Abstract
Fire disasters are one the most challenging accidents that can take place in any urban buildings like houses, offices, hospitals, colleges and industries. These accidents which the world faces now, have never been more frequent and fatal, leading to innumerable loses, damage of expensive equipment and unparalleled human lives. The concrete landscapes are threatened by fire disasters, which have prolifically outnumbered in the last decade, both in intensity and frequency. Thus, to minimize the impact of fire disasters, adoption of well planned, intelligent and robust fire detection technology harnessing the niches of machine learning is necessary for early warning and coordinated prevention and response approach. In this research a novel hybrid ensemble technology based machine algorithm using maximum averaging voting classifier has been designed for fire detection in buildings. The proposed model uses feature engineering pre-processing techniques followed by a synergistic integration of four classifiers namely, logistic regression, support vector machine (SVM), Decision tree and Naive Bayes classifier to yield better prediction and improved robustness. A database from NIST has been chosen to validate the research under different fire scenarios. Results indicate an improved classification accuracy of the proposed ensemble technique as compared to reported literatures. After validating the algorithm, the firmware has been implemented on a laboratory developed prototype of smart multi sensor, embedded fire detection node. The designed smart hardware is successfully able to transmit the sensed data wirelessly onto the cloud platform for further data analytics in real time with high precision and reduced root mean square error (MAE).
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References
Bu F, Gharajeh MS (2019) Intelligent and vision-based fire detection systems: a survey. Image Vis Comput 91:103803
Wu H, Wu D, Zhao J (2019) An intelligent fire detection approach through cameras based on computer vision methods. Process Saf Environ Prot 127:245–256
Meacham BJ (1994) The use of artificial intelligence techniques for signal discrimination in fire detection systems. J Fire Prot Eng 6:125–136
Ko B, Cheong K, Nam J (2009) Fire detection based on vision sensor and support vector machines. Fire Safety J 44:322–329
Olivas JA (2003) Forest fire prediction and management using soft computing Proceedings of the International Conference on Industrial Informatics (INDIN), pp. 338–344
Mahdipour E, Dadkhah C (2010) Automatic fire detection based on soft computing techniques: review from 2000 to Artif. Intell Rev 42(4):895–934
Anezakis VD, Demertzis K, Iliadis L, Spartalis S (2016) A hybrid soft computing approach producing robust forest fire risk indices. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer International Publishing, pp. 191–203.
Aertsen W, Kint V, Van J, Orshoven K., Ozkan, Muys B (2009) Performance of modelling techniques for the prediction of forest site index: a case study for pine and cedar in the Taurus mountains. Turkey XIII World Forestry Congress, pp. 18–23
Angelis AD, Ricotta C, Conedera M, Pezzatti GB (2015) Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions. PLoS ONE 10(2):0116875
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 Ecol. Manage 275:117–212
West AM, Kumar S, Jarnevich CS (2016) Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming USA. Clim Change 134(4):565–577
Bedia J, Herrera S, Camia A, Moreno JM, Gutiérrez JM (2014) Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios. Clim Change 122(1–2):185–199
Amatulli G, Camia A, San-Miguel-Ayanz J (2013) Estimating future burned areas under changing climate in the EU-Mediterranean countries Sci. Total Environ 450:209–222
Satir O, Berberoglu S, Donmez C (2015) Mapping regional forest fire probability using artificial neural network model in a mediterranean forest ecosystem. Geomatics Nat Hazards Risk. https://doi.org/10.1080/19475705.2015.1084541
Özbayoğlu AM, Bozer R (2012) Estimation of the burned area in forest fires using computational intelligence techniques. Proc Comput Sci 12:282–287
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 Forest Res 45(7):783–792
Denham M, Cortés AT , Margalef E (2008) Applying a dynamic data driven genetic algorithm to improve forest fire spread prediction International Conference on Computational Science. Springer Berlin Heidelberg, pp. 36–45
