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Tytuł:
Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS
Autorzy:
Nazir, Awais
Khan, Safdar Abbas
Khan, Malak Adnan
Alam, Zaheer
Khan, Imran
Irfan, Muhammad
Rehman, Saifur
Nowakowski, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/27311430.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
wind energy conversion systems
WECS
robust control
maximum power point tracking
MPPT
sliding mode control
SMC
super-twisting algorithm
STA
high gain observer
artificial neural network
ANN
function fitting
backstepping
śledzenie maksymalnego punktu mocy
obserwator o dużym wzmocnieniu
sztuczna sieć neuronowa
dopasowanie funkcji
system konwersji energii wiatrowej
sterowanie odporne
sterowanie ślizgowe
algorytm super skręcania
Opis:
The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 5; art. no. e147063
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Neural Network-based Prediction Technique for Waterproofness of Seams Obtained by Using Fusible Threads
Autorzy:
Karabay, Gulseren
Senol, Yavuz
Ozturk, Hasan
Mesegul, Cansu
Powiązania:
https://bibliotekanauki.pl/articles/2171995.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
waterproof
seam
sewing thread
fusible thread
artificial neural network
ANN
Opis:
The aim of this study was to estimate waterproofness values of seams composed of the combination of fusible threads and antiwick sewing threads through artificial neural networks (ANN). Fusible threads were used to obtain waterproof seams for the first time. Therefore, estimating the value of the waterproofness variable with the help of models created from test values can contribute to accelerating the progress of further studies. Hence, ten different samples were prepared for two fabrics, and the waterproofness values of the seams obtained were tested using a Textest FX 3000 Hydrostatic Head Tester III. For the prediction of waterproofness values of the seams, the Levenberg-Marquardt backpropagation algorithm was used for artificial neural network pattern models with sigmoid and positive linear transfer functions. Finally, the ANN model was successful in estimating the waterproofness of the seams. The highest correlation coefficient was R = 0.95081 which indicated that the prediction made by the neural network model proved to be reliable.
Źródło:
Fibres & Textiles in Eastern Europe; 2022, 3 (151); 27--32
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative analysis of Solar Generation System with 21- CHB-MLI integrated SAPF based ANN and AGPSO tuned PI controller to enhance power quality
Autorzy:
Agrawal, Seema
Kumar, Mahendra
Palwalia, D. K.
Powiązania:
https://bibliotekanauki.pl/articles/41176533.pdf
Data publikacji:
2022
Wydawca:
Politechnika Warszawska, Instytut Techniki Cieplnej
Tematy:
SAPF
shunt active power filter
THD
ANN
artificial neural network
AGPSO algorithm
PCC
bocznikowy filtr mocy czynnej
sztuczna sieć neuronowa
algorytmy
Opis:
This paper represents comparative analysis of artificial neural network (ANN) and AGPSO tuned PI controller based power quality improvement solar generation system. Now a day's Power quality is a major problem due to non-liner load based on power electronics. SAPF is solution to overcome such power quality issues in dynamic manner. With the use of both soft computing controllers based Shunt active power filter, it is tried to reduce harmonics (distortions), compensate reactive power, enhance power quality and power factor correction of supply voltage. System comprises 21-Level cascaded H-bridge inverter supplied from photovoltaic panel, series coupling inductor and self supported DC (capacitor) bus. Voltage harmonics of supplied voltage from PV is reduced by 21-level cascades H-bridge inverter in which switching signal is generated by carrier based in phase level shifted pulse width modulation technique. Incremental conductance (IC) MPPT technique is incorporated to maximize PV panel output. Phase locked loop based unit template generation and Levenberg Marquardt algorithm trained ANN and AGPSO tuned PI controller based DC bus voltage regulation is utilized for current quality improvement in SAPF. Comparative results show the effectiveness of ANN controller than A GPSO tuned PI controller. Suggested model is simulated in Matlab/Simulink 2016(b) for effectiveness.
Źródło:
Journal of Power Technologies; 2022, 102, 4; 121-131
1425-1353
Pojawia się w:
Journal of Power Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco
Autorzy:
El Azhari, Kaoutar
Abdallaoui, Badreddine
Dehbi, Ali
Abdalloui, Abdelaziz
Zineddine, Hamid
Powiązania:
https://bibliotekanauki.pl/articles/2174362.pdf
Data publikacji:
2022
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network
ANN
learning algorithm
multi-layer perceptron
MLP
modelling
Rabat-Kenitra
relative humidity
Opis:
This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.
Źródło:
Journal of Water and Land Development; 2022, 54; 13--20
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Investigating the FSW parameter’s role on microstructure and mechanical properties of welding AZ31B–AA8110 alloy
Autorzy:
Dharmalingam, S.
Lenin, K.
Srinivasan, D.
