scholarly journals Assessing the damming effects on runoff using a multiple linear regression model: A case study of the Manwan Dam on the Lancang River

2012 ◽  
Vol 13 ◽  
pp. 1771-1780 ◽  
Author(s):  
Q.H. Zhao ◽  
S.L. Liu ◽  
L. Deng ◽  
S.K. Dong ◽  
C. Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Lancang River

QUANTIFYING ENERGY SAVINGS FOR RETROFIT CENTRALIZED HVAC SYSTEMS AT SELANGOR STATE SECRETARY COMPLEX

2015 ◽  
Vol 77 (5) ◽  
Author(s):  
Suhaidi Mohd Aris ◽  
Nofri Yenita Dahlan ◽  
Mohd Nasrun Mohd Nawi ◽  
Tengku Ahmad Nizam ◽  
Mohamad Zamhari Tahir
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Energy Saving ◽  
Linear Regression Model ◽  
Energy Use ◽  
Energy Savings ◽  
Multiple Linear Regression Model ◽  
Baseline Period

Objective of this study is to estimate building energy saving at Bangunan Sultan Salahuddin Abdul Aziz Shah from a retrofit of Water Cooling Package Unit (WCPU) system. This research calculates energy savings as recommended by International Performance Measurement and Verification Protocol (IPMVP) using Option C-Whole Facility Measurement. In this study, the baseline period is defined from July 2012 to June 2013, the retrofit of WCPU was performed on July 2013 and the reporting period is from August 2013 to July 2014. The baseline energy use and the post retrofit energy use data are collected from utility bills. On the other hand, the energy governing factors other than the retrofit such as outdoor temperature or Cooling Degree Day (CDD), number of working days (NWD) and occupancy on the building are gathered corresponding to the pre-defined baseline and post-retrofit period. These non-retrofit energy governing factors are used to model adjusted baseline energy in calculating energy savings using regression analysis. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression. Results show that number of occupancy has the highest coefficient regression, R2 followed by NWD and CDD. This indicates that occupancy has stronger correlation with the energy use in the building than NWD and CDD. Finding also shows that the R² for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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