Development of Ice Prediction Mobile Application

Year 2021, , 1543 - 1555, 01.12.2021

Hatice Kabaoğlu , Emine Uçar , Fecir Duran

https://doi.org/10.2339/politeknik.735408

Cited By: 2

Abstract

Cold weather and heavy winter conditions cause icing on the roads, and therefore many fatal, injured and materially damaged accidents occur every year. In this study, an icing prediction algorithm and mobile application has been developed to prevent accidents caused by icing on the roads. With the developed application, it is aimed to give preliminary information about the formation of icing in line with the routes of the drivers. In the study, the temperature, dew point, sensed temperature, wind intensity, wind direction, relative humidity, wind speed input parameters were taken from the road condition sensor and weather stations. At the exit, double classification was made with icing information. After the training of the system is completed, weather forecast information is obtained from the meteorology and icing forecast is made for the next 12 hours on the developed mobile application. In addition, in order to measure and compare the accuracy of the developed system, the multi-layer perceptron (MLP) neural network model and linear and nonlinear support vector machines (SVM) methods are used. Considering the classification accuracy of the algorithms used in the study, based on the total number of correctly classified samples, it was seen that the model of the MLP performed best with 87,26% accuracy rate, followed by the linear SVM model with 86,32% and our proposed model with 75,47% accuracy rate. However, in the developed prediction algorithm, although the classification accuracy is lower compared to others, it has been observed that the number of samples used in training increases, the icing prediction accuracy increases in direct proportion.

Keywords

Machine learning, mobile application, icing, prediction algorithm

Project Number

-

Buzlanma Tahmini Yapan Mobil Uygulama Geliştirilmesi

Abstract

Soğuk hava ve ağır kış şartları, yollarda buzlanmaya sebep olmakta ve bu nedenle her yıl birçok ölümlü, yaralanmalı ve maddi hasarlı kaza meydana gelmektedir. Bu çalışmada yollardaki buzlanmadan kaynaklı kazaların önlenmesine yönelik bir buzlanma tahmin algoritması ve mobil uygulama geliştirilmiştir. Geliştirilen uygulama ile sürücülerin güzergâhları doğrultusunda buzlanma oluşumu ile ilgili ön bilgi verilmesi amaçlanmaktadır. Çalışmada yol durum sensörü ve hava istasyonlarından alınan sıcaklık, çiğ noktası, hissedilen sıcaklık, rüzgâr şiddeti, rüzgâr yönü, bağıl nem, rüzgâr hızı giriş parametreleri olarak kullanılmıştır. Çıkışta ise buzlanma bilgisi ile ikili sınıflandırma yapılmıştır. Sistemin eğitimi tamamlandıktan sonra meteorolojiden hava durumu tahmin bilgisi alınarak, geliştirilen mobil uygulama üzerinde gelecek 12 saat için buzlanma tahmini yapılmaktadır. Ayrıca geliştirilen sistemin doğruluğunu ölçmek ve karşılaştırma yapabilmek için sınıflandırma alanında en çok kullanılan yöntemlerden çok katmanlı algılayıcı (ÇKA) sinir ağı modeli ile doğrusal ve doğrusal olmayan destek vektör makineleri (DVM) yöntemleri kullanılmıştır. Çalışmada kullanılan algoritmaların sınıflandırma doğruluğuna bakıldığında, toplam doğru sınıflandırılan örnek sayısı temel alındığında ÇKA modelinin %87,26 doğruluk oranı ile en iyi sonucu verdiği, ardından %86,32 ile doğrusal DVM modelinin geldiği önerilen modelimizin ise %75,47 doğruluk oranına sahip olduğu görülmüştür. Ancak geliştirilen tahmin algoritmasında sınıflandırma doğruluğu diğerlerine kıyasla daha az olmasına rağmen eğitimde kullanılan örnek sayısı arttıkça, buzlanma tahmin doğruluğunun da doğru orantılı olarak arttığı gözlemlenmiştir.  

Keywords

Makine öğrenmesi, mobil uygulama, buzlanma, tahmin algoritması

Supporting Institution

Yok

Project Number

-

Thanks

-

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