Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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 951–961
Al-Janabi S (2018) Smart system to create an optimal higher education environment using IDA and IOTs. Int J Comput Appl. https://doi.org/10.1080/1206212X.2018.1512460
Al-Janabi S, Alkaim AF (2019) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput. https://doi.org/10.1007/s00500-019-03972-x
Aslan YE, Korpeoglu I, Ulusoy O (2012) A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput Environ Urban Syst 36(6):614–625
Garcia-Jimenez S, Jurio A, Pagola M, De Miguel L, Barrenechea E, Sola H (2017) Forest fire detection: a fuzzy system approach based on overlap indices. Appli Soft Comput 52:834–842
Kaur H, Sood SK (2019) Adaptive neuro fuzzy inference system (ANFIS) based wildfire risk assessment. J Exp Theor Artif Intell 31(4):599–619
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 18:101–111
Molina-Pico A, Cuesta-Frau D, Araujo A, Alejandre J, Rozas A (2016) Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network. J Sens 2016:8325845
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 2018:1639715
Ulucinar AR, Korpeoglu I, Cetin AE (2014) A Wi-Fi cluster based wireless sensor network application and deployment for wildfire detection. Int J Distrib Sens Netw 10(10):651957
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
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kaur, H., Sood, S.K. Soft-computing-centric framework for wildfire monitoring, prediction and forecasting. Soft Comput 24, 9651–9661 (2020). https://doi.org/10.1007/s00500-019-04477-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04477-3