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    Wyświetlanie 1-12 z 12
    Tytuł:
    Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression
    Autorzy:
    Parviz, Laleh
    Powiązania:
    https://bibliotekanauki.pl/articles/763798.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    yield, climate, MLR, SVM
    Opis:
    Investigations of the relation between crop yield and climate variables are crucial for agricultural studies and decision making related to crop monitoring. Multiple linear regression (MLR) and support vector regression (SVR) are used to identify and model the impact of climate variables on barley yield. The climate variables of 36 years (1982–2017) are gathered from three provinces of Iran with different climate: Yazd (arid), Zanjan (semi-arid), Gilan (very humid). Air temperature by high correlation coefficient with barley yield was introduced as the dominant climate variable. According to evaluation criteria, SVR provided accurate estimation of crop yield in comparison with MLR. The diversity of climate impressed the estimated yield in which UI, decreasing from Gilan to Yazd provinces, was 47.77%. Support vector machine (SVM) with capturing the nonlinearity of time series, could improve barley yield estimation, with the minimum UI for Yazd province. Also, the minimum correlation coefficient between the observed and simulated yield was found in Gilan province. Based on GMER calculations, SVM forecasts were underestimated in three provinces. All findings show that SVM is able to have high efficiency to model the climate effect on crop yield.
    Źródło:
    Annales Universitatis Mariae Curie-Sklodowska, sectio C – Biologia; 2018, 73, 1
    2083-3563
    0066-2232
    Pojawia się w:
    Annales Universitatis Mariae Curie-Sklodowska, sectio C – Biologia
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Grain boundary effect on the anisotropy piezoresistance of laser-recrystallized polysilicon layers in SOI-structures
    Autorzy:
    Pankov, Y.
    Druzhinin, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/307640.pdf
    Data publikacji:
    2001
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    SOI
    polysilicon layers
    MLR
    Opis:
    A physical model of grain boundary influence on the piezoresistive effect of p-type conductivity of polysilicon layers in SOI-structures is developed. Software calculating piezoresistive properties of boron-doped p-type polysilicon layers has been developed. These properties may be calculated over wide concentration and temperature ranges with anisotropy taken into account and with the average grain size as a parameter. The potential barrier regions around the grain boundaries influence the deformation changes of anisotropy resistance in the fine-grained non-recrystallized SOI-structures doped with boron up to 3ź10(19)cm(-3) only.
    Źródło:
    Journal of Telecommunications and Information Technology; 2001, 1; 46-48
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of the Density of Energetic Materials on the Basis of their Molecular Structures
    Autorzy:
    Rahimi, R.
    Keshavarz, M. H.
    Akbarzadeh, A. R.
    Powiązania:
    https://bibliotekanauki.pl/articles/358109.pdf
    Data publikacji:
    2016
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Przemysłu Organicznego
    Tematy:
    crystal density
    energetic compound
    QSPR
    MLR
    ANN
    Opis:
    The density of an energetic compound is an essential parameter for the assessment of its performance. A simple method based on quantitative structure-property relationship (QSPR) has been developed to give an accurate prediction of the crystal density of more than 170 polynitroarenes, polynitroheteroarenes, nitroaliphatics, nitrate esters and nitramines as important classes of energetic compounds, by suitable molecular descriptors. The evaluation techniques included cross-validation, validation through an external test set, and Y-randomization for multiple linear regression (MLR) and training state analysis for artificial neural network (ANN), and were used to illustrate the accuracy of the proposed models. The predicted MLR results are close to the experimental data for both the training and the test molecular sets, and for all of the molecular sets, but not as close as the ANN results. The ANN model was also used with 20 hidden neurons that gave good result. The results showed high quality for nonlinear modelling according to the squared regression coefficients for all of the training, validation and the test sets (R2 = 0.999, 0.914 and 0.931, respectively). The calculated results have also been compared with those from several of the best available predictive methods, and were found to give more reliable estimates.
