Development of Ice Prediction Mobile Application

Year 2021, Volume: 24 Issue: 4, 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

-

References

• [1] Balbay, A., Esen, M., “Yollardaki kar ve buzu önleyici sistemler." Fırat Üniversitesi Doğu Araştırmaları Dergisi, 6.1: 169-174, (2007).
• [2] Joshi, A., Kamble, B., Joshi, V., Kajale, K. and Dhange, N., “Weather Forecasting and Climate Changing Using Data Mining Application”. International Journal of Advanced Research in Computer and Communication Engineering, 4(3): 19-21, (2015).
• [3] Drezga, I. and Rahman, S., “Short-term load forecasting with local ANN predictors”, IEEE Transactions on Power Systems, 14(3): 844-850, (1999).
• [4] Chen, S. M. and Hwang, J. R., “Temperature prediction using fuzzy time series”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30(2): 263-275, (2000).
• [5] De Giorgi, M. G., Ficarella, A. and Tarantino, M., “Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods”, Department of Engineering for Innovation, 36(7): 3968-3978, (2011).
• [6] Lee, M., & Kim, M., “Development of Real-Time Road Surface Condition Determination Algorithm Using an Automatic Weather System”, In 2016 6th International Conference on IT Convergence and Security (ICITCS), Prague, Czech Republic, pp. 1-2, (2016).
• [7] Yang C. H., Park M. S.,Yun D. G., “A Road Surface Temperature Prediction Modeling for Road Weather Information System”, Journal of Korean Society of Transportation, Vol. 29, No. 2, pp. 123-131, (2011).
• [8] Krsmanc R., SajnSlak A., Carman S., Korosec M., “Metro Model Testing at Slovenian Road Weather Stations and Suggestions for Further Improvements”, 16th International Road Weather Conference in Helsinki, (2012).
• [9] Bogren, J., Gustavsson, T., Nordin, L., Ekström, P., & Sjölander, P. O., “SRIS—Slippery Road Information System”, In Proc., of the 14th Standing International Road Weather Commission—SIRWEC Conf, Stockholm, Sweden, (2008).
• [10] Yang C. H., Yun D. G., Sung J. G., “Validation of a Road Surface Temperature Prediction Model Using Real-time Weather Forecasts”, KSCE Journal of Civil Engineering, 16, pp. 1289-1294, (2012).
• [11] Jokela, M., Kutila, M., & Le, L., “Road condition monitoring system based on a stereo camera”, In 2009 IEEE 5th International conference on intelligent computer communication and processing, Romania, pp. 423-428, (2009)
• [12] Yamada, M., Oshima, T., Ueda, K., Horiba, I., & Yamamoto, S., “A study of the road surface condition detection technique for deployment on a vehicle”, JSAE review, 24(2): 183-188, (2003).
• [13] Dregza, I. and Rahman, S., “Short-term load forecasting with local ANN predictors”, IEEE Transactions on Power Systems, 14(3): 844-850, (1999).
• [14] Taylor, J.W. and Buizza, R., Neural network load forecasting with weather ensemble predictions, IEEE Transactions on Power Systems, 17(3):626-632, (2002).
• [15] Sapankevych, N., I. and Sankar, R., “Time series prediction, using support vector machines: a survey”, IEEE Computational Intelligence Magazine, 4(2): 24-38, (2009).
• [16] Lu, W., Wang, W., Leung, Y. T. A., Lo, S.-M., Yuen, R. K. K., Xu, Z. and Fan, H., “Air pollutant parameter forecasting using support vector machines”, Conference on Proceedings of the 2002 International Joint Neural Networks, USA, 630-635, (2002).
• [17] Rao, T., Rajasekhar, N. and Rajinikanth, T. V., “An efficient approach for Weather forecasting using Support Vector Machines”, International Conference on Computer Technology and Science, 208-212, (2012).
• [18] Perrone, M. P., “General Averaging Results for Convex Optimization”, Proceeding of the 1993 Connectionist Models Summer School, Psychology Press, New York,USA, 364-371, (1994).
• [19] Pal, N. R., Pal, S., Das, J. and Majumdar, K., “SOFM-MLP: A hybrid neural network for atmospheric temperature prediction”. IEEE Transactions on Geoscience and Remote Sensing, 41(12): 2783-2791, (2003).
• [20] Chen, N., Qian, Z., Nabney, l. T. and Meng, X., “Wind power forecasts using Gaussian processes and numerical weather prediction”, IEEE Transactions on Power Systems, 29(2): 656-665, (2014).
• [21] Costa, M. and Pasero, E., “Artificial Neural Systems for Verglass Forecast”, International Joint Conference on Neural Networks, Washington, USA, 258-262, (2001).
• [22] Li, J., “A Combination of DE and SVM with Feature Selection for Road Icing Forecast”, 2nd International Asia Conference on Informatics in Control, Automation and Robotics, 509-512, (2010).
• [23] Mandale, A. and Jadhawar, B. A., Weather forecast prediction: a Data Mining application. International Journal of Engineering Research and General Science 3(2), 1279-1284, (2015).
