Predicting COD and BOD Parameters of Greywater Using Multivariate Linear Regression

Wide Scope ◽

Quality Outcomes ◽

Greywater Reuse ◽

Variable Information

Greywater reuse furthermore, reusing can be an incredible method to get non-consumable water. Since it contains broke down pollutions, greywater can’t be utilized straightforwardly. As an outcome, it is critical to decide the nature of water prior to utilizing it. Body estimations require five days to finish, while COD estimations require only a couple of hours. Not exclusively improve models for evaluating water quality are required; however, a more coordinated methodology is additionally getting more normal. Most of these models require a wide scope of information that isn’t in every case promptly available, making it a costly and tedious activity. Because of different issues in the enlistment with estimation included in water quality boundaries like BOD as well as COD, the principal objective of this investigation is to track down the best multivariate direct relapse models for foreseeing complex water quality outcomes. The code was written in Python for multi-variable information sources, and a Linear Regression Model was created. The projected COD versus estimated COD chart shows that the noticed and expected qualities are practically the same. The R-squared worth was 0.9973. A plot of extended BOD as an element of COD is likewise remembered for the outcome.

Short period PM2.5 prediction based on multivariate linear regression model

PLoS ONE ◽

10.1371/journal.pone.0201011 ◽

2018 ◽

Vol 13 (7) ◽

pp. e0201011 ◽

Cited By ~ 11

Author(s):

Rui Zhao ◽

Xinxin Gu ◽

Bing Xue ◽

Jianqiang Zhang ◽

Wanxia Ren

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Short Period

Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019

Open-File Report ◽

10.3133/ofr20211018 ◽

2021 ◽

Author(s):

Thomas J. Williams

Keyword(s):

Water Quality ◽

Linear Regression ◽

Regression Model ◽

Real Time ◽

Linear Regression Model ◽

Quality Data ◽

Water Quality Data ◽

Kansas River

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics ◽

Testing for elliptical symmetry of errors in the multivariate linear regression model

10.2991/icecee-15.2015.192 ◽

2015 ◽

Author(s):

Yan Su ◽

Shaoyue Kang

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Elliptical Symmetry

Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

10.21203/rs.2.22137/v2 ◽

2020 ◽

Author(s):

Haoua Tall ◽

Issaka Yaméogo ◽

Ryan Novak ◽

Lionel L Ouedraogo ◽

Ousmane Ouedraogo ◽

...

Keyword(s):

Relative Humidity ◽

Linear Regression ◽

Regression Model ◽

Burkina Faso ◽

Linear Model ◽

Linear Regression Model ◽

Climate Factors ◽

Multivariate Linear Model

Abstract Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors. Aims: The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district. Data and methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district. Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou. Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

The multivariate linear regression model

Statistical Foundations of Econometric Modelling ◽

10.1017/cbo9780511599293.026 ◽

1986 ◽

pp. 571-607

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Multivariate Linear Regression Model

Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

10.21203/rs.2.22137/v1 ◽

2020 ◽

Author(s):

Haoua Tall ◽

Issaka Yaméogo ◽

Ryan Novak ◽

Lionel L Ouedraogo ◽

Ousmane Ouedraogo ◽

...

Keyword(s):

Relative Humidity ◽

Linear Regression ◽

Regression Model ◽

Burkina Faso ◽

Linear Model ◽

Linear Regression Model ◽

Climate Factors ◽

Multivariate Linear Model

Abstract Background Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors.Aims The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district.Data and methods The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district.Results The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou.Conclusion The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.