    Źródło:
    Central European Journal of Energetic Materials; 2016, 13, 1; 73-101
    1733-7178
    Pojawia się w:
    Central European Journal of Energetic Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of the Detonation Pressure of Co-crystal Explosives through a Novel, Simple and Reliable Model
    Autorzy:
    Zohari, Narges
    Montazeri, Mahnaz
    Hosseini, Seyed Ghorban
    Powiązania:
    https://bibliotekanauki.pl/articles/1062768.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Przemysłu Organicznego
    Tematy:
    energetic co-crystals
    detonation pressure
    QSPR approach
    MLR method
    Opis:
    The detonation properties of energetic co-crystals have a substantial role in the design of new co-crystals and it is necessary to know about them. In this study, a linear relationship is proposed between the detonation pressure of energetic co-crystals and their molecular structures via a quantitative structure property relationship (QSPR) method. This model assumes that the detonation pressure of an energetic co-crystal is a function of nN, Mw, nC/nH and nO/nH. The new model was obtained based on the calculated detonation pressures of 39 co-crystals as a training set. The R2 or determination coefficient of the acquired model was 0.9409. This novel correlation provided a proper assessment for a further 12 energetic co-crystals as a test set. Additionally, the root mean square and average absolute deviation of this newly presented correlation were found to be 2.249 and 1.716 GPa, respectively. As a consequence, the proposed correlation can also be utilized to design new energetic co-crystals.
    Źródło:
    Central European Journal of Energetic Materials; 2020, 17, 4; 492-505
    1733-7178
    Pojawia się w:
    Central European Journal of Energetic Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall-Runoff Relationship in Water Resources Management
    Autorzy:
    Turhan, Evren
    Powiązania:
    https://bibliotekanauki.pl/articles/1838400.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    rainfall-runoff model
    artificial neural networks
    MLR
    Nergizlik Dam
    Opis:
    Recently, Artificial Neural Network (ANN) methods, which have been successfully applied in many fields, have been considered for a large number of reliable streamflow estimation and modeling studies for the design and project planning of hydraulic structures. The present study aimed to model the rainfall-runoff relationship using different ANN methods. The Nergizlik Dam, located in the Seyhan sub-basin and one of the important basins in Turkey, was chosen as the study area. Analyses were carried out based on streamflow estimation with the help of observed precipitation and runoff data at certain time intervals. Feed Forward Backpropagation Neural Network (FFBPNN) and Generalized Regression Neural Network (GRNN) methods were adopted, and obtained results were compared with Multiple Linear Regression (MLR) method, which is accepted as the traditional method. Also, the models were performed using three different transfer functions to create optimum ANN modeling. As a result of the study, it was seen that ANN methods showed statistically good results in rainfall-runoff modeling, and the developed models can be successfully applied in the estimation of average monthly flows.
    Źródło:
    Journal of Ecological Engineering; 2021, 22, 5; 166-178
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation of grade and recovery in the concentration of barite tailings by the flotation using the MLR and ANN analyses
    Autorzy:
    Deniz, Vedat
    Umucu, Yakup
    Deniz, Orcun T.
    Powiązania:
    https://bibliotekanauki.pl/articles/2146934.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    barite
    tailing
    flotation
    recovery
    grade
    MLR model
    ANN model
    Opis:
    This study aimed to find optimal models in a comparative framework to estimate the recovery and grade of barite concentrate obtained from the rougher flotation of the barite tailings. Therefore, firstly, the effect of four operating parameters (flotation time, pH, collector dosage, and depressant dosage) on the rougher flotation of the barite tailings containing 37.23% BaSO4 was experimentally investigated. Secondly, two models called the multivariable linear regression (MLR) and the artificial neural network (ANN) were used for the estimation of the recovery and grade of the barite concentrate for the rougher flotation optimization. The R2 values found from the MLR and ANN models were 0.828 and 0.995 for the concentrate recovery, and 0.977 and 0.960 for the barite concentrate grade, respectively. In the comparison of the models determined, it was found that the ANN model expressed quite well than the MLR models, especially for the recovery of the rougher concentrate.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2022, 58, 5; art. no. 150646
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Streamflow prediction using data-driven models: Case study of Wadi Hounet, northwestern Algeria
    Autorzy:
    Beddal, Dalila
    Achite, Mohammed
    Baahmed, Djelloul
    Powiązania:
    https://bibliotekanauki.pl/articles/1844406.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    Algeria
    back propagation neural network
    BPNN
    multi linear regression
    MLR
    streamflow
    Wadi Hounet
    Opis:
    Streamflow modelling is a very important process in the management and planning of water resources. However, complex processes associated with the hydro-meteorological variables, such as non-stationarity, non-linearity, and randomness, make the streamflow prediction chaotic. The study developed multi linear regression (MLR) and back propagation neural network (BPNN) models to predict the streamflow of Wadi Hounet sub-basin in north-western Algeria using monthly hydrometric data recorded between July 1983 and May 2016. The climatological inputs data are rainfall (P) and reference evapotranspiration (ETo) on a monthly scale. The outcomes for both BPNN and MLR models were evaluated using three statistical measurements: Nash–Sutcliffe efficiency coefficient (NSE), the coefficient of correlation (R) and root mean square error (RMSE). Predictive results revealed that the BPNN model exhibited good performance and accuracy in the prediction of streamflow over the MLR model during both training and validation phases. The outcomes demonstrated that BPNN-4 is the best performing model with the values of 0.885, 0.941 and 0.05 for NSE, R and RMSE, respectively. The highest NSE and R values and the lowest RMSE for both training and validation are an indication of the best network. Therefore, the BPNN model provides better prediction of the Hounet streamflow due to its capability to deal with complex nonlinearity procedures.
