Temat: linear regression - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The Interaction of Chlorophyll-a and Total Suspended Matter along the Western Semarang Bay, Indonesia, Based on Measurement and Retrieval of Sentinel 3
Autorzy:: Maslukah, Lilik
Ismunarti, Dwi Haryo
Widada, Sugeng
Sandi, Nur Fikri
Prayitno, Hanif Budi
Powiązania:: https://bibliotekanauki.pl/articles/2202166.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:: Sentinel 3
chlorophyll-a
total suspended matter
linear regression
Opis:: The Kendal Regency area is one of the areas on the northern coast of Central Java that has been experiencing rapid industrial development. The high human activity in this area will impact the quality of water in these surrounding areas and affect the fertility of the waters. The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. The retrieval satellite of the 3 OLCI chosen in this study has a 300 m spatial resolution. This study aimed to see the distribution and effect of total suspended matter (TSM) on chlorophyll-a based on measurement and retrieval of Sentinel 3 imagery using the linear regression method. The results show the chlorophyll-a distribution and the value from retrieval satellite are higher and occur over larger surface area compared to chlorophyll-a measurements. The linear regression model of chlorophyll-a by retrieval satellite imagery and measurement is y = 0.65x + 4.65 with R2 = 0.54. The presence of high amounts of suspended solids in the waters causes disturbances in the reflectance values, which are recorded by the retrieval of satellite. The model regression chlorophyll-a with TSM accuracy from retrieval satellite results in the equation y = -0.0416x + 5.14 (R2 = 0.45, p = 0.05, n = 13). The determination (R2) coefficient value is 0.445, which means that suspended solids have a 44.5% effect on chlorophyll-a and 55.5% is influenced by other factors and not examined in this study. The results show that TSM has an influence on the accuracy of chlorophyll-a and retrieval satellite recording can be disrupted if waters have high turbidity.
Źródło:: Journal of Ecological Engineering; 2022, 23, 10; 191--201
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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