Powiązania:
https://bibliotekanauki.pl/articles/2173552.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
AA8011–AZ31B alloy
FSW
friction stir welding
ANN-GA
artificial neural network based genetic algorithm
mechanical properties
stop AA8011–AZ31B
właściwości mechaniczne
zgrzewanie tarciowe z mieszaniem materiału zgorzeliny
algorytm genetyczny
sztuczna sieć neuronowa
Opis:
The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011–AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e140098, 1--7
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Mechanical Properties of Woven Fabrics by ANN
Autorzy:
Elkateb, Sherien N.
Powiązania:
https://bibliotekanauki.pl/articles/2171977.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
ANN
artificial neural network
mechanical properties
prediction performance
modelling
woven fabric
Opis:
This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.
Źródło:
Fibres & Textiles in Eastern Europe; 2022, 4 (151); 54--59
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Artificial Neural Network to Predict the Tensile Properties of Dual-Phase Steels
Autorzy:
Shin, Seung-Hyeok
Kim, Sang-Gyu
Hwang, Byoungchul
Powiązania:
https://bibliotekanauki.pl/articles/2049252.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial neural network
ANN
dual-phase steels
alloying element
microstructural factor
tensile properties
Opis:
An artificial neural network (ANN) model was developed to predict the tensile properties of dual-phase steels in terms of alloying elements and microstructural factors. The developed ANN model was confirmed to be more reasonable than the multiple linear regression model to predict the tensile properties. In addition, the 3D contour maps and an average index of the relative importance calculated by the developed ANN model, demonstrated the importance of controlling microstructural factors to achieve the required tensile properties of the dual-phase steels. The ANN model is expected to be useful in understanding the complex relationship between alloying elements, microstructural factors, and tensile properties in dual-phase steels.
Źródło:
Archives of Metallurgy and Materials; 2021, 66, 3; 719-723
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive models for estimation of labyrinth weir aeration efficiency
Autorzy:
Aradhana, Aradhana
Singh, B.
Sihag, P.
Powiązania:
https://bibliotekanauki.pl/articles/1818800.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:
labyrinth weir
oxygen aeration efficiency
artificial neural network
ANN
fuzzy logic
ANFIS
efektywność napowietrzania
napowietrzanie
sztuczne sieci neuronowe
logika rozmyta
Opis:
Purpose: The purpose of the study is to estimate the aeration efficiency (E20) of Labyrinth weir using artificial intelligent (AI)-based models. Design/methodology/approach: The aeration efficiency (E20) was collected by using the nine models of Labyrinth weir with different shapes and dimensions. A total of 180 observations were used out of which 126 used to train the AI-based models and the remaining used to test the model. This observation consists of input variables such as Fraud number (Fr), Reynolds number (Re), numbers of keys (N), the ratio of head to the width of the channel (H/W), the ratio of crest length to width of the channel (L/W), the ratio of drop height to width of the channel (D/W) and shape factor (SF) and E20 as the output variables. The AI-based models used were Fuzzy Logic, multi-linear regression (MLR), adaptive neuro fuzzy interface system (ANFIS), and artificial neural network (ANN). Findings: The main findings of this investigation are that ANN is the best AI-based model that can estimate the E20 accurately than MLR, ANFIS, and Fuzzy Logic. Sensitivity analysis depicts that drop height at labyrinth weir is the essential factors for the estimation of E20; further, parametric studies have also been performed. Research limitations/implications: The proposed AI-based models can be used in the estimation of E20 with different shapes of labyrinth weir but still it needs improvement for the different dimensions. Practical implications: The best AI-based model can be used to calculate the E20 with the different values of input variables. Originality/value: There are no such AI-based models such as ANN, ANFIS, and Fuzzy Logic, available in the literature which can estimate the values of E20 accurately.
Źródło:
Journal of Achievements in Materials and Manufacturing Engineering; 2021, 105, 1; 18--32
1734-8412
Pojawia się w:
Journal of Achievements in Materials and Manufacturing Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Preface to special issue on Modern Intelligent Systems Concepts II
Autorzy:
Idrissi, Abdellah
Powiązania:
https://bibliotekanauki.pl/articles/2141893.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
modern intelligent systems
artificial neural network
ANN
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 35-36
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN)
Autorzy:
Obasi, Arinze A.
Ogbu, Kingsley N.
Orakwe, Chukwuemeka L.
Ahaneku, Isiguzo E.
Powiązania:
https://bibliotekanauki.pl/articles/292776.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network (ANN)
rainfall
flood forecasting
river discharge
Opis:
This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periods respectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this technique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.
Źródło:
Journal of Water and Land Development; 2020, 44; 98-105
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neuroevolutionary approach to COLREGs ship maneuvers
Autorzy:
Łącki, M.
