scholarly journals Estimating PM2.5 concentrations in contiguous eastern coastal zone of China using MODIS AOD and a two-stage random forest model

2021 ◽  
Author(s):  
Lijuan Yang ◽  
Hanqiu Xu ◽  
Shaode Yu
Keyword(s):  
Random Forest ◽  
Missing Values ◽  
Ground Level ◽  
River Delta ◽  
Random Forest Model ◽  
Pearl River Delta Region ◽  
Two Stage ◽  
Forest Model ◽  
Full Coverage ◽  
Modis Aod

AbstractThe coarse moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3 km AOD to obtain the PM2.5 concentrations with full-coverage in a contiguous coastal developed region, i.e., Yangtze River Delta-Fujian-Pearl River Delta region of China (YRD-FJ-PRD). A first-stage random forest integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest derived AOD and MODIS 3 km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD-FJ-PRD region, and we achieved a site-based cross validation (CV) R2 of 0.87 and a time-based CV R2 of 0.85, respectively. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40 and 35 μg/m3 in YRD, PRD and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas.

Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model

2021 ◽  
Vol 248 ◽  
pp. 105146 ◽  
Author(s):  
Tingting Jiang ◽  
Bin Chen ◽  
Zhen Nie ◽  
Zhehao Ren ◽  
Bing Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Model ◽  
Two Stage ◽  
Forest Model ◽  
Full Coverage

scholarly journals A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

2019 ◽  
Vol 11 (13) ◽  
pp. 1558 ◽  
Author(s):  
Zhenqun Hua ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Qian Du
Keyword(s):  
Missing Values ◽  
Additive Model ◽  
Ground Level ◽  
Yangtze River Delta ◽  
Two Stage ◽  
High Coverage ◽  
Daily Average ◽  
Full Coverage ◽  
Spatial Coverage ◽  
Modis Aod

Current PM2.5 retrieval maps have many missing values, which seriously hinders their performance in real applications. This paper presents a framework to map full-coverage daily average PM2.5 concentrations from MODIS C6 aerosol optical depth (AOD) products and fill missing pixels in both the AOD and PM2.5 maps. First, a two-stage inversed variance weights (IVW) algorithm was adopted to fuse the MODIS C6 Terra and Aqua AOD products, which fills missing data in MODIS standard AOD data and obtains a high coverage daily average. After that, using the fused MODIS daily average AOD and ground-level PM2.5 in all grid cells, a two-stage generalized additive model (GAM) was implemented to obtain the full-coverage PM2.5 concentrations. Experiments on the Yangtze River Delta (YRD) in 2013–2016 were carefully designed to validate the performance of our proposed framework. The results show that the two-stage IVW could not only improve the spatial coverage of MODIS AOD against the original standard product by 230%, but could also keep its data accuracy. When compared with the ground-level measurements, the two-stage GAM can obtain accurate PM2.5 concentration estimates (R2 = 0.78, RMSE = 19.177 μg/m3, and RPE = 28.9%). Moreover, our method performs better than the inverse distance weighted method and kriging methods in mapping full-coverage daily PM2.5 concentrations. Therefore, the proposed framework provides a good methodology for retrieving full-coverage daily average PM2.5 concentrations from MODIS standard AOD products.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model

High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region

2019 ◽  
Vol 203 ◽  
pp. 70-78 ◽  
Author(s):  
Chen Zhao ◽  
Zhaorong Liu ◽  
Qing Wang ◽  
Jie Ban ◽  
Nancy Xi Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Random Forest Model ◽  
Forest Model ◽  
Full Coverage ◽  
Model Approach

scholarly journals FunSAV: Predicting the Functional Effect of Single Amino Acid Variants Using a Two-Stage Random Forest Model

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e43847 ◽  
Author(s):  
Mingjun Wang ◽  
Xing-Ming Zhao ◽  
Kazuhiro Takemoto ◽  
Haisong Xu ◽  
Yuan Li ◽  
...  
Keyword(s):  
Amino Acid ◽  
Random Forest ◽  
Random Forest Model ◽  
Single Amino Acid ◽  
Functional Effect ◽  
Two Stage ◽  
Forest Model ◽  
Amino Acid Variants

scholarly journals A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

2021 ◽  
Vol 13 (18) ◽  
pp. 3657
Author(s):  
Chau-Ren Jung ◽  
Wei-Ting Chen ◽  
Shoji F. Nakayama
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Model Performance ◽  
Random Forest Model ◽  
Scale Model ◽  
National Scale ◽  
Two Stage ◽  
Forest Model ◽  
Monsoon Area ◽  
Fold Cross Validation

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

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