Probabilistic Study of Liquefaction Response of Fine-Grained Soil Using Multi-Linear Regression Model

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.

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Parametric modeling of the coupling channel of conducted interference based on multi-linear regression model

2016 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO) ◽

10.1109/nemo.2016.7561656 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shunxin Wang ◽

Fei Dai ◽

Tao Zheng

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Parametric Modeling ◽

Coupling Channel ◽

Multi Linear Regression

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A Multi-Linear Regression Model to Predict the Factors Affecting Water Consumption in Qatar

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/691/1/012004 ◽

2021 ◽

Vol 691 (1) ◽

pp. 012004

Author(s):

M Alshaikhli ◽

S Aqeel ◽

N Valdeolmillos ◽

F Fathima ◽

P Choe

Keyword(s):

Linear Regression ◽

Regression Model ◽

Water Consumption ◽

Linear Regression Model ◽

Factors Affecting ◽

Multi Linear Regression

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Multi-linear regression model for chlorine consumption by waters

Environmental Engineering Research ◽

10.4491/eer.2020.402 ◽

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-, NO2- 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 (R2) 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.

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