Powiązania:
https://bibliotekanauki.pl/articles/116206.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
collision avoidance
colregs
neuroevolutionary approach to colregs
ship handling system
artificial helmsman
Artificial Neural Network (ANN)
evolutionary algorithms
ship manoeuvering
Opis:
The paper describes the usage of neuroevolutionary method in collision avoidance of two power-driven vessels approaching each other regarding COLREGs rules. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with artificial neural networks. The helmsman observes an environment by its input signals and according to assigned CORLEGs rule, he calculates the values of required parameters of maneuvers (propellers rpm and rudder deflection) in a collision avoidance situation. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task safely and efficiently. The main task of this project is to evolve a population of helmsmen which is able to effectively implement chosen rule: crossing or overtaking.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 4; 745-750
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training
Autorzy:
Al_Duais, M. S.
Mohamad, F. S.
Powiązania:
https://bibliotekanauki.pl/articles/307920.pdf
Data publikacji:
2017
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network (ANN)
batch back propagation algorithm
dynamic training rate
speed up training
accuracy training
Opis:
The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.
Źródło:
Journal of Telecommunications and Information Technology; 2017, 4; 82-89
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network
Autorzy:
Najafabadi, S. S.
Anaraki, A. T.
Moradi, M.
Powiązania:
https://bibliotekanauki.pl/articles/281868.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
deep drawing
finite element analysis (FEA)
multi-layer artificial neural network (ANN)
Taguchi design
Opis:
In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 707-718
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The usage of neural networks to forecast for churn of telecommunications clients
Wykorzystanie sztucznych sieci neuronowych do prognozowania zjawiska churn wśród klientów usług telekomunikacyjnych
Autorzy:
Wojda, Przemysław
Powiązania:
https://bibliotekanauki.pl/articles/389805.pdf
Data publikacji:
2017
Wydawca:
Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB
Tematy:
churn
artificial neural network
ANN
CLV
telecommunications
sztuczne sieci neuronowe
telekomunikacja
Opis:
This paper presents an attempt to use an artificial neural network to investigate the churn phenomenon among the customers of a telecommunications operator. An attempt was made to create a data model based on the customer lifetime value (CLV) rather than on activity alone. A multilayered artificial neural network was used for the experiments. The results yielded a 99% successful identification rate for customers in no danger of leaving, while only 57% of those identified as in danger of leaving actually did so and stopped using the company's services.
W pracy przedstawiono próbę wykorzystania sztucznej sieci neuronowej do badania zjawiska churn wśród klientów operatora telekomunikacyjnego. Podjęto próbę stworzenia modelu danych opartego o całkowitą wartość klienta (CLV), a nie tylko jego aktywność. Do przeprowadzenia eksperymentów wykorzystana została wielowarstwowa sztuczna sieć neuronowa. Uzyskano 99% skuteczność identyfikowania klientów nie zagrożonych odejściem, natomiast tylko 57% klientów wskazanych jako zagrożonych odejściem w rzeczywistości zaprzestało korzystania z usług firmy.
Źródło:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2017, 20; 5-14
1899-0088
Pojawia się w:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A wavelet-SARIMA-ANN hybrid model for precipitation forecasting
Hybrydowy model wavelet-SARIMA-ANN do prognozowania opadów
Autorzy:
Shafaei, M.
Adamowski, J.
Fakheri-Fard, M.
Dinpashoh, Y.
Adamowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/292320.pdf
Data publikacji:
2016
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network (ANN)
precipitation forecasting
seasonal auto regressive integrated moving average (SARIMA)
water resources management
wavelet
gospodarka zasobami wodnymi
metoda wavelet
prognozowanie opadów
sezonowa zintegrowana autoregresja z ruchomą średnią
sztuczne sieci neuronowe
Opis:
Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.
Prognozowanie opadów, ze względu na ich znaczenie w gospodarce zasobami wodnymi, szczególnie w zmniejszaniu ryzyka powodzi czy susz, było już przedmiotem wielu badań. Serie miesięcznych opadów mają właściwości nieliniowe i różne skale czasowe, w związku z czym przetestowano różne metody: wavelet, metodę zintegrowanej sezonowej autoregresji z ruchomą średnią (SARIMA) i hybrydową metodę sztucznych sieci neuronowych (ANN) pod kątem ich zdolności do dokładnego przewidywania miesięcznych opadów. Czterdziestoletnią (1970–2009) serię opadów z irańskiej stacji meteorologicznej w Nahavand (34°12’N, 48°22’E) rozłożono na jedną podserię o niskiej częstotliwości i kilka podserii o wysokiej częstotliwości występowania opadów przez transformację falkową. Podserie o niskiej częstotliwości prognozowano za pomocą modelu SARIMA, podczas gdy podserie o wysokiej częstotliwości prognozowano, stosując ANN. Na koniec prognozowane podserie zrekonstruowano celem przewidywania opadów w poszczególnych miesiącach w przyszłości. Porównanie wartości generowanych przez model z danymi z obserwacji wykazało lepszą dokładność prognozowania opadów za pomocą modelu wavelet-SARIMA-ANN niż za pomocą modeli wavelet-ANN i wavelet-SARIMA.
Źródło:
Journal of Water and Land Development; 2016, 28; 27-36
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 35

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