FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3000260079> ?p ?o ?g. }

• W3000260079 endingPage "264" @default.
• W3000260079 startingPage "264" @default.
• W3000260079 abstract "Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values." @default.
• W3000260079 created "2020-01-23" @default.
• W3000260079 creator A5019176178 @default.
• W3000260079 date "2020-01-13" @default.
• W3000260079 modified "2023-10-17" @default.
• W3000260079 title "A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5" @default.
• W3000260079 cites W1173523477 @default.
• W3000260079 cites W1606767976 @default.
• W3000260079 cites W1795776638 @default.
• W3000260079 cites W1801405843 @default.
• W3000260079 cites W1964931633 @default.
• W3000260079 cites W1970486901 @default.
• W3000260079 cites W1971844317 @default.
• W3000260079 cites W1972585310 @default.
• W3000260079 cites W1976991085 @default.
• W3000260079 cites W1990797640 @default.
• W3000260079 cites W2010026170 @default.
• W3000260079 cites W2011740524 @default.
• W3000260079 cites W2018630616 @default.
• W3000260079 cites W2021439613 @default.
• W3000260079 cites W2028820461 @default.
• W3000260079 cites W2050250373 @default.
• W3000260079 cites W2053757129 @default.
• W3000260079 cites W2057829983 @default.
• W3000260079 cites W2066625239 @default.
• W3000260079 cites W2067129339 @default.
• W3000260079 cites W2069977802 @default.
• W3000260079 cites W2075924373 @default.
• W3000260079 cites W2080010520 @default.
• W3000260079 cites W2083944525 @default.
• W3000260079 cites W2085260550 @default.
• W3000260079 cites W2102553184 @default.
• W3000260079 cites W2102834380 @default.
• W3000260079 cites W2104621935 @default.
• W3000260079 cites W2108162680 @default.
• W3000260079 cites W2110849583 @default.
• W3000260079 cites W2112335693 @default.
• W3000260079 cites W2122825543 @default.
• W3000260079 cites W2138017294 @default.
• W3000260079 cites W2161669929 @default.
• W3000260079 cites W2178279283 @default.
• W3000260079 cites W2194775991 @default.
• W3000260079 cites W2292421103 @default.
• W3000260079 cites W2294971749 @default.
• W3000260079 cites W2302255633 @default.
• W3000260079 cites W2314479789 @default.
• W3000260079 cites W2316861799 @default.
• W3000260079 cites W2323112777 @default.
• W3000260079 cites W2323483937 @default.
• W3000260079 cites W2334392363 @default.
• W3000260079 cites W2339392509 @default.
• W3000260079 cites W2412588858 @default.
• W3000260079 cites W2461336101 @default.
• W3000260079 cites W2480175994 @default.
• W3000260079 cites W2555269287 @default.
• W3000260079 cites W2566321408 @default.
• W3000260079 cites W2588952554 @default.
• W3000260079 cites W2588978790 @default.
• W3000260079 cites W2607054926 @default.
• W3000260079 cites W2609482742 @default.
• W3000260079 cites W2611772571 @default.
• W3000260079 cites W2617480188 @default.
• W3000260079 cites W2620300958 @default.
• W3000260079 cites W2623406985 @default.
• W3000260079 cites W2626098613 @default.
• W3000260079 cites W2690621383 @default.
• W3000260079 cites W2742946820 @default.
• W3000260079 cites W2765105153 @default.
• W3000260079 cites W2769333905 @default.
• W3000260079 cites W2777546168 @default.
• W3000260079 cites W2789690971 @default.
• W3000260079 cites W2795360835 @default.
• W3000260079 cites W2804076223 @default.
• W3000260079 cites W2804926452 @default.
• W3000260079 cites W2851862743 @default.
• W3000260079 cites W2890012302 @default.
• W3000260079 cites W2891750563 @default.
• W3000260079 cites W2896264495 @default.
• W3000260079 cites W2900016313 @default.
• W3000260079 cites W2901541896 @default.
• W3000260079 cites W2902960097 @default.
• W3000260079 cites W2906500497 @default.
• W3000260079 cites W2914858494 @default.
• W3000260079 cites W2917616628 @default.
• W3000260079 cites W2937452004 @default.
• W3000260079 cites W2949545510 @default.
• W3000260079 cites W4212883601 @default.
• W3000260079 doi "https://doi.org/10.3390/rs12020264" @default.
• W3000260079 hasPublicationYear "2020" @default.
• W3000260079 type Work @default.
• W3000260079 sameAs 3000260079 @default.
• W3000260079 citedByCount "28" @default.
• W3000260079 countsByYear W30002600792020 @default.
• W3000260079 countsByYear W30002600792021 @default.
• W3000260079 countsByYear W30002600792022 @default.
• W3000260079 countsByYear W30002600792023 @default.
• W3000260079 crossrefType "journal-article" @default.
• W3000260079 hasAuthorship W3000260079A5019176178 @default.

• next