Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.
This paper presents the results of the application of wavelet decomposition to processing data from the GGP sites (The Global Geodynamics Project). The GGP is an international project within which the Earth's gravity field changes are recorded with high accuracy at a number of stations worldwide using superconducting gravimeters. Data with a 5-second sampling interval from Wettzell and Bad Homburg were used for the research. The wavelet transform enables the investigation of the temporal changes of the oscillation amplitudes or the decomposition of the time series for the analysis of the required frequencies. The wavelet decomposition was performed using the regular orthogonal symmetric Meyer wavelet. The research concerned data from an earthquake period recorded at various locations and a quiet period when the gravimeters worked without any disturbances. The decomposition was followed by the Fast Fourier Transform for signal frequency components and then by correlation analyses of corresponding frequency components (for periods from 10 to 60 000 seconds) for all sensor combinations, for the quiet and the earthquake periods separately. Frequency components defining long term changes for all sensor combinations, as well as combinations between two sensors at the same site for the quiet days are characterised by high correlation coefficients. For the time of the earthquake, the Wettzell site data proved strong correlation for all frequency components, while the Bad Homburg site data showed an unexpected decrease of correlation for the majority of frequency components. The authors also showed that wavelet decomposition can be a good method of data interpolation, especially from the time of earthquakes. Moreover, it is a very useful tool for filtering the data and removing the noises., Janusz Bogusz, Anna Klos and Wieslaw Kosek., and Obsahuje bibliografii
The timing of egg laying by songbirds is known to be strongly affected by local climate, with temperature and precipitation being the most influential factors. However, most research to date relates only to the start of the breeding season: later records and the duration of the whole have not been taken into consideration. In the case of multibrooded species, productivity usually depends on the length of the breeding season. In this work we analysed climatic factors affecting breeding season length of an urban blackbird (Turdus merula) population. The study was conducted in two parks in the city of Szczecin, north-western Poland, spanning 14 breeding seasons since 1997. We found that over the study period, the breeding season became shorter as a result of colder springs and possibly because
of warmer June-July temperatures. Our study revealed a positive relationship between breeding season length and the mean and mean
minimum temperatures in April. Total precipitation in April-July also positively influenced breeding season length. The present survey confirms the influence of temperature and precipitation on the breeding season length of blackbird.
Based on the fuzzy probability distribution and its properties, the paper defines the fuzzy reliability and its characteristics for the double-stage probability model of object. Two fuzzy reliability models are described that are based on the Weibull fuzzy distribution. The results can be applied to determining the reliabililty of real objects in cases where pre-failure times are of a vague numerical type. and Obsahuje seznam literatury
Empirical Mode Decomposition (EMD) is suitable to process the nonlinear and non-stationary time series for filtering noise out to extract the signals. The formal errors are provided along with Global Navigation Satellite System (GNSS) position time series, however, not being considered by the traditional EMD. In this contribution, we proposed a modified approach that called weighted Empirical Mode Decomposition (weighted EMD) to extract signals from GNSS position time series, by constructing the weight factors based on the formal errors. The position time series over the period from 2011 to 2018 of six permanent stations (SCBZ, SCJU, SCMN, HLFY, FJPT, SNXY) were analyzed by weighted EMD, as well as the traditional EMD. The results show that weighted EMD can extract more signals than traditional EMD from original GNSS position time series. Additionally, the fitting errors were reduced 14.52 %, 12.25 % and 8.06 % for North, East and Up components for weighted EMD relative to traditional EMD, respectively. Moreover, 100 simulations of four stations are further carried out to validate the performances of weighted EMD and traditional EMD. The mean Root Mean Squared Errors (RMSEs) are reduced from traditional EMD to weighted EMD with the reductions of 9.08 %, 9.63 % and 6.84 % for East, North and Up components, respectively, which highlights the necessity of considering the formal errors. Therefore, it reasonable to conclude that weighted EMD can extract the signals more than traditional EMD, which can be suggested to analyze GNSS position time series with formal errors., Xiaomeng Qiu, Fengwei Wang, Yunqi Zhou and Shijian Zhou., and Obsahuje bibliografii
A new genus, Weketrema, is erected in the family Lecithasteridae for the species hitherto known as Lecithophyllum hawaiiense. Weketrema hawaiiense (Yamaguti, 1970) comb. n. is redescribed from Scolopsis bilineatus (Bloch) (Perciformes: Nemipteridae) from Lizard Island and Heron Island, Queensland, Plectorhinchus gibbosus (Lacepède) (Perciformes: Haemulidae) from Heron Island and Cheilodactylus nigripes Richardson (Perciformes: Cheilodactylidae) and Latridopsis forsteri (Castelnau) (Perciformes: Latridae) from Stanley, northern Tasmania. The new genus is distinguished from related members of the family Lecithasteridae by its complete lack of a sinus-sac. Although placed in the subfamily Lecithasterinae pro tem, its true subfamily position is not entirely clear. Comment is made on its unusual distribution, both in terms of zoogeography and hosts.