基于数据挖掘的气候分析和天气预报:土耳其阿达纳省的案例

Mümine Kaya Keleş, Elif Kavak
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摘要

近年来,随着气候的影响,气候分析和天气预报被公认为最重要的自然课题之一。阿达纳位于地中海地区,属于地中海气候,是土耳其最恶劣的城市之一。由于其地中海气候,在阿达纳一年中的每个季节都观察到不同的天气条件。在这项研究中,它的目的是做一个月平均天气预报在阿达纳未来12个月。本研究中使用的数据集是自2019年1月以来从两个不同的天气网站收集的,包括Adana的每日天气值,包括最高温度、最低温度、湿度和风速。利用Weka数据挖掘工具,采用线性回归方法对该数据集进行天气预报。在使用线性回归方法预测天气时,计算了变量之间的关系,建立了变量之间的方程进行预测。根据所获得的结果,已经观察到,当要求以日为基础的预测时,不能得到很好的结果,但当要求以月平均值时,可以得到几乎相同的结果。此外,估计的结果是近似的,研究结果发现相关系数为0.9832。此外,我们还得出结论,在预测天气时,当使用月平均值进行预测时,线性回归(一种数据挖掘技术)会产生积极的结果。
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CLIMATE ANALYSIS AND WEATHER FORECASTING WITH DATA MINING: THE CASE OF ADANA PROVINCE IN TURKEY
In recent years, with the effect of the climate analysis and weather forecasting are accepted as one of the most important natural topics. Adana, located in the Mediterranean region, has a Mediterranean climate and is one of Turkey's roughest cities. Because of its Mediterranean climate, different weather conditions are observed in every season of the year in Adana. In this study, it is aimed to make a monthly average weather forecast for the next 12 months in Adana. The dataset used in this study was collected from two different weather websites since January 2019 and includes Adana's daily weather values including maximum temperature, minimum temperature, humidity, and wind speed. Weather forecasting was performed on this dataset using linear regression method with Weka data mining tool. While predicting the weather using the linear regression method, it is provided that the relationship between the variables was calculated, and an equation between them was established to make predictions. According to the results obtained, it has been observed that good results cannot be obtained when day-based forecasting is requested, but almost the same results are obtained when the monthly average are requested. In addition, the results of the estimations were approximate, and as a result of the study, the Correlation Coefficient was found to be 0.9832.Additionally, it was concluded that while forecasting the weather, linear regression, which is a data mining technique, yields positive results when the forecast is made with the monthly average.
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