    Źródło:
    Journal of Water and Land Development; 2020, 47; 16-24
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of the Density of Energetic Co-crystals: a Way to Design High Performance Energetic Materials
    Autorzy:
    Zohari, Narges
    Mohammadkhani, Faezeh Ghiasvand
    Powiązania:
    https://bibliotekanauki.pl/articles/357956.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Przemysłu Organicznego
    Tematy:
    energetic co-crystals
    density
    MLR method
    artificial neural network
    molecular design
    Opis:
    For designing a new energetic material with good performance, a knowledge of its density is required. In this study, the relationship between the densities of energetic co-crystals and their molecular structures was examined through a quantitative structure-property relationship (QSPR) method. The methodology of this research provides a new model which can relate the density of an energetic co-crystal to several molecular structural descriptors, which are calculated by Dragon software. It is indicated that the density of a co-crystal is a function of sp, OB, DU, nAT, as well as several non-additive structural parameters. The new recommended correlation was derived on the basis of the experimental densities of 50 co-crystals with various structures as a training set. The R2 or determination coefficient of the derived correlation was 0.937. This correlation provided a suitable estimate for a further 12 energetic co-crystals as a test set. Meanwhile, the predictive ability of the correlation was investigated through a cross validation method. Moreover, the new model has more reliability and performance for predicting the densities of energetic co-crystals compared to a previous one which was based on an artificial neural network approach. As a matter of fact, designing novel energetic co-crystals is possible by utilising the proposed method.
    Źródło:
    Central European Journal of Energetic Materials; 2020, 17, 1; 31-48
    1733-7178
    Pojawia się w:
    Central European Journal of Energetic Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Structure Activity Relationships, QSAR Modeling and Drug-like calculations of TP inhibition of 1,3,4-oxadiazoline-2-thione Derivatives
    Autorzy:
    Almi, Z.
    Belaidi, S.
    Lanez, T.
    Tchouar, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/971332.pdf
    Data publikacji:
    2014
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    SAR
    QSAR
    drug-like
    TP inhibitory
    MLR
    1,3,4-oxadiazoline-2-thione derivatives
    Opis:
    QSAR studies have been performed on twenty-one molecules of 1,3,4-oxadiazoline-2-thiones. The compounds used are among the most thymidine phosphorylase (TP) inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and TP inhibition of the 1,3,4-oxadiazoline-2-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, Pol, MR, MV, and MW, qO1, SAG, for the TP inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.