Bui DT, Bui QT, Nguyen QP, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric For Meteorol 233:32–44
Artés T, Cencerrado A, Cortés A, Margalef T (2016) Time aware genetic algorithm for forest fire propagation prediction: exploiting multi-core platforms. Concurrency Computat: Pract Exper. https://doi.org/10.1002/cpe.3837
Hong H, Naghibi SA, Dashtpagerdi MM, Pourghasemi HR, Chen W (2017) A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10:167
Bisquert M, Caselles E, Sánchez JM, Caselles V (2012) Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. Int J Wildland Fire 21:1025–1029
Goldarag YJ, Mohammadzadeh A, Ardakani AS (2016) Fire risk assessment using neural network and logistic regression. J Indian Soc Rem Sens. https://doi.org/10.1007/s12524-016-0557-6
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 Geoinf 11:265–272
Safi Y, Bouroumi A (2013) Prediction of forest fires using artificial neural networksAppl. Math Sci 7:271–286
Basheer I, Hajmeer AM (2000) Artificial neural networks: fundamentals computing design and application. J Microbiol Methods 43:3–31
Sakr GE, Elhajj IH, Mitri G (2011) Efficient forest fire occurrence prediction for developing countries using two weather parametersEng. Appl Artif Intell 24:888–894
Xie DW, Shi SL (2014) Prediction for burned area of forest fires based on SVM model. Appl Mech Mater 513:4084–4089
Ko BC, Cheong KH, Nam JY (2009) Fire detection based on vision sensor and support vector machines. Fire Saf J 44:322–329
Zhao J, Zhang Z, Han S, Qu C, Yuan Z, Zhang D (2011) SVM based forest fire detection using static and dynamic features. Computer Sci Inform Syst 8:821–841
Wen T, Zhang B, University LT (2014) Prediction model for open-pit coal mine slope stability based on random forest. Sci Technol Rev 32:105–109
Haifley T (2002) Linear logistic regression: an introduction. IEEE International Integrated Reliability Workshop Final Report, pp. 184-187 https://doi.org/10.1109/IRWS.2002.1194264
Rao P, Manikandan J (2016) Design and evaluation of logistic regression model for pattern recognition systems. IEEE Annual India Conference (INDICON), pp. 1-6. https://doi.org/10.1109/INDICON.2016.7839010
Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15:142–147. https://doi.org/10.1109/TGE.1977.6498972
Navada A, Ansari AN, Patil S, Sonkamble BA (2011) Overview of use of decision tree algorithms in machine learning. IEEE Control and System Graduate Research Colloquium, Shah Alam. https://doi.org/10.1109/ICSGRC.2011.5991826
Zhao H, Yin S, Ru Z (2012) Relevance vector machine applied to slope stability analysis. Int J Numer Anal Meth Geomech 36:643–652
Yang Y, Jianping L, Yang Y (2015) The research of the fast SVM classifier method. 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, pp. 121–124. https://doi.org/10.1109/ICCWAMTIP.2015.7493959.
Mousa A, Ghasemian B, Shirzadi A, Shahabi H et al (2019) A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Geocarto Int 34:1427–1457. https://doi.org/10.1080/10106049.2018.1499820
Chok YH, Jaksa MB, Kaggwa WS, Griffiths DV, Fenton GA (2016) Neural network prediction of the reliability of heterogeneous cohesive slopes. Int J Numer Anal Meth Geomech 40:1556–1569
Ren J, Lee SD, Chen X, Kao B, Cheng R, Cheung D (2009) Naive Bayes Classification of Uncertain Data. Ninth IEEE International Conference on Data Mining, Miami, FL, pp. 944–949, https://doi.org/10.1109/ICDM.2009.90
Dietterich TG (1997) Machine-learning research. AI Mag 18:97
Cho SB, Ryu J (2002) Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features. Proc IEEE 90:1744–1753
Qi C, Tang X (2018) A hybrid ensemble method for improved prediction of slope stability. Int J Numer Anal Methods Geomech 42:1823–1839. https://doi.org/10.1002/nag.2834
Dogan A, Birant D (2019) A Weighted Majority Voting Ensemble Approach for Classification. 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, 1–6, https://doi.org/10.1109/UBMK.2019.8907028
Jiakun Z, Ju J, Si C, Ruifeng Z, Bilin Y, Qingfang L (2020) A weighted hybrid ensemble method for classifying imbalanced data” Knowledge-Based Systems. ISSN 203:106087
He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284
Oscar C, Przemysław K, Bogdan T (2011) Special issue on hybrid and ensemble methods in machine learning. New Gener Comput. 29:241–244. https://doi.org/10.1007/s00354-011-0300-3
Wang N, Zhao S, Cui S, Fan W (2021) A hybrid ensemble learning method for the identification of gang-related arson cases. Knowl-Based Syst 218:0950–7051. https://doi.org/10.1016/j.knosys.106875
