The present study was aimed at developing a rainfall forecasting methodology using historical records of rainfall data. Time series modelling was carried out using IMD rainfall data over the Udaipur region of Rajasthan, India, from 1988-2020 to make a forecast for the year 2021. Auto-Regression Integrated Moving Average (ARIMA) model was used to make a forecast. Further, the forecast rainfall data were used for spatial distribution and spatial estimation of rainfall using the interpolation method of Kriging. The error in estimation was quantified with the R2 value. The methodology used in this study was found adequate and quite efficient to be relied upon for making rainfall forecast in the future.
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- Article
Time Series Modelling to Predict Rainfall
- RAHUL GROVER,
- SIDDHARTHA SHARMA,
- BALJIT SINGH WALIA,
- CHADETRIK ROUT *
Author Information
Received: 01 Aug 2023 | Accepted: 22 Sep 2023
Abstract
Keywords
ACF | ARIMA | Kriging interpolation | spatial interpolation | time series modelling
Issue
Volume 29, Issue 1How to Cite
GROVER, R.; SHARMA, S.; WALIA, B. S.; ROUT, C. Time Series Modelling to Predict Rainfall. Annals of Agri-bio Research 2024, 29 (1), 89–92.
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Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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