Forecast of climatologically events using improved grey model (Case Study: Qazvin Province Climatology)
الموضوعات :
مریم کریمی خواجه قیاسی
1
(MSc. Student, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.)
علیرضا علینژاد
2
(Associate Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.)
الکلمات المفتاحية: Forecast, metabolism, Grey Model,
ملخص المقالة :
The theory of grey system is used when sufficient information of the community under study is not in hand. The grey forecast model is proper when the information variety is fix and certain. Grey model can apply some additional computations to improve forecasting activities when data is insufficient. Through using improved grey model, the assessment error decreases significantly. This study made use of the mean maximum daily temperature data collected by Qazvin meteorological station, from August 2001 to August 2013. The findings revealed that the grey model metabolism method can reduce errors and improve the precision of forecasting the mean variable of maximum daily temperature.
Aliari Shorehdeli, M., Teshnehlab, M., & Khaki Sedigh, A. (2004). Short Term Prediction of Air Pollution via Multilayer Perceptron Neural Networks and Memorable Line of Delay. Essays Collection of the Six Conference on Intelligent Systems. Shahid Bahonar University of Kerman. 139-164.
Balouchian, H. (2014). Modeling & Predicting the Number of Road Accidents. Rahvar Scientific & Propagative Magazine, 11(26) , 115-135.
Hsu, C. C., & Chen, C. Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and management, 44(14), 2241-2249.
Huang, M., He, Y., & Cen, H. (2007). Predictive analysis on electric-power supply and demand in China. Renewable Energy, 32(7), 1165-1174.
Huang, Y. P., & Yang, H. P. (2004). Using hybrid grey model to achieve revenue assurance of telecommunication companies. Journal of Grey System, 7(1), 38-49.
Javanmard, H. & Faghidian S. F. (2014). Prediction of Raw Oil Price of OPEC via Grey Model of Forecast. Economic Modelling Seasonal, 27,91-114.
Kazemi, A., Modarres, M., & Mehregan M. R. (2011). Prediction of Energy Demand of Transportation Sector using Grey Markov’s Chain Model: Case Study of Iran. Industrial Management, 7,117-132.
Khalili, N., Khodashenas, S. R., Davari, K., & Mousavi Bayegi, M. (2010). Prediction of Daily Rain through artificial Neural Networks. Cast study: Mashhad Synoptic Stations. Watershed Management Researches Magazine, 89, 7-15.
Li, G. D., Yamaguchi, D., & Nagai, M. (2007). A GM (1, 1)–Markov chain combined model with an application to predict the number of Chinese international airlines. Technological Forecasting and Social Change, 74(8), 1465-1481.
Liu, C., Shu, T., Chen, S., Wang, S., Lai, K. K., & Gan, L. (2016). An improved grey neural network model for predicting transportation disruptions. Expert Systems with Applications, 45, 331-340.
Malek, A. M., Dabbaghi, A., Arianejad, M. (2011). Fundamentals of Grey Systems Theory with a review on uncertainty methods, Termeh Publications, Iran.
Mohammadi, A., & Molaei, N. (2010). Application of Grey Multi- Criteria Decision Making in Assessing Companies’ Performances. Industrial Management Magazine, 2(4), 125-142.
Tongyuan, H., & Yue, W. (2007, December). Forecasting Model of Urban Traffic Accidents Based on Gray Model GM (1, 1). In Digital Media and its Application in Museum & Heritages, Second Workshop on (pp. 438-441). IEEE.
Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2001). Applied hybrid grey model to forecast seasonal time series. Technological Forecasting and Social Change, 67(2-3), 291-302.
