Abstract
One of the most ubiquitous cause of worldwide deforestation and devastation of wildlife is fire. To control fire and reach the forest area in time is not always possible. Consequently, the level of destruction is often high. Therefore, predicting fires well in time and taking immediate action is of utmost importance. However, traditional fire prediction approaches often fail to detect fire in time. Therefore, a more reliable approach like the Internet of Things (IoT) needs to be adopted. IoT sensors can not only observe the real-time conditions of an area, but it can also predict fire when combined with Machine learning. This paper provides an insight into the use of Machine Learning models towards the occurrence of forest fires. In this context, eight Machine Learning algorithms: Boosted Decision Trees, Decision Forest Classifier, Decision Jungle Classifier, Averaged Perceptron, 2-Class Bayes Point Machine, Local Deep Support Vector Machine (SVM), Logistic Regression and Binary Neural Network model have been implemented. Results suggest that the Boosted decision tree model with the Area Under Curve (AUC) value of 0.78 is the most suitable candidate for a fire prediction model. Based on the results, we propose a novel IoT-based smart Fire prediction system that would consider both meteorological data and images for early fire prediction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alkhatib AA (2014) A review on forest fire detection techniques. International Journal of Distributed Sensor Networks 10(3):1–12
Allison RS, Johnston JM, Craig G, Jennings S (2016) Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors 16(8):6988–7004
Amatulli G, Peréz-Cabello F, de la Riva J (2007) Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty. Ecol Model 200(3–4):321–333
Bogue R (2013) Sensors for fire detection. Sens Rev 33:99–103
Castelli M, Vanneschi L, Popovič A (2015) Predicting burned areas of forest fires: an artificial intelligence approach. Fire ecology 11(1):106–118
Cortez P, Morais ADJR (2007) A data mining approach to predict forest fires using meteorological data
Denham M, Wendt K, Bianchini G, Cortés A, Margalef T (2012) Dynamic data-driven genetic algorithm for forest fire spread prediction. Journal of Computational Science 3(5):398–404
Herutomo A, Abdurohman M, Suwastika NA, Prabowo S, Wijiutomo CW (2015) Forest fire detection system reliability test using wireless sensor network and OpenMTC communication platform. Third IEEE international conference on information and communication technology pp 87–91
Imteaj A, Rahman T, Hossain MK, Alam MS, Rahat SA (2017) An IoT based fire alarming and authentication system for workhouse using raspberry pi 3. IEEE international conference on electrical, computer and communication engineering pp 899–904
Kang DH, Park MS, Kim HS, Kim DY, Kim SH, Son HJ, Lee SG (2017) Room temperature control and fire alarm/suppression IoT service using MQTT on AWS. IEEE international conference on platform technology and service pp 1–5
Kansal A, Singh Y, Kumar N, Mohindru V (2015) Detection of forest fires using machine learning technique: a perspective. Third IEEE international conference on image information processing, pp 241–245
Lazarescu MT (2013) Design of a WSN platform for long-term environmental monitoring for IoT applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3(1):45–54
Lee BS, Woodard PM, Titus SJ (1996) Applying neural network technology to human-caused wildfire occurrence prediction. AI applications
Li Z, Nadon S, Cihlar J (2000) Satellite-based detection of Canadian boreal forest fires: development and application of the algorithm. Int J Remote Sens 21(16):3057–3069
Martínez-de Dios JR, Merino L, Ollero A (2005) Fire detection using autonomous aerial vehicles with infrared and visual cameras. IFAC Proceedings Volumes 38(1):660–665
May A, Mitchell V, Piper J (2014) A user centred design evaluation of the potential benefits of advanced wireless sensor networks for fire-in-tunnel emergency response. Fire Saf J 63:79–88
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. Journal of Sensors 2016:1–8
Morse T, Cundy M, Kytomaa H (2017) Vehicle fires resulting from hot surface ignition of grass and leaves, SAE Technical Paper
Muhammad K, Ahmad J, Mehmood I, Rho S, Baik SW (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174–18183
Nie W, Wang W, Huang X (2019) SRNet: structured relevance feature learning network from skeleton data for human action recognition. IEEE Access 7:132161–132172
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JM (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random Forest. For Ecol Manag 275:117–129
Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic 64:72–84
Qu Z, Hu H, Yu L (2009) Study of a prediction model for Forest fire-initial burnt area on meteorological factors. IEEE international workshop on intelligent systems and applications pp 1–4
Roe BP, Yang HJ, Zhu J, Liu Y, Stancu I, McGregor G (2005) Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 543(2–3):577–584
Sakr GE, Elhajj IH, Mitri G, Wejinya UC (2010) Artificial intelligence for forest fire prediction. IEEE international conference on advanced intelligent mechatronics pp 1311–1316
Taylor SW, Woolford DG, Dean CB, Martell DL (2013) Wildfire prediction to inform management: statistical science challenges. Stat Sci 28:586–615
Woodruff K (2017) Introduction to boosted decision trees. New Mexico State University. https://indico.fnal.gov/event/15356/contribution/1/material/slides/0.pdf. Accessed 16 April 2020
Ying-cong Z, Jing Y (2013) A study on the fire IOT development strategy. Procedia Engineering 52:314–319
Yuan C, Zhang Y, Liu Z (2015) A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can J For Res 45(7):783–792
Zhang J, Li W, Han N, Kan J (2008) Forest fire detection system based on a ZigBee wireless sensor network. Frontiers of Forestry in China 3(3):369–374
Author information
Authors and Affiliations
Corresponding author
Additional information
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
Sharma, R., Rani, S. & Memon, I. A smart approach for fire prediction under uncertain conditions using machine learning. Multimed Tools Appl 79, 28155–28168 (2020). https://doi.org/10.1007/s11042-020-09347-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09347-x