scholarly journals Assessment of pine biomass density through mid-infrared spectroscopy and multivariate modeling

BioResources ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. 807-822 ◽  
Author(s):  
Brian K. Via ◽  
Oladiran Fasin ◽  
Hui Pan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Infrared Spectra ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multivariate Modeling ◽  
Biomass Density ◽  
Mid Infrared ◽  
Derivative Spectra

The assessment of wood biomass density through multivariate modeling of mid-infrared spectra can be useful for interpreting the relationship between feedstock density and functional groups. This study looked at predicting feedstock density from mid-infrared spectra and interpreting the multivariate models. The wood samples possessed a random cell wall orientation, which would be typical of wood chips in a feedstock process. Principal component regression and multiple linear regression models were compared both before and after conversion of the raw spectra into the 1st derivative. A principal component regression model from 1st derivative spectra exhibited the best calibration statistics, while a multiple linear regression model from the 1st derivative spectra yielded nearly similar performance. Earlywood and latewood based spectra exhibited significant differences in carbohydrate-associated bands (1000 and 1060 cm-1). Only statistically significant principal component terms (alpha less than 0.05) were chosen for regression; likewise, band assignments only originated from statistically significant principal components. Cellulose, lignin, and hemicelllose associated bands were found to be important in the prediction of wood density.

scholarly journals PENERAPAN REGRESI AKAR LATEN DALAM MENANGANI MULTIKOLINEARITAS PADA MODEL REGRESI LINIER BERGANDA

2014 ◽  
Vol 3 (1) ◽  
pp. 8
Author(s):  
DWI LARAS RIYANTINI ◽  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Least Squares Method ◽  
Simulated Data ◽  
Principal Component ◽  
Latent Root ◽  
Estimation Of Parameters ◽  
Independent Variables ◽  
New Variables

Multicollinearity is a problem that often occurs in multiple linear regression. The existence of multicollinearity in the independent variables resulted in a regression model obtained is far from accurate. Latent root regression is an alternative in dealing with the presence of multicollinearity in multiple linear regression. In the latent root regression, multicollinearity was overcome by reducing the original variables into new variables through principal component analysis techniques. In this regression the estimation of parameters is modified least squares method. In this study, the data used are eleven groups of simulated data with varying number of independent variables. Based on the VIF value and the value of correlation, latent root regression is capable of handling multicollinearity completely. On the other hand, a regression model that was obtained by latent root regression has   value of 0.99, which indicates that the independent variables can explain the diversity of the response variables accurately.

scholarly journals Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2749 ◽  
Author(s):  
Xiang Cheng ◽  
Qingquan Li ◽  
Zhiwei Zhou ◽  
Zhixiang Luo ◽  
Ming Liu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Random Errors ◽  
Monitoring Model ◽  
Rockfill Dam ◽  
Core Rockfill Dam

The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.

scholarly journals Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O3) Concentrations Prediction

2020 ◽  
Vol 616 ◽  
pp. 012004
Author(s):  
Nur Nazmi Liyana Mohd Napi ◽  
Mohammad Syazwan Noor Mohamed ◽  
Samsuri Abdullah ◽  
Amalina Abu Mansor ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component Regression ◽  
Principal Component

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.

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