Classification of fire station requirement using machine learning algorithms

Year 2018, Issue: 14, 169 - 175, 31.12.2018

Can Aydın

https://doi.org/10.31590/ejosat.458613

Cited By: 15

Abstract

In crowded cities, the right location of the fire stations in the city is a very vital issue to intervene quickly in fires and to minimize loss of life and property. In selecting the location of the fire station, it is necessary to question the need for a fire station for each region determined by dividing the whole city into specific zones. In this study, firefighting station needs to be classified according to the regions by using machine learning algorithms. Within the scope of the study, a classification study was carried out for the estimation of the station necessity by using data which transportation times of the fire brigades, the population density of the region, the average number of main and assistant vehicles and the presence of the fire station in the region. The purpose of this study is to determine the most successful classification algorithm in the classification of fire station requirements for the 808 regions determined by İzmir Metropolitan Municipality. By analyzing the fire records between 2015-2017, it was determined that the most successful algorithm is Random Forest algorithm with 93.84% classification of the regions. In determining the most successful algorithm, accuracy, mean absolute error (MAE), root mean square error (RMSE) and Kappa values are taken into consideration.

Keywords

Machine Learning, Location Choice, Geographical Information System, Management Information System

Makine Öğrenmesi Algoritmaları Kullanılarak İtfaiye İstasyonu İhtiyacının Sınıflandırılması

Abstract

Kalabalık şehirlerde kent içerisinde itfaiye istasyonlarının doğru yer seçimi, yangınlara hızlı müdahale etmek, can ve mal kaybını en aza indirmek açısından çok hayati bir konudur. İtfaiye istasyonu yer seçiminde; kent bütününü belirli bölgelere ayırarak belirlenen her bir bölge için itfaiye istasyonu ihtiyacının sorgulanması gerekmektedir. Bu çalışmada da mevcut itfaiye istasyonlarından yola çıkarak makine öğrenmesi algoritmaları kullanarak bölgelere göre itfaiye istasyonu ihtiyacının sınıflandırılması gerçekleştirilmiştir. Çalışma kapsamında her bir bölgeye ait, itfaiye araçlarının o bölgeye ulaşım süreleri, bölgenin nüfus yoğunluğu, bölgeye giden ortalama ana ve yardımcı araç sayısı verileri ile bölgedeki itfaiye istasyonu bulunma durumu verileri kullanılarak istasyon ihtiyacının tahmini için sınıflandırılma çalışması gerçekleştirilmiştir. Bu çalışmadaki amaç İzmir Büyükşehir Belediyesinin belirlediği 808 bölgeye dair itfaiye istasyonu ihtiyacı sınıflandırılmasında en başarılı sınıflandırma algoritmasının tespit edilmesidir. 2015-2017 tarihleri arasındaki yangın kayıtları analiz edilerek bölgelerin sınıflandırılmasında %93.84 ile en başarılı algoritmanın Random Forest algoritması olduğu tespit edilmiştir. En başarılı algoritma tespit edilirken doğruluk, ortalama mutlak hata (MAE), kök hata kareler ortalaması (RMSE) ve Kappa değerleri göz önüne alınmıştır.

Keywords

makine öğrenmesi, yer seçim, coğrafi bilgi sistemleri

References

• Alonso-Betanzos, A., Fontenla-Romero, O., Guijarro-Berdiñas, B., Hernández-Pereira, E., Andrade, M. I. P., Jiménez, E., ... & Carballas, T. (2003). An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert systems with applications, 25(4), 545-554.
• Amatulli, G., Rodrigues, M. J., Trombetti, M., & Lovreglio, R. (2006). Assessing long‐term fire risk at local scale by means of decision tree technique. Journal of Geophysical Research: Biogeosciences, 11.
• Song, C., Kwan, M. P., Song, W., & Zhu, J. (2017). A comparison between spatial econometric models and random forest for modeling fire occurrence. Sustainability, 9(5), 819.1(G4).
• Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). ACM.
• Cortez, P., & Morais, A. D. J. R. (2007). A data mining approach to predict forest fires using meteorological data.
• Congalton, R.G., Green, K., 1998. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, first edn. Lewis Publications, Boca Raton p. 137.
• Cover, T., Hart, P., 1967. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27.
• Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences, 63, 22-33.
• De’ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88, 243–251. doi:10.1890/0012-9658(2007)88[243:BTFEMA] 2.0.CO;2
• Elith J, Phillips SJ, Hastie T, Dudı´k M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity & Distributions 17, 43–57. doi:10.1111/J.1472-4642.2010.00725.X
• Guyon, I., 2008. Practical feature selection: from correlation to causality. In: Fogelman-Soulié, F., Perrotta, D., Piskorski, J., Steinberger, R. (Eds.), Mining Massive Data Sets for Security – Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security. IOS Press, Amsterdam, pp. 27–43.
• Guyon, I., 2009. A practical guide to model selection. In: Marie, J. (Ed.), Proceedings of the Machine Learning Summer School. Canberra, Australia, January 26 - February 6, Springer Text in Statistics, Springer p.37.
• Hastie, T., Tibshirani, R., Friedman, J.H., 2009. The elements of statistical learning: data mining, Inference and Prediction, 2nd edn. Springer, New York, USA p. 533.
• Hsu, C.-W., Chang, C.-C., Lin, C.-J., 2010. A Practical Guide to Support Vector ClassificationDepartment of Computer Science, National Taiwan University, Taipei, Taiwan16.Hastie, T., Tibshirani, R., Friedman, J.H., 2009.
• The elements of statistical learning: data mining, Inference and Prediction, 2nd edn. Springer, New York, USA p. 533.
• O’Connor, C. D., Calkin, D. E., & Thompson, M. P. (2017). An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. International journal of wildland fire, 26(7), 587-597.
• Kubat, M., Holte, R. C., & Matwin, S. (1998). Machine learning for the detection of oil spills in satellite radar images. Machine learning, 30(2-3), 195-215.
• Kuncheva, L., 2004. Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons p. 376.
• Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870Marsland, S., 2009.
• Machine Learning: An Algorithmic Perspective. Chapman & Hall/CRC (406 pp.)
• Molina, R., P´erez de la Blanca, N., Taylor, C.C., 1994. Modern statistical techniques. In: Michie, D., Spiegelhalter, D.J., Taylor, C.C. (Eds.), Machine Learning. Neural and Statistical Classification. Ellis Horwood, New York, pp. 29–49.
• Iliadis, L. S. (2005). A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling & Software, 20(5), 613-621.
• Şeker Ş. E. (2012). Karar Ağacı Öğrenmesi. Bilgisayar kavramları internet sitesi: http://bilgisayarkavramlari.sadievrenseker.com/2012/04/11/karar-agaci-ogrenmesi-decision-tree-learning/
• Tzeng, G. H., & Chen, Y. W. (1999). The optimal location of airport fire stations: a fuzzy multi‐objective programming and revised genetic algorithm approach. Transportation Planning and Technology, 23(1), 37-55.
• Witten, I.H., Frank, E., 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier/Morgan Kaufman, San Fransisco, USA p. 525
• Valinski D. 1955. “A Determination of the Optimum Location of Fire-Fighting Units in New York City,” Journal of Operations Research Society of America, 3(4) 494-512.
• Yu, L., Porwal, A., Holden, E.J., Dentith, M.C., 2012. Towards automatic lithological classification from remote sensing data using support vector machines. Comput. Geosci. 45, 229–239.