International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 11, No. 1, 8 Jan. 2019
Cover page and Table of Contents: PDF (size: 514KB)
Classification of the Fire Station Requirement with Using Machine Learning Algorithms
Full Text (PDF, 514KB), PP.24-30
Views: 0 Downloads: 0
Author(s)
Can Aydin
1,*
Index Terms
Machine Learning, Selection of Location, Geographical Information Systems, Management Information System
Abstract
In crowded cities, selection of the suitable location for fire stations within the town is a vital issue in terms of rapid response to fires and minimizing loss of life and property. For the selection of the suitable fire station location, at first it is necessary to divide the whole city into certain zones and the need for a fire station service should be questioned for each zone. In this study, based on existing fire stations service area, classification of fire station requirement by zones was carried out using machine learning classification algorithms. In order to estimate fire station requirement according to the zones, a classification study was conducted by using some data such as the travel time of the fire engines to zone from closed fire stations, population density of the zone, the mean number of main and assistant vehicles travelling to the zone from closed fire stations, and the fire station existence data in the zone. The purpose of this study was to determine the most successful classification algorithm for the classification of the fire station requirement of 808 zones determined by Izmir Metropolitan Municipality. As a result of the analysis of fire records between 2015 and 2017, it was found that for the classification of the zones, the most successful algorithm was Random Forest algorithm with 93.84% accuracy rate. Experimental evaluation of the study; according to the 5-minute access distance of the existing fire stations, the fire station requirements of the regions and the fire station needs of the regions covered by the machine learning algorithm classification results were found to be 85.43% similar.
Cite This Paper
Can Aydın, "Classification of the Fire Station Requirement with Using Machine Learning Algorithms", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.1, pp.24-30, 2019. DOI:10.5815/ijitcs.2019.01.03
Reference
[1]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.
[2]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
[3]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).
[4]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.
[5]Cortez, P., & Morais, A. D. J. R. (2007). A data mining approach to predict forest fires using meteorological data.
[6]Congalton, R.G., Green, K., 1998. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, first edn. Lewis Publications, Boca Raton p. 137.
[7]Cover, T., Hart, P., 1967. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27.
[8]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.
[9]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
[10]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
[11]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.
[12]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.
[13]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.
[14]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.
[15]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.
[16]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.
[17]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.
[18]Kuncheva, L., 2004. Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons p. 376.
[19]Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870
[20]Marsland, S., 2009. Machine Learning: An Algorithmic Perspective. Chapman & Hall/CRC (406 pp.)
[21]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.
[22]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.
[23]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.
[24]Şeker Ş. E. (2012). Karar Ağacı Öğrenmesi. Bilgisayar kavramları internet sitesi: http://bilgisayarkavramlari.sadievrenseker.com/2012/04/11/karar-agaci-ogrenmesi-decision-tree-learning/
[25]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.
[26]Witten, I.H., Frank, E., 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier/Morgan Kaufman, San Fransisco, USA p. 525
[27]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.
[28]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.