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Fire Technology
Published in: Fire Technology 2/2021

05-11-2020

A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

Author: Faroudja Abid

Published in: Fire Technology | Issue 2/2021

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Abstract

Forest fires are one of the major environmental concerns, each year millions of hectares are destroyed over the world, causing economic and ecological damage as well as human lives. Thus, predicting such an environmental issue becomes a critical concern to mitigate this threat. Several technologies and new methods have been proposed to predict and detect forest fires. The trend is toward the integration of artificial intelligence to automate the prediction and detection of fire occurrence. This paper presents a comprehensive survey of the machine learning algorithms based forest fires prediction and detection systems. First, a brief introduction to the forest fire concern is given. Then, various methods and systems in forest fires prediction and detection systems are reviewed. Besides works that reported fire prediction and detection systems, studies that assessed the factors influencing the fire occurrence and risk are discussed. The main issues and outcomes within each study are presented and discussed.
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Literature
1.
Qiang Z, Meka EZ, Anderson RC, Kakabadse Y (2011) Forests nature at your service. UNEP report. The magazine of the United Nations Environment Program
2.
Rowell A, Moore PF (2000) Global review of forest fires. WWF/IUCN, Gland, Switzerland
3.
Sahar O (2015) Wildfires in Algeria: problems and challenges. IFOREST 8(6):818–826CrossRef
4.
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422CrossRef
5.
Karray F, Jmal MW, Abid M, BenSaleh MS, Obeid AM (2014) A review on wireless sensor node architectures. In: Proceedings of 9th international symposium on reconfigurable and communication-centric systems-on-chip (ReCoSoC). IEEE, pp 1–8
6.
Alkhatib A (2014) A review on forest fire detection techniques. Int J Distrib Sens N. Hindawi Publishing 2014:1673–1683
7.
Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier
8.
Dreyfus G (2005) Neural networks methodology and applications. Springer, HeidelbergMATH
9.
Dimuccio LA, Ferreira R, Lucio C, De Almeida AC (2011) Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment. Int J Wildland Fire (IJWF) 20(6):776–791CrossRef
10.
Hodges JL, Lattimer BY, Hughes J (2019) Wildland fire spread modeling using convolutional neural networks. Fire Technol 55:1–28CrossRef
11.
Karouni A, Daya B, Chauvet P (2014) Applying decision tree algorithm and neural networks to predict forest fires in Lebanon. J Theor Appl Inf Technol 63(2):282–291
12.
McCormick R J, Brandner TA, Allen TFH (1999) Toward a theory of mesoscale wildfire modeling- a complex systems approach using artificial neural networks. In: Proceedings of the joint fire science conference and workshop, pp 1–12
13.
Özbayoğlu AM, Bozer R (2012) Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Comput Sci 12:282–287CrossRef
14.
Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinos I (2009) Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat Hazards 50(1):125–143CrossRef
15.
Liu W, Wanga Z, Xiaohui L, Nianyin Z, Yurong L, Elsaadin F (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26CrossRef
16.
Arrue BC, Ollero A, Martinez de Dios JR (2000) An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intell Syst App 15(3):64–73CrossRef
17.
Kalabokidis K, Xanthopoulos G, Moore P, Caballero D, George K, Llorens J, Roussou O, Christos V (2012) Decision support system for forest fire protection in the Euro-Mediterranean region. Eur J Forest Res. Springer 131(3): 597–608
18.
Liu Y, Gu Y, Chen G, Ji Y, Li J (2011) A novel accurate forest fire detection system using wireless sensor networks. In: Proceedings of the IEEE Seventh international conference on mobile ad-hoc and sensor networks (MSN). IEEE, pp 52–59
19.
Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30–42CrossRef
20.
Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GFB, Hansen MC (2009) Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. Int J Appl Earth Obs 11:265–272
21.
Park JH, Lee S, Yun S, Kim H, Kim WT (2019) Dependable fire detection system with multifunctional artificial intelligence framework. Sensors 19(9):1–22CrossRef
