A time delay artificial neural network approach for flow routing in a river system

International audienceRiver flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications

DOWNSCALING OUTPUTS OF THE GENERAL CIRCULATION MODELS FOR THE PREDICTION OF THE MONTHLY PRECIPITATION IN A STATION BY USING NEURAL NETWORKS

Η πρόβλεψη της κλιματικής αλλαγής βασίζεται κυρίως στις εκτιμήσεις των μοντέλων γενικής κυκλοφορίας (General Circulation Models) (GCMs). Οι εκτιμήσεις αυτές αναφέρονται σε μεγάλη χωρική ανάλυση και είναι επιβεβλημένη η εφαρμογή διαδικασιών για τον υποβιβασμό κλίμακας (downscaling) σε κλίμακα είτε τοπική είτε σταθμού. Στην εργασία αυτή ο υποβιβασμός περιγράφει τη σχέση μεταξύ μετεωρολογικών μεταβλητών μεγάλης κλίμακας που προσομοιώνονται από GCMs μοντέλα όπως είναι η βροχόπτωση, η θερμοκρασία, η υγρασία κ.λπ. και της μηνιαίας βροχόπτωσης ενός σταθμού και γίνεται με την εφαρμογή τεχνητών νευρωνικών δικτύων (Artificial Neural Networks) (ANNs) σε συνδυασμό με ανάλυση κυρίων συνιστωσών (PCA). Από την ανάλυση των αποτελεσμάτων συμπεραίνεται η καταλληλότητα των ANNs μοντέλων για τον υποβιβασμό κλίμακας μετεωρολογικών μεταβλητών όπως η βροχόπτωση, η θερμοκρασία, η εξατμισοδιαπνοή κ.λπ.Climate change predictions are generally based on the estimations by general circulation models (GCMs). The GCMs outputs are usually at resolution that is too coarse for many climate change impact studies. Hence, there is a great need to develop tools for downscaling GCM predictions of climate change to regional and local or station scales. This paper examines the potential of the Artificial Neural Network models (ANNs) in combination with Principal Component Analysis (PCA) to describe the relationship between large-scale atmospheric variables such as precipitation, temperature, humidity, pressure, geopotential height etc., and monthly precipitation for a station. It was concluded that ANN-based downscaling models are reliable and can be applied to atmospheric variables downscaling, like precipitation, temperature, evapotranspiration etc

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum