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Time series ARIMA model to forecast monthly precipitation for Balodabazar, Bemetara and Raipur (Chhattisgarh)

International Journal of Chemical Studies · 2020 · Vol. 8(4) · pp. 180–185
Avinash YaduAnosh GrahamSanadya Anurag

Abstract

Weather forecasting is an important issue in meteorology and scientific research in this research, the Seasonal Auto Regressive Integrated Moving Average (ARIMA) model, which is based on Box-Jenkins method, was adopted to build the forecasting model. The Monthly Rainfall data for Balodabazar, Bemetara, Raipur city for the period (Jan. 1968 to Dec. 2018) was used. The autocorrelation and partial autocorrelation functions for time series data from years 1968 to 2018 were used to identify the most appropriate orders of the ARIMA models. To calculate the model's accuracy and compare among them, statistical criteria such as RMSE and R2 were used. The model Balodabazar (1,0,1) (1,1,1), Bemetara (0,0,2) (0,1,1), Raipur (1,0,1) (0,1,1) gave the most accurate results and used to forecast the monthly rainfall for the period (2019 to 2025) for study regions. This long term prediction will help decision makers in efficient scheduling of urban planning, and rainwater harvesting and crop management.

Hydrological Forecasting Using AIForecasting Techniques and ApplicationsEnergy Load and Power ForecastingAutoregressive integrated moving averageAutocorrelationPartial autocorrelation functionBox–JenkinsRainwater harvestingTime seriesMoving averageEnvironmental sciencePrecipitationStatistics
Citations
0
FWCI
0.00
field-weighted impact
References
10
Percentile
6%
vs. same field & year
References
Time Series Analysis: Forecasting and Control
Technometrics · 1977 · 3,845 citations
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Time series ARIMA model to forecast monthly precipitation for Balodabazar, Bemetara and Raipur (Chhattisgarh) · Scinovex