Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city

Yuchi, Weiran; Gombojav, Enkhjargal; Boldbaatar, Buyantushig; Galsuren, Jargalsaikhan; Sarangerel, Enkhmaa; Beejin, Bolor; Naidan, Gerel; Оchir, Chimedsuren; Legtseg, Bayarkhuu; Byambaa, Tsogtbaatar; Barn, Prabjit; Henderson, Sarah B.; Janes, Craig R.; Lanphear, Bruce P.; McCandless, Lawrence C.; Takaro, Tim K.; Venners, Scott A.; Webster, Glenys M.; Allen, Ryan

doi:10.1016/j.envpol.2018.11.034

Cited by 124 publications

(74 citation statements)

References 37 publications

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“…We adopted a two-step process, removing highly intercorrelated variables for reducing the computational complexity of the algorithm and the dimension of the input data. Several related publications have shown that the predictive power of RFs may benefit from variable selection [50,71]. Finally, in terms of the most appropriate diversity index to be used with remote sensing data, the results indicate that, in our site, a remotely sensed spectral signal correlates better with Shannon's Index than Simpson's diversity, since, across all sensors, the Sentinel-2 MSI model provided the highest coefficient of determination.…”

Section: Discussionmentioning

confidence: 58%

“…Unlike to the majority of the previous studies, we did not use a linear regression approach for assessing the relationship between remote sensing data and field the measurements of tree species diversity. RF regression does not require any data distribution assumptions and can detect interactions and higher order relationships between independent variables without a priori specification of these terms [50]. Furthermore, RF are particularly appealing due to their ability to generalize even under a limited training samples regime, as often is the case in remote sensing applications [48].…”

Section: Discussionmentioning

confidence: 99%

“…More specifically, the variables of each image were compared one by one through the Spearman rank matrices. We then grouped these variables according to the area of the electromagnetic spectrum (i.e., visible near infrared and shortwave infrared) recorded by the respective sensor band, and we removed from further analysis the highly correlated variables (r higher or equal to 0.7 with a p-value smaller than 5%) with the highest ranking predictor in the respective part of the spectrum [47,50].…”

Section: Statistical Modellingmentioning

confidence: 99%

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A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

et al. 2020

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Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for H’ and 0.37 for D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems.

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Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Statistical Modellingmentioning

confidence: 99%

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A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

et al. 2020

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“…Every bootstrap sample for each CART is randomly selected from pre-datasets, and the features used are also extracted randomly from all features in a certain proportion. Specifically, every CART can train a nonlinear fitting model to estimate NSSR with the defined bootstrap sample; the output NSSR of RF is an average of the outputs of an individual CART [44]. Hence, because of its 'bagging' thought, RF algorithms typically yield a reduced bias of the estimations and, in general, good accuracies.…”

Section: Brief Introduction Of Machine Learning Algorithmsmentioning

confidence: 99%

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

Ying

2019

Remote Sensing

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Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m2, and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m2. There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.

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“…Many papers compared linear regression with random forest algorithms in other elds. As we can see, the random forest algorithm has not performed better in all eld and aspects (26)(27)(28), this reveals that random forest method can only take advantage over linear regression in some data models. So a more signi cant number of multi-centre data are needed to validate our outcomes in the eld of GFR estimation.…”

Section: Discussionmentioning

confidence: 92%

Applying Random Forest Model Algorithm to GFR estimation

Yang

Chong

et al. 2020

Preprint

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Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P < 0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P < 0.01, 19.08 vs 20.60, P < 0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

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Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city

Cited by 124 publications

References 37 publications

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

Applying Random Forest Model Algorithm to GFR estimation

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