Spatio-temporal modeling of fire hotspots using GSTAR(1;1) model with meteorology based weight matrices

Year 2025, Volume: 54 Issue: 6, 2525 - 2542, 30.12.2025

Nurfitri Imro'ah , Nur'ainul Miftahul Huda , Hesty Pratiwi , Muhammad Yahya Ayyash

https://doi.org/10.15672/hujms.1726843

Abstract

This study proposes a new approach to spatio-temporal modeling of fire data by utilizing the generalized space-time autoregressive (1;1) model and the construction of spatial weight matrices based on dynamic environmental variables. The data used consisted of the highest confidence level for fire spots in West Kalimantan from April 2020 to September 2023. These fire points were separated into seven grids, each measuring 3 $\times$ 3 degrees. This research analyzes three different types of weight matrices, departing from the typical methods that only employ inverse distance weights. These weight matrices are the inverse distance, the distance relative to the average maximum wind speed, and the distance relative to the average maximum total rainfall. Evaluations using the mean absolute percentage error, the mean square error, and the mean absolute error demonstrate that the weight matrix based on wind speed generates the most accurate model by producing the highest level of precision. A more accurate and adaptable representation of the fire spread process is made possible by incorporating meteorological elements into the spatial structure of the model. It is the primary innovation featured in the model. In an effort to increase the accuracy of forest and land fire predictions in tropical regions such as West Kalimantan, these findings highlight the importance of keeping atmospheric dynamics in mind when performing spatial weighting.

Keywords

Environment , meteorological factors , weight matrix , wind speed

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