    Źródło:
    International Letters of Chemistry, Physics and Astronomy; 2014, 18; 113-124
    2299-3843
    Pojawia się w:
    International Letters of Chemistry, Physics and Astronomy
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of multiple linear regression for multi-criteria yield prediction of winter wheat
    Zastosowanie analizy regresji wielorakiej dla wielokryterialnej prognozy plonów pszenicy ozimej
    Autorzy:
    Niedbała, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/335462.pdf
    Data publikacji:
    2018
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
    Tematy:
    forecast
    multiple regression
    MLR
    winter wheat
    yield prediction
    prognoza
    regresja wielokrotna
    pszenica ozima
    predykcja plonu
    Opis:
    The aim of the work was to produce three independent models for prediction and simulation of winter wheat yield, which were marked in the following way: ReWW15_04, ReWW31_05 and ReWW30_06. The produced models enable to make yield forecasts for April 15, May 31 and June 30, directly before harvest in the current agrotechnical season. For the construction of prediction models the Multiple Linear Regression (MLR) method was used. The models are based on meteorological data (air temperature and rainfall) and information on mineral fertilisation. The data were collected from 2008- 2015 from 301 production fields located in Poland, in the Wielkopolskie Voivodeship. Evaluation of the quality of forecasts based on MLR models was verified by determining forecast errors using RAE, RMS, MAE and MAPE error gauges. An important feature of the produced prediction model consists in the possibility of making a prediction in the current agrotechnical year on the basis of current weather and fertilizer information.
    Celem pracy było wytworzenie trzech niezależnych modeli do predykcji i symulacji plonu pszenicy ozimej, które oznaczono w następujący sposób: ReWW15_04, ReWW31_05 and ReWW30_06. Wytworzone modele umożliwiają wykonanie prognozy plonu na dzień 15 kwietnia, 31 maja i 30 czerwca, bezpośrednio przed zbiorem w aktualnie trwającym sezonie agrotechnicznym. Do budowy modeli predykcyjnych użyto metody liniowej regresji wielorakiej (MLR). Modele powstały w oparciu o dane meteorologiczne (temperatura powietrza i opady atmosferyczne) oraz informacje o nawożeniu mineralnym. Dane zostały zebrane z lat 2008-2015 z 301 pól produkcyjnych zlokalizowanych w Polsce, na terenie województwa Wielkopolskiego. Ocena jakości prognoz wytworzonych na bazie modeli MLR została zweryfikowana poprzez określenie błędów prognozy za pomocą mierników błędów RAE, RMS, MAE oraz MAPE. Ważną cechą wytworzonego modelu predykcyjnego jest możliwość wykonania prognozy w bieżącym roku agrotechnicznym w oparciu o aktualne informacje pogodowe i nawozowe.
    Źródło:
    Journal of Research and Applications in Agricultural Engineering; 2018, 63, 4; 125-131
    1642-686X
    2719-423X
    Pojawia się w:
    Journal of Research and Applications in Agricultural Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiple regression analysis model to predict and simulate winter rapeseed yield
    Model analizy regresji wielorakiej dla prognozy i symulacji plonu rzepaku ozimego
    Autorzy:
    Niedbała, G.
    Piekutowska, M.
    Adamski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/336860.pdf
    Data publikacji:
    2018
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
    Tematy:
    Forecast
    multiple regression
    MLR
    winter rapeseed
    yield prediction
    prognoza
    regresja wielokrotna
    rzepak ozimy
    prognoza plonu
    Opis:
    The aim of the work is to create a model for prediction and simulation of winter rapeseed yield. The model made it possible to perform a yield forecast on 30 June, directly before harvest in the current agrotechnical season. The prediction model was built using the multiple regression method (MLR). The model was based on meteorological data (air temperature and precipitation) and information about mineral fertilization. The data were collected from the years 2008-2017 from 291 production fields located in Poland, in the southern Opole region. The assessment of the quality of forecasts generated on the basis of the regression model was verified by determining prediction errors using RAE, RMS, MAE and MAPE error meters. An important feature of the created prediction model concerns the possibility of making the forecast in the current agrotechnical year on the basis of the current weather and fertilizer information.
    Celem pracy było zbudowanie modelu do predykcji i symulacji plonu rzepaku ozimego. Model ten umożliwił wykonanie prognozy plonu na dzień 30 czerwca, bezpośrednio przed zbiorem w aktualnie trwającym sezonie agrotechnicznym. Do budowy modelu predykcyjnego użyto metody regresji wielorakiej (MLR). Model powstał w oparciu o dane meteorologiczne (temperatura powietrza i opady atmosferyczne) oraz informacje o nawożeniu mineralnym. Dane zostały zebrane z lat 2008- 2017 z 291 pól produkcyjnych zlokalizowanych w Polsce, na obszarze południowej Opolszczyzny. Ocena jakości prognoz wytworzonych na bazie modelu regresyjnego została zweryfikowana poprzez określenie błędów prognozy za pomocą mierników błędów RAE, RMS, MAE oraz MAPE. Ważną cechą wytworzonego modelu predykcyjnego jest możliwość wykonania prognozy w bieżącym roku agrotechnicznym w oparciu o aktualne informacje pogodowe i nawozowe.