22.
Zhang Q, Xu J, Xu L, Guo H (2016) Deep convolutional neural networks for forest fire detection. International Forum on Management, Education and Information Technology Application (IFMEITA), pp 568–575
23.
Breejen ED, Breuers M, Cremer F, Kemp R, Roos M, Schutte K, de Vries JS (1998) Autonomous forest fire detection. In: III international conference on forest fire research and 14th conference on fire and forest meteorology, vol 2, pp 2003–2012
24.
Hefeeda M, Bagheri M (2007) Wireless sensor networks for early detection of forest. In: Proceedings of the IEEE international conference on mobile ad hoc and sensor systems (MASS). IEEE, pp 1–6
25.
26.
Yan X, Cheng H, Zhao Y, Yu W, Huang H, Zheng X (2016) Real-time identification of smoldering and flaming combustion phases in forest using a wireless sensor network-based multi-sensor system and artificial neural network. Sensors (Basel) 16(8):1–10
27.
28.
Lloret J, Vega-Garcia M, Bri D, Sendra S (2009) A wireless sensor network deployment for rural and forest fire detection and verification. Sensors 9(11):8722–8747CrossRef
29.
Chowdary V, Gupta MK (2018) Automatic forest fire detection and monitoring techniques: a survey. In: Singh R, Choudhury S, Gehlot A. (eds) Intelligent communication, control and devices. Advances in intelligent systems and computing, vol 624. Springer, pp 1111–1117
30.
Georgiades G, Papageorgiou XS, Loizou SG (2019) Integrated forest monitoring system for early fire detection and assessment. In: Proceedings of the IEEE 6th international conference on control, decision and information technologies. IEEE, pp 1–6
31.
Hossain FMA, Zhang Y, Yuan C (2019) A survey on forest fire monitoring using unmanned aerial vehicles. In: Proceedings of the 3rd international symposium on autonomous systems (ISAS). IEEE, pp 484–489
32.
Kinaneva D, Hristov G, Raychev J, Zahariev P (2019) Early forest fire detection using drones and artificial intelligence. In Proceedings of the 42nd international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 1060–1065. https://​doi.​org/​10.​23919/​MIPRO.​2019.​8756696
33.
Sherstjuk V, Zharikova M, Sokol I (2018) Forest fire monitoring system based on UAV team, remote sensing, and image processing. In Proceeding of the IEEE second international conference on data stream mining &processing (DSMP). IEEE, pp 590–594
34.
Berie HT, Burud I (2018) Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia. Eur J Remote Sens 51(1):326–335CrossRef
35.
Hristov G, Raychev J, Kinaneva D, Zahariev P (2018) Emerging methods for early detection of forest fires using unmanned aerial vehicles and Lora WAN sensor networks. In: Proceedings of the 28th EAEEIE annual conference. IEEE, pp 1–9
36.
Kleinbaum DG, Klein M (2010) Logistic regression: a self-learning text, 2nd edn. Springer, pp 1–513
37.
Chang Y, Zhu Z, Bu R, Chen H, Feng Y, Li Y, Hu Y, Wang Z (2013) Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landsc Ecol 28(10):1989–2004
38.
Selvam S, Manisha A, Vidhya J, Venkatramanan S (2019) Fundamentals of GIS, Chapter 1. In: GIS and geostatistical techniques for groundwater science. Elsevier, pp 1–115
39.
Catry FX, Rego F, Bação F, Moreira F (2009) Modeling and mapping wildfire ignition risk in Portugal. Int J Wildland Fire 18:921–931CrossRef
40.
Chuvieco E, González I, Verdú F, Aguado I, Yebra M (2009) Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. Int J Wildland Fire 18:430–441CrossRef
41.
De Vasconcelos MJP, Sliva S, Tome M, Alvim M, Perelra JMC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogramm Eng Remote Sens 67(1):73–81
42.
Kalabokidis KD, Konstantinidis P, Vasilakos C (2002) GIS analysis of physical and human impact on wildfire patterns. In Viegas X (ed.) Forest fire research & wildland fire safety. Millpress
43.
Vega-Garcia C, Lee BS, Woodard PM, Titus SJ (1996) Applying neural network technology to human caused wildfire occurrence prediction. AI Applications 10(3):9–18
44.
Padilla M, Vega-Garcıa C (2011) On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. Int J Wildland Fire 20:46–58CrossRef
45.
46.
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
47.
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, pp 1–358
48.
49.
Stojanova D, Panov P, Kobler A, Džeroski S, Taškova K (2006) Learning to predict forest fires with different data mining techniques. In: Conference on data mining and data warehouses (SiKDD 2006), Ljubljana, Slovenia, pp 255–258
