Soft-computing-centric framework for wildfire monitoring, prediction and forecasting
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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.
KeywordsInternet of Things (IoT) Fog computing Fuzzy K-nearest neighbor (FK-NN) Temporal mining Holt–Winter’s forecasting
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Ali SH (2012) A novel tool (FP-KC) for handle the three main dimensions reduction and association rule mining. In: 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT). IEEE, pp 951–961Google Scholar
- Lin H, Liu X, Wang X, Liu Y (2018) A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks. Sustain Comput Inform Syst 18:101–111Google Scholar
- Toledo-Castro J, Caballero-Gil P, Rodríguez-Pérez N, Santos-González I, Hernández-Goya C, Aguasca-Colomo R (2018) Forest fire prevention, detection, and fighting based on fuzzy logic and wireless sensor networks. Complexity 2018:1639715Google Scholar
- Weather in May, 2018 in Hoshiarpur, Punjab, India. https://www.timeanddate.com/weather/india/hoshiarpur/historic?month=5&year=2018. Last Accessed on 21 May 2019