• [24] Lampton, A. and Valasek, J., “Prediction of icing effects on the lateral/directional stability and control of light airplanes”, Aerospace Science and Technology, 23(1): 305–311, (2012).
• [25] McCann, D. W., “NNICE – A Neural Network Aircraft Icing Algorithm”, Environmental Modelling & Software, 20(10): 1335–1342, (2005).
• [26] Wang, Z., & Li, C., “River ice forecasting based on genetic neural network”, In 2009 International Conference on Information Engineering and Computer Science, China, (pp. 1-4), (2009).
• [27] Savadjiev, K. and Farzaneh, M., “Modeling of icing and ice shedding on overhead power lines based on statistical analysis of meteorological data”, IEEE Transactions on Power Delivery, 19(2): 715-721, (2004).
• [28] Luo, Y., Li, Y., Yao, Y., & Zhan, X., “Research on power transmission line ice prediction system based on BP neural network”, In Proceedings of 2012 International Conference on Measurement, Information and Control, Vol. 2, pp. 817-820, (2012).
• [29] Chen, S. Q. and Guo, S. Z., “Fuzzy Prediction”, Guiyang: Guizhou Science & Technology Press, 332-342, (1994).
• [30] Liu, C., Liu, H. W., Wang, Y. S., Lu, J. Z., Xu, X. J. and Tan, Y. J., “Research of icing thickness on transmission lines based on fuzzy Markov chain prediction”, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, Beijing, China, 327-330, (2014).
• [31] Huang, J., Yang, H., Hunan, Y. W., “Forecast of Line Ice-Coating Degree Using Circumfluence Index & Support Vector Machine Method”, International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, China, 327-330, (2016).
• [32] Li, Q., Li, P., Zhang, Q., Ren, W., Cao, M. and Gao, S., “Icing Load Prediction for Overhead Power Lines Based on SVM”, Proceedings of 2011 International Conference on Modelling, Identification and Control, China, 104-108, (2011).
• [33] Patel, B. G., Dabhi, V. K., Tyagi, U. and Shah, P. B., “A Survey on Location Based Application Development for Android Platform”, International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 731-739, (2015).
• [34] López, V. F., Medina, S. L. and de Paz, J. F., “Taranis: Neural networks and intelligent agents in the early warning against floods”, Expert Systems with Applications, 39(11): 10031-10037, (2012).
• [35] Lee, M., Bak, C. and Lee, J. W., “A prediction and auto-execution system of smartphone application services based on user context-awareness”, Journal of Systems Architecture, 60(8): 702-710, (2014).
• [36] Mantas, V.M., Liu, Z., Pereira, A.J.S.C., “A web service and android application for the distribution of rainfall estimates and earth observation data”, Computers & Geosciences, 77: 66–76, (2015).
• [37] Sezgin, E., & Çelik, Y., “Veri madenciliğinde kayıp veriler için kullanılan yöntemlerin karşılaştırılması”, Akademik Bilişim Konferansı, Akdeniz Üniversitesi, Türkiye, 23-25, (2013).
• [38] Shen, J., Pei, Z. J., & Lee, E. S., “Support vector regression in the analysis of soft-pad grinding of wire-sawn silicon wafers”, Cybernetics and Information Technologies Systems and Applications (CITSA), 21-25, (2004).
• [39] Güner, N., & Çomak, E., “Mühendislik öğrencilerinin matematik I derslerindeki başarısının destek vektör makineleri kullanılarak tahmin edilmesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 17(2): 87-96, (2011).
• [40] Dan, L., Lihua, L. and Zhaoxin, Z., “Research of Text Categorization on WEKA”, Third International Conference on Intelligent System Design and Engineering Applications (ISDEA), Hong Kong, China, 1129-1131, (2013).
• [41] Dantas, L. and Valença, M., “Using Neural Networks In The Identification Of Signatures For Prediction Of Alzheimer's Disease”, IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), Limassol, Cyprus, 238-242, (2014).
• [42] Maysanjaya, D., Nugroho, H. A and Setiawan, N. O., “A Comparison of Classification Methods on Diagnosis of Thyroid Diseases”, 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 89-93, (2015).
• [43] Rumelhart, D. E., Hinton, G. E. and Williams, R. J., “Learning Internal Representations by Error Propagation”, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, (1): 318-362, (1986).
• [44] Halder, C., Paul, J. and Roy, K., “Comparison of The Classifiers In Bangla Handwritten Numeral Recognition”, 2012 International Conference on Communication and Computing (ICRCC), Tiruvannamalai, India, 272-276, (2012).
• [45] Duran, F., & Teke, M., “Akıllı yol durum sensoru tasarımı”, Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 11(1): 396-401, (2019).