Soft-computing-centric framework for wildfire monitoring, prediction and forecasting
Document Type: Journal Article
Author(s): Harkiran Kaur; Sandeep K. Sood
Publication Year: 2020

Cataloging Information

  • fire forecasting
  • fire prediction
  • fog computing
  • Holt-Winter's forecasting
  • IoT - Internet of Things
  • K-nearest neighbor
  • temporal mining
Record Maintained By:
Record Last Modified: May 28, 2020
FRAMES Record Number: 58942


Wildfires are exorbitantly cataclysmic disasters that lead to the destruction of forest cover, wildlife, land resources, human assets, reduced soil fertility and global warming. Every year wildfires wreck havoc across the globe. Therefore, there is a need of an efficient and reliable system for real-time wildfire monitoring to dilute their disastrous effects. Internet of Things (IoT) has demonstrated remarkable evolution and has been successfully adopted in environmental monitoring domain. This paper proposes a collaborative IoT-Fog-Cloud framework based on soft computing techniques for real-time wildfire monitoring, prediction and forecasting. The framework includes proposals for classifying a forest terrain into its appropriate wildfire proneness class using fuzzy K-nearest-neighbor classifier by analyzing wildfire influent attributes and wildfire consequent attributes. Moreover, real-time emergency alert generation mechanism based on temporal mining has been proposed in event of adverse wildfire conditions. Estimation of future wildfire proneness levels of a forest terrain using Holt–Winter’s forecasting model also forms an integral part of the proposed framework. Implementation results reveal that high values of accuracy, specificity, sensitivity and precision averaging to 93.97%, 92.35%, 93.01% and 91.24% are attained for determination of wildfire proneness of a forest terrain. Low values of mean absolute error (MAE), mean square error (MSE), mean absolute percentage error and root mean square error (RMSE) averaging to 0.665, 2, 11.705 and 1.405, respectively, for real-time alert generation are registered, thereby increasing the utility of the proposed framework. Wildfire proneness forecasting also yields highly accurate results with low values of MAE, MSE and RMSE averaging to 0.166667, 0.25 and 0.492799, respectively.

Online Link(s):
Kaur, Harkiran; Sood, Sandeep K. 2020. Soft-computing-centric framework for wildfire monitoring, prediction and forecasting. Soft Computing 24(13):9651-9661.