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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
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Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(4): 1516-1522
DOI: https://doi.org/10.20546/ijcmas.2018.704.170


Prediction of Rainfall of Allahabad District by the Development of Autoregressive Time Series Model
K.N. Singh1, A. Dalai1, M.P. Kaiwart1, R.R. Mohanty2 and B. Kumar1
1Faculty of SWE, SVCAET, IGKV, Raipur-492012, Chhatishgarh, India
2(Agril. Engg.) of Agro Polytechnic Centre, Rourkela, OUAT, Bhubaneswar-751003, Odisha, India
*Corresponding author
Abstract:

The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic differential equation. The present study was conducted with the main objective to develop a stochastic time series model for prediction of rainfall of Allahabad district, which lies between 25047’ N latitude, 81021’E longitude and elevation of 104 m from the mean sea level. The Geographical area of Allahabad district is 5246 km2 and to determine the annual rainfall, rainfall data of 28 years from 1983 to 2010 were used to develop the Autoregressive (AR) time series models of orders 0, 1 and 2. The general recursive formula was used to determine various parameters of the model. The goodness of fit and adequacy of models were tested by Box-Pierce Portmonteau test, Akaike Information Criterion, by comparing observed and predicted correlogram. The AIC value for AR (1) model is lying between AR (0) and AR (2) which is satisfying the selection criteria. The close agreement in rainfall is observed from the graphical representation between observed and generated correlogram. The developed model can be used efficiently for the prediction of rainfall of Allahabad district, as observed from the comparison between the observed and predicted rainfall by AR (1) model. This method can be immensely helpful for the farmers and research workers for water harvesting, ground water recharge, flood control and development of water management strategies.


Keywords: Stochastic time series model, Autoregressive (AR) models, Akaike information criterion, Box- Pierce Portmanteau
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How to cite this article:

Singh, K.N., A. Dalai, M.P. Kaiwart, R.R. Mohanty and Kumar, B. 2018. Prediction of Rainfall of Allahabad District by the Development of Autoregressive Time Series Model.Int.J.Curr.Microbiol.App.Sci. 7(4): 1516-1522. doi: https://doi.org/10.20546/ijcmas.2018.704.170