Open AccessPrediction of Meteorological Parameter Time Series Data for the Forest Fire Early Warning System
Author(s) -
A S Hadisuwito,
Fadratul Hafinaz Hassan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1807/1/012029
Subject(s) - wind speed , environmental science , warning system , meteorology , index (typography) , hazard , time series , fire hazard , backpropagation , artificial neural network , relative humidity , series (stratigraphy) , statistics , computer science , mathematics , geography , machine learning , telecommunications , paleontology , chemistry , environmental protection , organic chemistry , world wide web , biology
A forest fire early warning system must be developed to reduce the impact of greater community losses. One effort to develop an early warning system is to use a forest fire hazard index as a potential assessment guide. The main factor which is a parameter in the fire hazard index calculation method is the meteorological parameter. In general, to know today’s fire hazard index is calculated from today’s weather conditions, but the need for an early warning system is to know the future fire hazard index. Based on a series of meteorological conditions data held for thirty-six months, using the backpropagation algorithm, it is estimated that the meteorological conditions will be several months to come. Several meteorological parameters have their respective roles, the unknown contribution of which is calculated. In this study, each parameter will be measured by predicting time series data and compared with the results of calculations. The method of calculating the forest fire index used is the McArthur Forest Fire Danger Index with the meteorological parameter elements are temperature, relative humidity, wind speed, and drought factor. Each parameter was trained in artificial neural networks and tested its predictions to produce accuracy for data series temperatures of 91.67%, the relative humidity of 83.33%, and wind speed of 50%.