A Hybrid Methodology for Short Term Temperature Forecasting  

作　　者：Wissam Abdallah Nassib Abdallah Jean-Marie Marion Mohamad Oueidat Pierre Chauvet Wissam Abdallah;Nassib Abdallah;Jean-Marie Marion;Mohamad Oueidat;Pierre Chauvet(Angevin Research Laboratory in Systems Engineering, University of Angers, Angers, France;Computer Science and Systems Laboratory, Polytech Marseille, Aix Marseille University, Aix-en-Provence, France;Angevin Research Laboratory in Systems Engineering, Université Catholique de l’Ouest, Angers, France;Department of Industrial Engineering and Maintenance, Faculty of Technology, Lebanese University, Beirut, Lebanon;Angevin Research Laboratory in Systems Engineering, Université Catholique de l’Ouest, Angers, France)

机构地区：[1]Angevin Research Laboratory in Systems Engineering, University of Angers, Angers, France [2]Computer Science and Systems Laboratory, Polytech Marseille, Aix Marseille University, Aix-en-Provence, France [3]Angevin Research Laboratory in Systems Engineering, Université Catholique de l’Ouest, Angers, France [4]Department of Industrial Engineering and Maintenance, Faculty of Technology, Lebanese University, Beirut, Lebanon [5]Angevin Research Laboratory in Systems Engineering, Université Catholique de l’Ouest, Angers, France

出　　处：《International Journal of Intelligence Science》2020年第3期65-81,共17页智能科学国际期刊（英文）

摘　　要：Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

关 键 词：Time Series Analysis ARIMA Auto Regressive Integrated Moving Average Weather Forecasting Model MULTIPROCESSING 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]