利用灰色理论预测干旱区露点温度的参数选择:以伊朗为例

M. Qolipour, A. Mostafaeipour, Mostafa Rezaei, Elham Behnam, Hossein Goudarzi, A. Razmjoo
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引用次数: 2

摘要

露点是空气中的水蒸气以与蒸发速率相同的速度凝结成液体的温度。露点研究在干旱少雨地区和其他水文气候条件不同的地区具有重要意义。本文首次应用灰色理论,提出了一种识别影响露点温度预测的重要参数的框架方法。灰色理论对具有缺失数据和不确定条件的问题的参数进行估计和排序的能力意味着它在上述应用中具有良好的潜力。本研究通过文献综述选取8个参数,分别为:水平面上太阳总辐射(H)、水汽压(VP)、大气压力(P)、日照时数(n)、最低气温(Tmin)、最高气温(Tmax)、平均气温(Tavg)、相对湿度(RH)。这项研究是在伊朗的阿巴德市进行的,使用了2005年至2015年10年的数据。结果表明,RH、Tavg、P、Tmax、Tmin、H、n和Vp是预测露点温度最重要、最有效的参数,其灰色可能性度分别为0.534、0.551、0.608、0.622、0.635、0.695、0.697和0.712。该方法还根据参数对露点温度预测的有效性对参数进行了排序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selection of parameters to predict dew point temperature in arid lands using Grey theory: a case study of Iran
Dew point is the temperature at which water vapor in the air condenses into liquid with the same rate it evaporates. Dew point study is important in arid lands with low rainfall, also in other regions with various hydrological and climatological conditions. In this study, the Grey theory is applied for the first time to propose a framework approach to identify the important parameters affecting the prediction of dew point temperature. The ability of Grey theory to estimate and rank the parameters of a problem with missing data and uncertain conditions means that it has a good potential for mentioned application. For this research, 8 parameters are selected using literature review including: global solar radiation on a horizontal surface (H), water vapor pressure (VP), atmospheric pressure (P), sunshine duration (n), minimum air temperature (Tmin), maximum air temperature (Tmax), average air temperature (Tavg), and Relative Humidity (RH). The study is conducted for the city of Abadeh in Iran by using the data pertaining to a 10 year period between 2005 and 2015. The findings show that RH, Tavg, P, Tmax, Tmin, H, n and Vp with the grey possibility degrees of, respectively, 0.534, 0.551, 0.608, 0.622, 0.635, 0.695, 0.697 and 0.712, are the most important and effective parameters in prediction of dew point temperature. The proposed method also prioritizes the studied parameters in the order of their effectiveness on predicted dew point temperature.
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