The Hourly Simulation of PM2.5 Particle Concentrations Using the Multiple Linear Regression (MLR) Model for Sea Breeze in Split, Croatia

2021 ◽  
Vol 232 (7) ◽  
Author(s):  
Tanja Trošić Lesar ◽  
Anita Filipčić
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sea Breeze ◽  
Mlr Model

NLEAP Computer Model and Multiple Linear Regression Prediction of Nitrate Leaching in Vegetable Systems

2002 ◽  
Vol 12 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Hudson Minshew ◽  
John Selker ◽  
Delbert Hemphill ◽  
Richard P. Dick
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nitrate Leaching ◽  
Cover Crops ◽  
N Uptake ◽  
N Leaching ◽  
Residual Soil ◽  
Vegetable Crops ◽  
Crop N Uptake ◽  
Mlr Model

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.

scholarly journals Aplikasi Analisis Multivariat Berdasarkan Warna untuk Memprediksi Brix dan pH pada Pisang

2017 ◽  
Vol 37 (1) ◽  
pp. 109 ◽  
Author(s):  
Yohanita Maulina Akbar ◽  
Dr. Rudiati Evi Masithoh ◽  
Nafis Khuriyati
Keyword(s):  
Multivariate Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Conventional Method ◽  
Calibration Model ◽  
Validation Data ◽  
Determination Coefficient ◽  
Mlr Model

In this research, Multiple Linear Regression (MLR) model was used to predict Brix and pH of banana based on RGB and Lab color values. Banana samples varied in color and ripening level from less ripen to ripen. RGB and Lab values were measured non-destructively using colormeter, while Brix and pH were determined using conventional method in laboratory. Multivariate analysis was done using the Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, and trial version). Results showed that calibration model using MLR was able to predict Brix and pH of banana based on RGB and Lab color values. Furthermore, validation data were used to test the selected models. MLR model to predict Brix based on RGB and Lab validation resulted in 0.8 and 0.84 of determination coefficient between observation and prediction data. The model was also able to predict pH based on RGB and Lab values with 0.71 and 0.79 of determination coefficient between observation and prediction data. ABSTRAKPada penelitian ini, model Multiple Linear Regression (MLR) digunakan untuk memprediksi Brix dan pH pada buah pisang berdasarkan nilai warna Red Green Blue (RGB) dan Lab. Pisang yang dianalisis mempunyai variasi warna dari kurang masak sampai masak. Parameter warna RGB dan Lab dilakukan secara non-destruktif dengan menggunakan colormeter, sedangkan pengukuran kualitas internal yaitu Brix dan pH ditentukan secara destruktif atau dengan prosedur konvensional di laboratorium. Aplikasi analisis multivariat yang digunakan adalah Unscrambler ® X 10.3 (CAMO, AS, OLSO, Norway, versi trial). Analisis data menunjukkan bahwa model kalibrasi MLR dapat digunakan untuk memprediksi Brix dan pH berdasarkan parameter warna RGB dan Lab pada buah pisang. Selanjutnya, data validasi digunakan untuk menguji model MLR terpilih. Model kalibrasi MLR dapat memprediksi Brix berdasarkan nilai RGB dan Lab dengan nilai koefisien determinasi (R2) sebesar 0,8 dan 0,84, secara berurutan. Sedangkan koefisien determinasi (R2) untuk pH berdasarkan warna RGB dan Lab adalah 0,71 dan 0,79.

scholarly journals Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Rachodbun Srimanus ◽  
Wechapraan Srimanus ◽  
Sarima Niampradit ◽  
Nopadol Preecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Local Level ◽  
Winter Season ◽  
Summer Season ◽  
Weather Data ◽  
Good Prediction ◽  
Mlr Model

Abstract Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Seasonal ground level ozone prediction using multiple linear regression (MLR) model

2020 ◽  
Vol 6 (4) ◽  
pp. 1981-1989
Author(s):  
Sarat Kumar Allu ◽  
Shailaja Srinivasan ◽  
Rama Krishna Maddala ◽  
Aparna Reddy ◽  
Gangagni Rao Anupoju
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ground Level ◽  
Ground Level Ozone ◽  
Ozone Prediction ◽  
Mlr Model

scholarly journals Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Bala Balarabe ◽  
◽  
Andy Anderson Bery ◽  
Keyword(s):  
Shear Strength ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Friction Angle ◽  
Accurate Estimation ◽  
Near Surface ◽  
Independent Variables ◽  
Refraction Tomography ◽  
Soil Shear Strength ◽  
Mlr Model

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log10 resistivity and Log10 velocity), calculated for p-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73° which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test).

Estimating and Modeling Uncertainties Affecting Production Throughput Using ARIMA-Multiple Linear Regression

2012 ◽  
Vol 488-489 ◽  
pp. 1263-1267
Author(s):  
Amir Azizi ◽  
Amir Yazid B. Ali ◽  
Loh Wei Ping ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Moving Average ◽  
Random Variables ◽  
Modeling Uncertainties ◽  
Break Time ◽  
Demand Changes ◽  
Changes Over Time ◽  
Mlr Model

Throughput of each production stage cannot meet the demand in the real production system because of the disruptions and interruptions of the production line for example break time and scrap. On the other hand, demand changes over time due to volume variation and product redesign as the customers’ needs are changing. This situation leads to planning and controlling under uncertain condition. This paper proposes a hybrid model of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) for estimating and modeling the random variables of production line in order to forecast the throughput in presence of production variations and demand fluctuation. The random variables under consideration of this study are demand, break-time, scrap, and lead-time. The random variables are formulated in the MLR model, where the mean absolute percentage of error (MAPE) was 2.53%. Further, nine ARIMA models with different parameters in MLR model are fitted to the data and compared by their MAPE. The best model with the lowest MAPE was when the ARIMA parameters set for p=1, d=0, and q=3. Finally the proposed model using ARIMA-MLR is formulated by MAPE of 1.55%.

Synergistic Effects of Four Heavy Metals on Photobacterium

2013 ◽  
Vol 807-809 ◽  
pp. 672-675
Author(s):  
Hui Lin Ge ◽  
Shu Shen Liu ◽  
De Fang Xie ◽  
Dong Mei Zou ◽  
Fang Fang Zhao
Keyword(s):  
Heavy Metals ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Synergistic Effects ◽  
Concentration Addition ◽  
Independent Action ◽  
Number Of Components ◽  
Mlr Model

The synergistic effects of mixtures of CdCl2, Ni (NO3)2, CuSO4, and ZnSO4 on photobacterium Q67 were predicted and evaluated by using models of concentration addition (CA), independent action (IA), effect summation (ES), ES with the exponent e (ESE), ES with the power of the number of components n (ESN), and integrated CA with IA based on multiple linear regression (MLR) model (ICIM). The effects of all mixtures were underestimated by CA, IA, ES, ESE, and ESN models, but were effectively predicted by the ICIM model.

scholarly journals Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose

2018 ◽  
Vol 28 ◽  
pp. 01036 ◽  
Author(s):  
Bartosz Szulczyński ◽  
Jacek Gębicki ◽  
Jacek Namieśnik
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electronic Nose ◽  
Sensory Analysis ◽  
Recognition System ◽  
Gas Mixtures ◽  
Pattern Recognition System ◽  
Mlr Model

The paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along with the fuzzy logic pattern recognition system can be successfully used to estimate the odour intensity of tested gas mixtures. The correctness of the results obtained using fuzzy logic was equal to 68%.

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