Temat: Multiple Regression - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Choosing Important Traits for the Model of High-Yielding Winter Wheat Variety Based on the Results of Regional Ecological Varietal Testing
Autorzy:: Lykhovyd, Pavlo
Powiązania:: https://bibliotekanauki.pl/articles/24201735.pdf
Data publikacji:: 2023
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: ideotype
modelling
multiple regression
productivity
varietal trait
Opis:: Current study is devoted to the development of an ideotype of winter wheat variety for cultivation in the conditions of the South of Ukraine. The investigation is based on the results of regional ecological varietal testing, conducted in the Southern Steppe zone on the non-irrigated lands. Varietal traits, included in the study, embraced growing season duration, 1000 grains weight, plant height, and ear length. The results of the testing were further processed using statistical procedures of linear Pearson’s correlation analysis and multiple regression analysis. As a result, the model of a winter wheat variety for the non-irrigated lands of the South of Ukraine was developed. The developed model is characterized by very high fitting quality (R2 = 0.9476) and good prediction accuracy (MAPE = 23.27%). According to the model, the variety should be late ripening with moderate to high plant height to provide the highest grain yield. The trait of 1000 grains weight was found out to be unimportant. The main trait, providing for the grain yield increase, is growing season duration, which must be long enough. Further ecological varietal testing studies with inclusion of additional varietal traits, such as cold-resistance, drought-resistance, frost-resistance, tolerance to diseases, etc., are to be conducted to extend the ideotype of winter wheat.
Źródło:: Journal of Ecological Engineering; 2023, 24, 6; 8--12
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Applying the Wastewater Quality Index for Assessing the Effluent Quality of Recently Upgraded Meet Abo El-koum Wastewater Treatment Plant
Autorzy:: Ayoub, Mohamed
El-Morsy, Ahmed
Powiązania:: https://bibliotekanauki.pl/articles/1838434.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: assessment
evaluation
multiple linear regression
quality
wastewater
WWTP
wastewater treatment plant
Opis:: The wastewater quality index (WWQI) can be defined as a single value, which reflects the overall wastewater quality related to its input constituent parameters. The major objective of the present study was to investigate the suitability of the effluent quality from Meet Abo El-koum wastewater treatment plant in Egypt for safe disposal based on the wastewater quality index approach. Moreover, statistical analysis was applied to develop a simple model using multiple linear regression (MLR) for accurate prediction of WWQI depending on different wastewater quality parameters. The results indicate good quality of the treated wastewater for safe disposal in general. Moreover, it is apparent that about 17% of the WWQI values reached excellent quality referring to the classification of the WWQI levels. For greater simplicity, a relationship between BOD5 and COD was deduced using linear regression, so that the results of the BOD5 analyses that appear after five days can be skipped. This approximation can be used to calculate WWQI on a specific day given the results of the treated wastewater analyses on that day.
Źródło:: Journal of Ecological Engineering; 2021, 22, 2; 128-133
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A Life Factor Approach to the Yield Prediction: a Comparison with a Technological Approach in Reliability and Accuracy
Autorzy:: Lykhovyd, Pavlo
Powiązania:: https://bibliotekanauki.pl/articles/124852.pdf
Data publikacji:: 2019
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: artificial neural network
life factor
multiple linear regression
technological factor
yield modelling
Opis:: There are a number of various approaches to the development of yield predictive models in agriculture. One of the most popular ones is based on the yield modeling from the parameters of crop cultivation technology. However, there is another view on the yield prediction models, which is based on the use of life factors as yielding parameters. Our study is devoted to the comparison of a conventional technological approach to the yield prediction with a less prevalent approach of life factor based yield modeling. The testing of two approaches was performed by using the yielding data of sweet corn cultivated in the field trials under the drip-irrigated conditions of the Southern Ukraine, under the different technological treatments, viz. plowing depth, nutrition, and crop density. We developed two multiple linear regression models to compare their efficiency in the yielding predictions. One of the models used cultivation technology parameters as the inputs while the other used life factors as the inputs. Life factors were expressed in numeric values by using the following converter: total water consumption of the crop was used as the factor of water, the total sum of positive temperatures was used as the factor of heat, and the total sum of the main nutrients (NPK) available in the soil was used as the factor of nutrition. The results of the study proved an equal accuracy and reliability of the studied models of sweet corn yields, which is obvious from the values of RSQ. RSQ of the both studied regression models was 0.897. However, additional check of the modeling approaches applied in the feed-forward artificial neural network showed that the life factor based model with the RSQ value of 0.953 provided better yield predictions than the technologically based model with the RSQ value of 0.913. Therefore, we concluded that the life factor approach should be preferred to the technological approach in the development of yield predictive models for agriculture.
Źródło:: Journal of Ecological Engineering; 2019, 20, 6; 177-183
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm
Autorzy:: Abdulkareem, Iman Ali
Abbas, Abdulhussain A.
Dawood, Ammar Salman
Powiązania:: https://bibliotekanauki.pl/articles/2068477.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: Shatt Al-Arab river
comprehensive pollution index
multiple linear regression
artificial neural network
genetic algorithm
Opis:: Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4-2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777.
Źródło:: Journal of Ecological Engineering; 2022, 23, 3; 236--250
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Predictive Modelling for Characterisation of Organics in Pit Latrine Sludge from Unplanned Settlements in Cities of Malawi
Autorzy:: Kalulu, K.
Thole, B.
Mkandawire, T.
Kululanga, G.
Powiązania:: https://bibliotekanauki.pl/articles/124540.pdf
Data publikacji:: 2018
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: Akaike Information Criterion
biochemical oxygen demand
chemical oxygen demand
faecal sludge characteristics
multiple linear regression model
Opis:: The limited availability of data on faecal sludge characteristics remains one of the major challenges faced by developing countries in proper management of faecal sludge. In view of the limited financial resources and expertise in these developing countries, there is a need to come up with less-resource-intensive approaches for faecal sludge characterisation. Despite being used substantially in wastewater, there is limited evidence on the use of predictive modelling as a tool for cost-effective characterisation of faecal sludge. In this study, first order multiple linear regression modelling is investigated as a less-resource-intensive approach for accurate prediction of organics (biochemical oxygen demand and chemical oxygen demand) in pit latrine sludge. The predictor variables explored in the modelling include pH, electrical conductivity, total solids, total volatile solids, fixed solids and moisture content. The modelling uses data collected from 80 latrines in unplanned settlements of four cities in Malawi. The study shows that it is possible to reliably predict chemical oxygen demand and biochemical oxygen demand in pit latrine sludge using electrical conductivity and total solids, which require low levels of resources and expertise to determine.
Źródło:: Journal of Ecological Engineering; 2018, 19, 3; 141-145
2299-8993
Pojawia się w:: Journal of Ecological Engineering
Dostawca treści:: Biblioteka Nauki

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