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Wyświetlanie 1-4 z 4
Tytuł:
Zastosowanie wybranych modeli nieliniowych do prognozy ilości osadu nadmiernego
Application of Selected Nonlinear Methods to Forecast the Amount of Excess Sludge
Autorzy:
Gawdzik, J.
Szeląg, B.
Bezak-Mazur, E.
Stoińska, R.
Powiązania:
https://bibliotekanauki.pl/articles/1818016.pdf
Data publikacji:
2016
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
osady nadmierne
oczyszczanie ścieków
metoda wektorów nośnych
k–najbliższego sąsiada
drzewa wzmacniane
excess sludge
wastewater treatment
support vector machine (SVM)
k–nearest neighbour
boosted trees
Opis:
Operation of a sewage treatment plant is a complex task because it requires maintaining the parameters of its activities at the appropriate level in order to achieve the desired effect of reducing pollution and reduce the flow of sediment discharged from the biological reactor. The basis for predicting the amount of excess sludge and operational parameters WWTP can provide physical models describing the biochemical changes occurring in the reactor, in which the input parameters, ie. Indicators of effluent quality and quantity of wastewater are modeled in advance. However, due to numerous interactions and uncertainty of the data in the physical models and forecast errors parameters of the inlet to the treatment plant Simulation results may be affected by significant errors. Therefore, to minimize the prediction error parameters of operation of the technological objects deliberate use of a black box model. In these models at the stage of learning is generated model structure underlying the projections analyzed the operating parameters of the plant. This publication presents the possibility of the use of methods: support vector, k – nearest neighbour and trees reinforced to predict the amount of the resulting excess sludge during wastewater treatment in the WWTP located in Sitkówka – News with a capacity of 72,000 3/d with a load of 275,000 PE . Due to the fact that did not have the quality parameters of wastewater at the inlet to the activated sludge chambers it was not possible to verify the empirical relationships commonly used in engineering practice to determine the size of the daily flow of excess sludge. Due to the significant differences in the amount of excess sludge generated in the period (t = 1-7 days) the simulation of the amount of sludge into the time were performed. To assessment the compatibility of measurement results and simulations quantities of sludge the mean absolute error and relative error of prediction for the considered parameter of technology was used. The analyzes carried out revealed that the amount of generated excess sludge can be predicted on the basis of parameters describing the quantity and quality of influent waste water (slurry concentration of total nitrogen and total phosphorus, BOD5) and the operating parameters of the biological reactor (recirculation rate, concentration and temperature of the sludge, the dosed amount of methanol and PIX). On the basis of computations, it can be concluded that the most accurate forecasting results amounts of sediment were obtained by using a reinforced trees (t = 2 to 5 days) and Support Vector Machines methods (t = 1, 6, 7 days). While the highest values of forecast errors sediments was obtained using a k – nearest neighbor (t = 2 to 5 days) and reinforced trees (t = 1, 6, 7 days).
Źródło:
Rocznik Ochrona Środowiska; 2016, Tom 18, cz. 2; 695-708
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie funduszy unijnych w powiatach województwa śląskiego
The use of EU funds in the districts of the Silesian province
Autorzy:
Wójcik, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/593398.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Ekonomiczny w Katowicach
Tematy:
Diagram Czekanowskiego
Fundusze unijne
Metoda k-średnich
Metoda najbliższego sąsiada
Powiaty
Counties
Czekanowski’s diagram
EU funds
K-means method
Nearest neighbor method
Opis:
W artykule przedstawiono wykorzystanie funduszy unijnych w powiatach województwa śląskiego w latach 2004-2006 oraz w latach 2007-2013. Ponieważ powiaty w województwie śląskim są bardzo zróżnicowane pod względem zurbanizowania oraz ukształtowania terenu, to ich potrzeby są różne, a więc cele inwestycji też są różne. Postawiono hipotezę, że w powiatach o podobnym położeniu geograficznym i podobnej specyfice struktura projektów współfinansowanych z funduszy unijnych powinna być podobna. Do weryfikacji postawionej hipotezy wykorzystano diagram Czekanowskiego, metodę najbliższego sąsiada oraz metodę k-średnich. Otrzymane wyniki częściowo potwierdziły postawioną hipotezę.
This paper presents the use of EU funds in the districts of the Silesian province in the years 2004-2006 and 2007-2013. Since the counties in the Silesian province are very diverse in terms of urbanization and terrain that their needs are different, and therefore investment purposes are also different. It was hypothesized that in counties with a similar geographical location and similar specificity structure projects co-financed from EU funds should be similar. To verify the hypothesis used Czekanowski diagram, nearest neighbor method and k-means method. The results confirmed the hypothesis part.
Źródło:
Studia Ekonomiczne; 2017, 318; 108-124
2083-8611
Pojawia się w:
Studia Ekonomiczne
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways
Autorzy:
Lee, Y.
Wei, C.-H.
Chao, K.-C.
Powiązania:
https://bibliotekanauki.pl/articles/223569.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
accident on freeway
accident duration
effect evaluating
correlation
artificial neural networks
k-nearest neighbour method
wypadek na autostradzie
czas trwania wypadku
ocena skutków
korelacja
sztuczne sieci neuronowe
metoda najbliższego sąsiada
Opis:
Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.
Źródło:
Archives of Transport; 2017, 43, 3; 91-104
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity metrics
Autorzy:
Wang, Yang
Xiao, Yu
Lai, Jianhui
Chen, Yanyan
Powiązania:
https://bibliotekanauki.pl/articles/949848.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
missing traffic data
similarity metrics
K-nearest neighbour method
stochastic characteristics
metoda porównywania danych
metryki podobieństwa
metoda najbliższego sąsiada
cechy stochastyczne
Opis:
Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.
Źródło:
Archives of Transport; 2020, 54, 2; 59-73
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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