Gandhi Pandey M (2015) Hybrid Ensemble of classifiers using voting. International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 399-404, https://doi.org/10.1109/ICGCIoT.2015.7380496
Liu H, Gegov A, Cocea M (2015) Hybrid ensemble learning approach for generation of classification rules. International Conference on Machine Learning and Cybernetics (ICMLC) . pp. 377–382
Hsu K (2012) Hybrid ensembles of decision trees and artificial neural networks. IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom) 25-29, https://doi.org/10.1109/CyberneticsCom.2012.6381610
Yang S, Chen L, Yan T, Zhao Y, Fan Y (2017) An ensemble classification algorithm for convolutional neural network based on AdaBoost. IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS). pp. 401–406
Lu H, Gao H, Ye M, Wang X (2019) A Hybrid ensemble algorithm combining adaboost and genetic algorithm for cancer classification with gene expression data. IEEE/ACM Trans Comput Biol Bioinf 01:1–1
Saqlain M, Jargalsaikhan B, Lee JY (2019) A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. IEEE Trans Semicond Manuf 32:171–182. https://doi.org/10.1109/TSM.2019.2904306
Vahini Ezhilraman S, Srinivasan S et al (2019) Breast cancer detection using gradient boost ensemble decision tree classifier. Int J Eng Adv Technol 9:2249–8958
Renjie X, Haifeng L, Kangjie L, Lin C, Liu Y (2021) A forest fire detection system based on ensemble learning. Forests 12(2):217. https://doi.org/10.3390/f12020217
Sujatha G, Usha Rani K (2020) A Comprehensive Hybrid Ensemble Method with Feature Selection Techniques. Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol.15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_8
Zhao J, Jin J, Chen S, Zhang R, Yu B, Liu Q (2020) A w eighted hybrid ensemble method for classifying imbalanced data, Knowledge-Based Systems, 203. ISSN 106087:0950–7051. https://doi.org/10.1016/j.knosys.2020.106087
Wang Y, Pan Z, Zheng J, Qian L, Li M (2019) A Hybrid ensemble method for pulsar candidate classification. Instrum Methods Astrophys. https://doi.org/10.1007/s10509-019-3602-4
Verma B, Hassan SZ (2011) Hybrid ensemble approach for classification. Appl Intell 34:258–278. https://doi.org/10.1007/s10489-009-0194-7
Chaudhary A, Kolhe S, Kamal R (2016) A hybrid ensemble for classification in multiclass datasets: an application to oilseed disease dataset. Comput Electron Agric 124:65–72. https://doi.org/10.1016/j.compag.2016.03.026
Kardani N, Zhou A, Nazem M, Shen SL (2021) Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J Rock Mech Geotech Eng 13(1):188–201. https://doi.org/10.1016/j.jrmge.2020.05.011
Rosadi D, Andriyani W (2021) Prediction of forest fire using ensemble method. J Phys: Conf Ser 1918:042043
Xie Y, Peng M (2018) Forest fire forecasting using ensemble learning approaches. Neural Comput Appl 31:4541–4550
Stracher GB et al (2019) Gases generated during the low-temperature oxidation and pyrolysis of coal and the effects on methane-air flammable limits. In: Stracher GB (ed) Coal and peat fires: a global perspective. Elsevier, Amsterdam, pp 157–171
Nikunj C et al (2004) Ensemble Data Mining Methods NASA Ames Research Centre, USA
Kohavi R ( 2001) A study of cross‐validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on ArtificialIntelligence, pp. 1137‐1143
Jiao Z, Zhang Y, Xin J et al (2019) A deep learning based forest fire detection approach using uav and yolov3. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China, 2019, pp. 1–5
Lin Z, Chen F, Li B et al (2019) A contextual and multitemporal active-fire detection algorithm based on FengYun-2G SVISSR data. IEEE Trans Geosci Remote Sens 57(11):8840–8852
Jang E, Kang Y, Im J, Lee DW, Yoon J, Kim SK (2019) Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea. Remote Sensing 11(3):271
Shi F, Qian H, Chen W, Huang M, Wan Z (2020) A fire monitoring and alarm system based on YOLOv3 with OHEM. In: Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China, 27–29 July 2020, pp. 7322–7327
Kim B, Lee J (2019) A video-based fire detection using deep learning models. Appl Sci 9:2862
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Jana, S., Shome, S.K. Hybrid Ensemble Based Machine Learning for Smart Building Fire Detection Using Multi Modal Sensor Data. Fire Technol 59, 473–496 (2023). https://doi.org/10.1007/s10694-022-01347-7
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DOI: https://doi.org/10.1007/s10694-022-01347-7