50.
Stojanova D, Kobler A, Ogrinc P, Ženko B, Džeroski S (2010) Estimating the risk of fire outbreaks in the natural environment. Data Min Knowl Disc 24:411–442CrossRef
51.
Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Investigation of general indicators in on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic 64:72–84CrossRef
52.
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest Ecol Manag 275:117–129CrossRef
53.
Lozano FJ, Suárez-Seoane S, Kelly M, Luis E (2008) A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region. Remote Sens Environ 112:708–719CrossRef
54.
Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199CrossRef
55.
Giuntini FT, Beder DM, Ueyama J (2017) Exploiting self-organization and fault tolerance in wireless sensor networks: a case study on wildfire detection application. Int J Distrib Sens Netw 13(4):1–16CrossRef
56.
Maksimović M, Vujović V (2013) Comparative analysis of data mining techniques applied to wireless sensor network data for fire detection. J Inform Technol Appl (JITA) 6(2):65–77
57.
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999CrossRef
58.
Cortez P, Morais A (2007) A data mining approach to predict forest fires using meteorological data. In Neves J, Santos MF, Machado J (eds) New trends in artificial intelligence, Proceedings of the 13th EPIA- Portuguese conference on artificial intelligence. APPIA, pp 512–523
59.
Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. In: European conference on computer vision (ECCV). Springer Verlag, pp 589–600
60.
61.
Dlamini WM (2010) A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland. Environ Model Softw 25:199–208CrossRef
62.
Borges PVK, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuits Syst Video Technol 20(5):721–731CrossRef
63.
Bahrepour M, Van der Zwaag BJ, Meratnia N, Havinga P (2010) Fire data analysis and feature reduction using computational intelligence methods. In: Advances in intelligent decision technologies smart innovation, systems and technologies, vol 4. Springer, pp 289–298
64.
Saoudi M, Bounceur A, Euler R, Kechadi T (2016) Data mining techniques applied to wireless sensor networks for early forest fire detection. In: International conference on internet of things and cloud computing (ICC). ACM, pp 1–7
65.
Mahmoud MAI, Ren H (2018) Forest fire detection using a rule-based image processing algorithm and temporal variation. Math Probl Eng. Hindawi 2018:1–9
66.
Baum LE, Petrie T (1966) Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat 37(6):1554–1563MathSciNetCrossRef
67.
Kim O, Kang DJ (2013) Fire detection system using random forest classification for image sequences of complex background. Opt Eng 52(6):1–10CrossRef
68.
Töreyin BU, Dedeoğlu Y, Çetin AE (2005) Flame detection in video using hidden Markov models. In: IEEE international conference on image processing (ICIP), vol 2, pp 1230–1233
69.
70.
71.
Manjunatha P, Verma AK, Srividya A (2008) Multi-sensor data fusion in cluster based wireless sensor networks using Fuzzy logic method. In: IEEE region 10 colloquium and the third international conference on industrial and information systems, ICIIS. IEEE, pp 1–6
72.
Moulianitis VC, Thanellas G, Xanthopoulos N, Aspragathos NA (2018) Evaluation of UAV based schemes for forest fire monitoring. In: Advances in service and industrial robotics. International conference on robotics in Alpe-Adria Danube Region (RAAD). Mechanisms and machine science, vol 67. Springer International Publishing, pp 143–150
73.
Chen S, Hong BAO, Zeng X, Yang Y (2003) A fire detecting method based on multi-sensor data fusion. In: Proceeding of the IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 3775–3780
74.
Saputra FA, Rasyid MUH Al, Abiantoro BA (2017) Prototype of early fire detection system for home monitoring based on wireless sensor network. In: Proceeding of the international electronics symposium on engineering technology and applications (IES-ETA). IEEE, pp 39–44
Metadata
Title
A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems
Author
Faroudja Abid
Publication date
05-11-2020
Publisher
Springer US
Published in
Fire Technology / Issue 2/2021
Print ISSN: 0015-2684
Electronic ISSN: 1572-8099
DOI
https://doi.org/10.1007/s10694-020-01056-z

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