Temat: multiple model - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Dynamic modelling of an anaerobic reactor treating coffee wet wastewater via multiple regression model
Autorzy:: Guardia-Puebla, Yans
Llanes-Cedeño, Edilberto
Domínguez-León, Ana Velia
Arias-Cedeño, Quirino
Sánchez-Girón, Victor
Morscheck, Gert
Eichler-Löbermann, Bettina
Powiązania:: https://bibliotekanauki.pl/articles/1841946.pdf
Data publikacji:: 2021
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: coffee wet wastewater
modelling
multiple regression model
upflow anaerobic sludge blanket
UASB
Opis:: A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion (AIC), Hannan–Quinn criterion (HQC), and Schwarz–Bayesian information criterion (SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.
Źródło:: Journal of Water and Land Development; 2021, 50; 229-239
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Determination of soil infiltration rate equation based on soil properties using multiple linear regression
Autorzy:: Harisuseno, Donny
Cahya, Evi N.
Powiązania:: https://bibliotekanauki.pl/articles/1844413.pdf
Data publikacji:: 2020
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: infiltration rate
model performance
multiple linear regression
soil property
Opis:: Infiltration process plays important role in water balance concept particularly in runoff analysis, groundwater recharged, and water conservation. Hence, increasing knowledge concerning infiltration process becomes essential for water manager to gain an effective solution to water resources problems. This study employed multiple linear regression for estimating infiltration rate where the soil properties used as the predictor variable and measured infiltration rate as the response variable. Field measurement was conducted at sixteen points to obtain infiltration rate using double ring infiltrometer and soil properties namely soil porosity, silt, clay, sand content, degree of saturation, and water content. The result showed that measured infiltration rate had an average initial infiltration rate (f0) of 6.92 mm∙min–1 and final infiltration rate (fc) of 1.49 mm∙min–1. Soil porosity and sand content showed a positive correlation with infiltration rate by 0.842, 0.639, respectively, while silt, clay, water content, and degree of saturation exhibited a negative correlation by –0.631, –0.743, –0.66 and –0.49, respectively. Three types of regression equations were established based on type of soil properties used as predictor variables. The model performance analysis was conducted for each equation and the result shows that the equation with five predictor variables fMLR_3 = – 62.014 + 1.142 soil porosity – 0.205 clay, – 0.063 sand – 0.301, silt + 0.07 soil water content with R2 (0.87) and Nash–Sutcliffe (0.998) gave the best result for estimating infiltration rate. The study found that soil porosity contributes mostly to the regression equation that indicates great influence in controlling soil infiltration behavior.
Źródło:: Journal of Water and Land Development; 2020, 47; 77-88
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply
Autorzy:: Vozhehova, Raisa
Kokovikhin, Sergii
Lykhovyd, Pavlo V.
Balashova, Halyna
Lavrynenko, Yuriy
Biliaieva, Iryna
Markovska, Olena
Powiązania:: https://bibliotekanauki.pl/articles/292829.pdf
Data publikacji:: 2020
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: linear model
multiple linear regression analysis
onion
potato
tomato
yield modelling
Opis:: Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R² of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R² of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.
Źródło:: Journal of Water and Land Development; 2020, 45; 190-197
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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