    Źródło:
    Journal of Research and Applications in Agricultural Engineering; 2018, 63, 4; 139-144
    1642-686X
    2719-423X
    Pojawia się w:
    Journal of Research and Applications in Agricultural Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Procedure of Building and Analysis of the Information Database of the Resistance of Existing Bridge Structures to Mining Tremors
    Procedura budowy i analiza bazy informacji o odporności istniejących obiektów mostowych na wstrząsy górnicze
    Autorzy:
    Rusek, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/385484.pdf
    Data publikacji:
    2017
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    MES
    dynamika budowli
    MLR
    wstrząsy górnicze
    odporność dynamiczna
    mosty
    FEM
    dynamics of structures
    mining tremors
    dynamic resistance
    bridges
    Opis:
    Jednym z przejawów negatywnego wpływu działalności przemysłowej kopalń na środowisko są wstrząsy górnicze. Ochrona obiektów budowlanych przed szkodliwym działaniem wynikających stąd drgań podłoża gruntowego wymaga ustalenia ich odporności dynamicznej. Problem ten nabiera szczególnego znaczenia w przypadku istniejących obiektów mostowych, przy których projektowaniu nie uwzględniono możliwości wystąpienia wstrząsów górniczych. W pracy przedstawiono metodę pozyskiwana danych o odporności dynamicznej istniejących obiektów mostowych usytuowanych na terenach górniczy w wyniku obliczeń numerycznych metodą elementów skończonych (MES). Odporność obiektów opisano za pomocą granicznych wartości przyspieszeń drgań gruntu w płaszczyźnie pionowej i poziomej, które istniejąca konstrukcja może przejąć bez zagrożenia bezpieczeństwa. Uwzględniając zróżnicowanie geometryczne i materiałowe, utworzono 3000 modeli numerycznych żelbetowych mostów drogowych. Następnie w odniesieniu do każdego obiektu przeprowadzono obliczenia numeryczne MES, w wyniku których dla każdego przypadku wyznaczono dopuszczalne wartości przyspieszeń drgań gruntu określających ich odporność dynamiczną. Utworzoną bazę danych poddano wstępnej analizie w celu wykrycia linowych relacji wiążących dane opisujące geometrię i właściwości materiałowe poszczególnych obiektów z ich odpornością dynamiczną na wpływ wstrząsów górniczych. W efekcie tych badań wyselekcjonowano zmienne, na podstawie których utworzono model wielorakiej regresji liniowej (MLR). Analiza uzyskanych wyników pozwoliła ocenić możliwości stosowania modeli liniowych do ustalania odporności dynamicznej obiektów mostowych poddanych wstrząsom górniczym.
    Mining tremors are one of the manifestations of negative impacts of the mining industry on the environment. In order to protect building structures against the damaging effects of ground vibrations, it is required that their dynamic resistance be determined. This problem is of particular importance for the existing bridge structures that were not designed for the potential occurrence of mining tremors. This paper presents the assumptions of and a method for acquiring data on the dynamic resistance of existing bridge structures located in mining areas as a result of numerical calculations using the Finite Element Method (FEM). Object resistance was described by the limit values of the acceleration of ground vibrations in the vertical and horizontal planes that can be carried by the existing structure without compromising safety. Taking into account the geometrical and material diversity, 3,000 numerical models of reinforced concrete overpasses were created. Then, for each object, numerical calculations using the FEM were performed, which resulted in the determination of permissible values of the acceleration of ground vibrations defining their dynamic resistance. The created database was subjected to a preliminary analysis in order to detect linear relationships binding the data that describe the geometry and material properties of individual structures with their dynamic resistance to the impact of mining tremors. As a result of these studies, variables were selected that formed the basis for creating a multiple linear regression model (MLR). Analysis of the obtained results allowed us to assess the possibilities of using linear models to determine the dynamic resistance of bridge structures subjected to mining tremors.
    Źródło:
    Geomatics and Environmental Engineering; 2017, 11, 4; 111-123
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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