IJEP 44(10): 912-921 : Vol. 44 Issue. 10 (October 2024)
Kanni Raj Arumugam Pillai* and Yara EzAl Deen Sultan
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Department of Chemistry, School of Sciences and Humanities, Chennai – 600 062, Tamil Nadu, India
Abstract
The goal of forest fire modelling is to understand and forecast the behaviour of forest fires through numerical simulation. To simulate fire dangers and fire spread behaviour, statistical techniques are applied to historical fire incidents. A number of fire-based data tables are used to create the Canadian fire weather index (FWI). FWI is a straightforward method that effectively identifies vegetation’s fire vulnerability. Simple empirical equations are used to calculate the Australian fire danger index (FDI). Similar to FWI is FDI. Models for forest fires that combine stochastic and mathematical techniques accurately mimic the spread of the fire and forecast its severity. Physical models use heat transport through convection and radiation as well as the heat balance of sources (fire) and sinks (forest areas close to the fire source). MATLAB or Delphi programmes are used to solve the differential equations relating to heat transfer. The world crown fire modelling experiment’s predicted fire spread behaviour closely matches the predictions made by mathematical approaches. In real-time forestry, forest landscape, wind speed, ambient temperature and rainfall level are obtained from satellite imagery and weather forecasting and machine learning is used for predicting the vulnerability to forest fire and the rate of spreading of the fire.
Keywords
Forest fire, Rate of spread, Fire vulnerability, Fire weather index, Fire danger index, Cellular automata, Physical model
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