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

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.

[1]  Zhu Teng,et al.  Fire detection based on hidden Markov models , 2010 .

[2]  Jun Hong Park,et al.  Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework , 2019, Sensors.

[3]  Luke Bisby,et al.  A Review of Factors Affecting the Burning Behaviour of Wood for Application to Tall Timber Construction , 2018, Fire Technology.

[4]  L. Dimuccio,et al.  Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment , 2011 .

[5]  Kostas Kalabokidis,et al.  Identifying wildland fire ignition factors through sensitivity analysis of a neural network , 2009 .

[6]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[7]  M. Vasconcelos,et al.  Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic Regression and Neural Networks , 2001 .

[8]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[9]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[10]  Wisdom M. Dlamini,et al.  A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland , 2010, Environ. Model. Softw..

[11]  Zohre Sadat Pourtaghi,et al.  Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .

[12]  Ebroul Izquierdo,et al.  A Probabilistic Approach for Vision-Based Fire Detection in Videos , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  O. Meddour-Sahar.  Wildfires in Algeria: problems and challenges , 2015 .

[14]  A. Srividya,et al.  Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[15]  Brian Y. Lattimer,et al.  Wildland Fire Spread Modeling Using Convolutional Neural Networks , 2019, Fire Technology.

[16]  Youmin Zhang,et al.  A Survey on Forest Fire Monitoring Using Unmanned Aerial Vehicles , 2019, 2019 3rd International Symposium on Autonomous Systems (ISAS).

[17]  Jiaolong Xu,et al.  Deep Convolutional Neural Networks for Forest Fire Detection , 2016 .

[18]  Xiaoqiao Meng,et al.  Real-time forest fire detection with wireless sensor networks , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[19]  Mohamed Abid,et al.  A review on wireless sensor node architectures , 2014, 2014 9th International Symposium on Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC).

[20]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[21]  Cristina Vega-García,et al.  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 , 2011 .

[22]  Tahar Kechadi,et al.  Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks , 2016 .

[23]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[24]  Yu Gu,et al.  A Novel Accurate Forest Fire Detection System Using Wireless Sensor Networks , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[25]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[26]  Miguel Garcia,et al.  A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification , 2009, Sensors.

[27]  Vinay Chowdary,et al.  Automatic Forest Fire Detection and Monitoring Techniques: A Survey , 2018 .

[28]  Jian Wang,et al.  A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment , 2019, Sensors.

[29]  Plamen Zahariev,et al.  Early Forest Fire Detection Using Drones and Artificial Intelligence , 2019, 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[30]  F. Moreira,et al.  Modeling and mapping wildfire ignition risk in Portugal , 2009 .

[31]  Berend Jan van der Zwaag,et al.  Fire data analysis and feature reduction using computational intelligence methods , 2010 .

[32]  Delano M. Beder,et al.  Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application , 2017, Int. J. Distributed Sens. Networks.

[33]  Vladimir Vujovic,et al.  Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection , 2013 .

[34]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[35]  Ali Karouni,et al.  Applying Decision Tree Algorithm and Neural Networks to Predict Forest Fires in Lebanon , 2014 .

[36]  Yu Chang,et al.  Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China , 2013, Landscape Ecology.

[37]  P. Cortez,et al.  A data mining approach to predict forest fires using meteorological data , 2007 .

[38]  Savvas G. Loizou,et al.  Integrated Forest Monitoring System for Early Fire Detection and Assessment , 2019, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).

[39]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[40]  Hong Bao,et al.  A fire detecting method based on multi-sensor data fusion , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[41]  Sung Wook Baik,et al.  Early fire detection using convolutional neural networks during surveillance for effective disaster management , 2017, Neurocomputing.

[42]  Bernard Zenko,et al.  Estimating the risk of fire outbreaks in the natural environment , 2012, Data Mining and Knowledge Discovery.

[43]  G Puschmann Klaus,et al.  GREGOR Fabry-Perot干渉計およびその付属装置の青色太陽観測画像分光計 , 2013 .

[44]  Dong-Joong Kang,et al.  Fire detection system using random forest classification for image sequences of complex background , 2013 .

[45]  Kostas Kalabokidis,et al.  GIS analysis of physical and human impact on wildfire patterns. , 2002 .

[46]  Vladimir Sherstjuk,et al.  Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[47]  M. Tahar Kechadi,et al.  Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection , 2016, ICC 2016.

[48]  Jian Pei,et al.  2012- Data Mining. Concepts and Techniques, 3rd Edition.pdf , 2012 .

[49]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[50]  Majid Bagheri,et al.  Wireless Sensor Networks for Early Detection of Forest Fires , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[51]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[52]  Long Tang,et al.  Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines , 2015, Sensors.

[53]  I. Burud,et al.  Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia , 2018 .

[54]  Plamen Zahariev,et al.  Emerging Methods for Early Detection of Forest Fires Using Unmanned Aerial Vehicles and Lorawan Sensor Networks , 2018, 2018 28th EAEEIE Annual Conference (EAEEIE).

[55]  A. Murat Ozbayoglu,et al.  Estimation of the Burned Area in Forest Fires Using Computational Intelligence Techniques , 2012, Complex Adaptive Systems.

[56]  Yosio Edemir Shimabukuro,et al.  Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[57]  Aníbal Ollero,et al.  An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection , 2000, IEEE Intell. Syst..

[58]  Gérard Dreyfus,et al.  Neural networks - methodology and applications , 2005 .

[59]  F. J. Lozano,et al.  A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region , 2008 .

[60]  Andrew Rowell,et al.  Global review of forest fires , 2000 .

[61]  Hiromitsu Ishii,et al.  Fire Detection System By Multi-layered Neural Network With Delay Circuit , 1994 .

[62]  D. Wooff.  Logistic Regression: a Self-learning Text, 2nd edn , 2004 .

[63]  Hong Cheng,et al.  Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network , 2016, Sensors.

[64]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[65]  David Caballero,et al.  Decision support system for forest fire protection in the Euro-Mediterranean region , 2012, European Journal of Forest Research.

[66]  M. Udin Harun Al Rasyid,et al.  Prototype of early fire detection system for home monitoring based on Wireless Sensor Network , 2017, 2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA).

[67]  A. Enis Çetin,et al.  Flame detection in video using hidden Markov models , 2005, IEEE International Conference on Image Processing 2005.

[68]  E. Chuvieco,et al.  Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem , 2009 .

[69]  A. Enis Çetin,et al.  Covariance matrix-based fire and flame detection method in video , 2012, Machine Vision and Applications.

[70]  Ahmad A. A. Alkhatib.  A Review on Forest Fire Detection Techniques , 2014, Int. J. Distributed Sens. Networks.