Classification of fire station requirement using machine learning algorithms

Yıl 2018, Sayı: 14, 169 - 175, 31.12.2018

Can Aydın

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

Cited By: 15

Öz

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.

Anahtar Kelimeler

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

Makine Öğrenmesi Algoritmaları Kullanılarak İtfaiye İstasyonu İhtiyacının Sınıflandırılması

Öz

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.

Anahtar Kelimeler

makine öğrenmesi, yer seçim, coğrafi bilgi sistemleri

Kaynakça

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