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Application of spectral analysis for temperature forecasting

100%

Kobus P. , Zielinski W.

Colloquium Biometryczne

1999

tom 29

Method of Air Temperature Forecast in Mechanized Longwall Workings in the Conditions of Vietnamese Mines

100%

Truong T. Q. , Łuczak R. , Życzkowski P. , Borowski M.

Inżynieria Mineralna

2021

tom no. 2

353--364

In the most recent years, the Vietnam National Coal - Mineral Industries Holding Corporation Limited (VINACOMIN) has been dynamically developing mechanization technologies in underground coal mines. The climatic conditions of Vietnam, as well as increasing the depth of the coal seams and the production capacity, contribute to an air temperature increasing in mining excavations. The article presents statistical equations enabling air temperature forecasting at the outlet of mechanized longwall workings. The results of numerical calculations, obtained from the solutions of the adopted mathematical descriptions, were compared with the measurement results and the statistical significance of the obtained deviations was determined. The performed analysis allowed to assess the practical usefulness of the adopted model for the air temperature forecasting in the workings of mechanized underground mines in Vietnam. The presented method can be used as a tool for mining services in the fight against the climate threat in underground excavations.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

84%

Papacharalampous G. , Tyralis H. , Koutsoyiannis D.

tom Vol. 66, no. 4

807--831

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

River/stream water temperature forecasting using artifcial intelligence models: a systematic review

84%

Zhu S. , Piotrowski A. P.

Acta Geophysica

2020

tom Vol. 68, no. 5

1433--1442

Water temperature is one of the most important indicators of aquatic system, and accurate forecasting of water temperature is crucial for rivers. It is a complex process to accurately predict stream water temperature as it is impacted by a lot of factors (e.g., meteorological, hydrological, and morphological parameters). In recent years, with the development of computational capacity and artifcial intelligence (AI), AI models have been gradually applied for river water temperature (RWT) forecasting. The current survey aims to provide a systematic review of the AI applications for modeling RWT. The review is to show the progression of advances in AI models. The pros and cons of the established AI models are discussed in detail. Overall, this research will provide references for hydrologists and water resources engineers and planners to better forecast RWT, which will beneft river ecosystem management.