Science and Research Journal |
UIKTEN |
SAR Journal |
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| ISSN 2619-9955 | eISSN 2619-9963 | Frequency:4/year | Peer Reviewed: Yes | UIKTEN Publisher |
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Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model
Evizal Abdul Kadir, Nur Ezzati Dayana, Sri Listia Rosa, Mahmod Othman, Rizauddin Saian
© 2020 Evizal Abdul Kadir, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: SAR Journal. Volume 3, Issue 3, Pages 101-110, ISSN 2619-9955, https://doi.org/10.18421/SAR33-03, September 2020.
Received: 12 July 2020.
Revised: 01 September 2020.
Accepted: 09 September 2020.
Published: 29 September 2020.
Abstract:
Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.
Keywords – hotspot, prediction, ARIMA, Riau province, Indonesia.
