التفاصيل البيبلوغرافية
| العنوان: |
Studying the lime Scale Dependence of Environmental Variables Predictability Using Fractal Analysis. |
| المؤلفون: |
YUVAL1 lavuy@tx.technion.ac.il, BRODAY, DAVID M.1 |
| المصدر: |
Environmental Science & Technology. 6/15/2010, Vol. 44 Issue 12, p4629-4634. 6p. |
| مصطلحات موضوعية: |
*AIR pollution, *STATISTICAL correlation, AIR quality research, METEOROLOGICAL research, FRACTALS, ENVIRONMENTAL exposure, PREDICTION models, TEMPORAL databases |
| مستخلص: |
Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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