Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio

scholarly journals Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7248
Author(s):  
Fugen Jiang ◽  
Mykola Kutia ◽  
Arbi J. Sarkissian ◽  
Hui Lin ◽  
Jiangping Long ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Texture Features ◽  
Estimation Accuracy ◽  
Stem Volume ◽  
Estimation Model ◽  
Red Edge ◽  
Variable Combination ◽  
Coniferous Plantations ◽  
Sentinel 2

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

scholarly journals A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jun-He Yang ◽  
Ching-Hsue Cheng ◽  
Chia-Pan Chan
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Variable Selection ◽  
Water Level ◽  
Selection Method ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Missing Value ◽  
Variable Selection Method ◽  
Model Based

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Random forest-based approach for physiological functional variable selection for driver’s stress level classification

2018 ◽  
Vol 28 (1) ◽  
pp. 157-185 ◽  
Author(s):  
Neska El Haouij ◽  
Jean-Michel Poggi ◽  
Raja Ghozi ◽  
Sylvie Sevestre-Ghalila ◽  
Mériem Jaïdane
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Stress Level ◽  
Functional Variable ◽  
Selection For

Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest

2018 ◽  
Vol 70 ◽  
pp. 980-987 ◽  
Author(s):  
S.S. Matin ◽  
L. Farahzadi ◽  
S. Makaremi ◽  
S. Chehreh Chelgani ◽  
Gh. Sattari
Keyword(s):  
Compressive Strength ◽  
Random Forest ◽  
Variable Selection ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity

scholarly journals UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

2021 ◽  
Vol 13 (24) ◽  
pp. 5166
Author(s):  
Jianjun Wang ◽  
Qi Zhou ◽  
Jiali Shang ◽  
Chang Liu ◽  
Tingxuan Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Variable Selection ◽  
Winter Wheat ◽  
Wheat Variety ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Monitoring Method ◽  
Chlorophyll Contents ◽  
Winter Wheat Variety

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

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