Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

Wahyuningsih, Sri; Goejantoro, Rito; Rizki, Nanda Arista

doi:10.1088/1755-1315/144/1/012022

Cited by 3 publications

(3 citation statements)

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“…On the other hand, an analysis of the driving factors, the pressures that arise and the responses to the fires was reported in [7], [8]. The research was conducted in the Pelalawan area of Riau province using two approaches: the analysis of biophysical factors such as soil properties and semi structured interviews for social and policy aspects.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Kadir¹,

Dayana²,

Rosa³

et al. 2020

SAR Journal

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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.

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Section: Related Workmentioning

confidence: 99%

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Kadir¹,

Dayana²,

Rosa³

et al. 2020

SAR Journal

View full text Add to dashboard Cite

show abstract

“…Holt–Winters methods have been popular in business and industry for decades. They are widely used in providing point forecasts in many real applications (Liu & Wu, 2022; Wahyuningsih et al, 2018; Wang et al, 2018; Wan & Li, 2018) as they are easy to interpret and require little computer storage; see Hyndman et al (2008). However, less attention has been paid on building prediction intervals for Holt–Winters methods.…”

Section: Introductionmentioning

confidence: 99%

Empirical prediction intervals for additive Holt–Winters methods under misspecification

Yang,

Tang,

Yau

2023

Journal of Forecasting

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Holt–Winters (HW) methods have been widely used by practitioners for the prediction of time series. However, traditional prediction intervals associated with the HW methods are only theoretically justified for a few types of SARIMA processes. In this article, we propose an empirical prediction interval for a general class of prediction procedures containing the HW methods as special cases. We establish the asymptotic validity of the prediction intervals under mild conditions, which allow model misspecification. Simulation experiments and an application to financial time series are provided to illustrate the good performance of the prediction intervals.

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“…Studies on the SS model as a forecasting method are limited. Reservoir inflow modeling, climatic data, catchment rainfalls, and land hotspots are the parameters modeled and forecasted by the SS method [33][34][35][36]. For example, Vijayakumar and Vennila [33] compared the autoregressive integrated moving average (ARIMA) model with the SS method.…”

Section: Introductionmentioning

confidence: 99%

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

2023

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The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses TS datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R2) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R2 of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R2 of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time.

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Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

Cited by 3 publications

References 0 publications

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Empirical prediction intervals for additive Holt–Winters methods under misspecification

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

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