scholarly journals Multi-linear regression model for chlorine consumption by waters

2020 ◽  
Vol 26 (4) ◽  
pp. 200402-0
Author(s):  
Guocheng Zhu ◽  
Shanshan Zhang ◽  
Yongning Bian ◽  
Andrew S Hursthouse
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Drinking Water Treatment ◽  
Model Combination ◽  
Water Matrix ◽  
Matrix Properties ◽  
Multi Linear Regression ◽  
The Relationship

In drinking water treatment, disinfection is a key step to ensure the safety of water quality and people's health but little is known of the relationship between chlorine consumption and water matrix properties from varied sources (BWM). In this study, we measured the fluorescence from fractions of NOM (FFN) for the relevant BWM. This included the evaluation of three components: the chlorine-dependence factor (CDF) (DOC and NH3-N), the BWM (such as NO3<sup>-</sup>, NO2<sup>-</sup> and turbidity), and FFN (I-V fluorescence fractions). Multi-linear regression model was used to fit the data. Results showed that when using the CDF, BWM and FNN, in the prediction of chlorine consumption showed the (R<sup>2</sup>) values were 0.72, 0.71 and 0.41, respectively. While the FNN did not fit the model well it did enhance the model using CDF by 11.26%. The FNN is not effective in enhancement of the BWM response to the model. Combination of the CDF, BWM and FNN or that of the CDF and BWM were both effective in prediction of chlorine consumption.

scholarly journals A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Regression Model ◽  
Population Exposure ◽  
Aerosol Layer ◽  
Multi Linear Regression ◽  
Sun Photometer ◽  
The Impact

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

The Settlement Prediction of Pile Foundation Based on Grey Linear Regression Model

2012 ◽  
Vol 204-208 ◽  
pp. 320-325
Author(s):  
Jia Kun Liu ◽  
Jian Ping Wang ◽  
Min Zhu ◽  
Xiao Jie Hou
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Pile Foundation ◽  
Load Test ◽  
Static Load Test ◽  
Combined Model ◽  
Actual Meaning ◽  
Relationship Of ◽  
The Relationship

Grey linear regression model is a covert grey combined model that is built based on GM(1,1) model and linear regression model. It improves undervaluation of linear regression model which can not in press the exponential growth and come to deficiency of grey GM (1, 1) model which has not linear factor. This paper briefly introduces the establishment and precision examination method of the grey linearity regression model and establishes the grey linear regression model to predict the relationship of load and settlement. Based on the data of static load test, the load-settlement curve is simulated and analyzed. The result of study shows that Grey Linear regression Model can effectively predict the settlement of pile foundation, and be of the theoretical and actual meaning for further analyzing the bearing capability of pile foundation.

Sign in / Sign up

Export Citation Format

Share Document