networks: a comparative study using the airline data, Journal of the Royal Statistical Society, Series C, Vol. 47, 2, 231-250. 8. Hejase, H.A.N. &Assi, A.H. (2012). Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE, ISRN Renewable Energy, Vol. 2012, Article ID 412471, 11 pages. 9. J. C. Ramesh Reddy, T. Ganesh, M. Venkateswaran, PRS Reddy (2017), Forecasting of Monthly Mean Rainfall in Coastal Andhra, International Journal of Statistics and Applications 2017, 7(4): 197-204. DOI: 10.5923/j.statistics.20170704.01 10. Kulkarni M.A., Patil, S., Rama, G.V. & Sen, P.N. (2008). Wind speed prediction using statistical regression and neural network, J. Earth Syst. Sci. 117, No. 4, 457–463. 11. Lee, J.H., Sohn, K. (2007). Prediction of monthly mean surface air temperature in a region of China, Advances in Atmospheric Sciences, Vol. 24, 3, 503-508. 12. Lee, J.H., Sohn, K. (2007). Prediction of monthly mean surface air temperature in a region of China, Advances in Atmospheric Sciences, Vol. 24, 3, 503-508. 13. Shrivastava, G., Karmakar, S., Kowar, M. K., Guhathakurta, P. (2012). Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review, International Journal of Computer Applications, Vol. 51, No.18, August 2012. 14. Stein, M. & Lloret, J. (2001). Forecasting of Air and Water Temperatures for Fishery Purposes with Selected Examples from Northwest Atlantic, J. Northw. Atl. Fish. Sci., Vol. 29, 23-30. 15. Tasadduq. I., Rehman, S., Bubshait, K. (2005). Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renewable Energy, 25, 545-554. 16. Zhang, G., Patuwo, B. E. & Hu, M. Y. (1998). Forecasting with Artificial Neural Networks: The State of the Art, International Journal of Forecasting, 14, 35-62.