FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4282007141> ?p ?o ?g. }

• W4282007141 endingPage "55" @default.
• W4282007141 startingPage "38" @default.
• W4282007141 abstract "The continuous distributions of PM2.5 concentrations and predictor variables in the surrounding regions influence the PM2.5 concentrations in the prediction positions notably, yet few machine learning models quantified the spatially continuous interactions between PM2.5 concentrations and predictor variations, which limits the prediction accuracy. To fill this gap, a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN) was proposed. For STWC-DNN, three sub-networks, Single Pixel Network (SPN), Multiple Station Network (MSN), and Continuous Region Network (CRN) were designed to analyze the influence of predictor variables at the prediction position, the influence of PM2.5 concentrations from surrounding stations, and the influence of continuous raster predictor variables from surrounding pixels respectively. STWC-DNN was experimented using hourly Himawari AOD data and the outputs were compared with a series of advanced models. STWC-DNN achieved higher accuracy than existing models and the sample-based, time-based, and station-based 10-fold cross-validation (CV) R2 were 0.92, 0.90, and 0.79, respectively. The principle of establishing STWC-DNN sheds useful lights on the effective use of raster predictor variables and automatic spatiotemporal weight function to better estimate PM2.5 and other airborne pollutants based on multiple data sources. The codes of STWC-DNN are now available at https://github.com/wangzh2022/STWC-DNN." @default.
• W4282007141 created "2022-06-13" @default.
• W4282007141 creator A5010577970 @default.
• W4282007141 creator A5012026072 @default.
• W4282007141 creator A5023919188 @default.
• W4282007141 creator A5028779704 @default.
• W4282007141 creator A5042371555 @default.
• W4282007141 creator A5055819576 @default.
• W4282007141 creator A5065058975 @default.
• W4282007141 creator A5069352060 @default.
• W4282007141 creator A5078670307 @default.
• W4282007141 date "2022-08-01" @default.
• W4282007141 modified "2023-10-17" @default.
• W4282007141 title "The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)" @default.
• W4282007141 cites W1588967193 @default.
• W4282007141 cites W1971794754 @default.
• W4282007141 cites W1995228070 @default.
• W4282007141 cites W2011740524 @default.
• W4282007141 cites W2054806977 @default.
• W4282007141 cites W2066281540 @default.
• W4282007141 cites W2070861023 @default.
• W4282007141 cites W2084103053 @default.
• W4282007141 cites W2090436332 @default.
• W4282007141 cites W2122290731 @default.
• W4282007141 cites W2136811230 @default.
• W4282007141 cites W2161669929 @default.
• W4282007141 cites W2297827415 @default.
• W4282007141 cites W2312602772 @default.
• W4282007141 cites W2345254164 @default.
• W4282007141 cites W2399455941 @default.
• W4282007141 cites W2480175994 @default.
• W4282007141 cites W2497200023 @default.
• W4282007141 cites W2516758599 @default.
• W4282007141 cites W2620300958 @default.
• W4282007141 cites W2739452932 @default.
• W4282007141 cites W2750146434 @default.
• W4282007141 cites W2776069591 @default.
• W4282007141 cites W2788388592 @default.
• W4282007141 cites W2788693790 @default.
• W4282007141 cites W2791444549 @default.
• W4282007141 cites W2796935963 @default.
• W4282007141 cites W2808247753 @default.
• W4282007141 cites W2811009165 @default.
• W4282007141 cites W2914670930 @default.
• W4282007141 cites W2917616628 @default.
• W4282007141 cites W2930499469 @default.
• W4282007141 cites W2941635653 @default.
• W4282007141 cites W2953978338 @default.
• W4282007141 cites W2969384816 @default.
• W4282007141 cites W2996041315 @default.
• W4282007141 cites W3014395405 @default.
• W4282007141 cites W3015976670 @default.
• W4282007141 cites W3044909247 @default.
• W4282007141 cites W3122817556 @default.
• W4282007141 cites W3122993262 @default.
• W4282007141 doi "https://doi.org/10.1016/j.isprsjprs.2022.05.011" @default.
• W4282007141 hasPublicationYear "2022" @default.
• W4282007141 type Work @default.
• W4282007141 citedByCount "10" @default.
• W4282007141 countsByYear W42820071412022 @default.
• W4282007141 countsByYear W42820071412023 @default.
• W4282007141 crossrefType "journal-article" @default.
• W4282007141 hasAuthorship W4282007141A5010577970 @default.
• W4282007141 hasAuthorship W4282007141A5012026072 @default.
• W4282007141 hasAuthorship W4282007141A5023919188 @default.
• W4282007141 hasAuthorship W4282007141A5028779704 @default.
• W4282007141 hasAuthorship W4282007141A5042371555 @default.
• W4282007141 hasAuthorship W4282007141A5055819576 @default.
• W4282007141 hasAuthorship W4282007141A5065058975 @default.
• W4282007141 hasAuthorship W4282007141A5069352060 @default.
• W4282007141 hasAuthorship W4282007141A5078670307 @default.
• W4282007141 hasConcept C124101348 @default.
• W4282007141 hasConcept C153180895 @default.
• W4282007141 hasConcept C154945302 @default.
• W4282007141 hasConcept C160633673 @default.
• W4282007141 hasConcept C181844469 @default.
• W4282007141 hasConcept C41008148 @default.
• W4282007141 hasConcept C50644808 @default.
• W4282007141 hasConceptScore W4282007141C124101348 @default.
• W4282007141 hasConceptScore W4282007141C153180895 @default.
• W4282007141 hasConceptScore W4282007141C154945302 @default.
• W4282007141 hasConceptScore W4282007141C160633673 @default.
• W4282007141 hasConceptScore W4282007141C181844469 @default.
• W4282007141 hasConceptScore W4282007141C41008148 @default.
• W4282007141 hasConceptScore W4282007141C50644808 @default.
• W4282007141 hasFunder F4320321001 @default.
• W4282007141 hasFunder F4320334977 @default.
• W4282007141 hasLocation W42820071411 @default.
• W4282007141 hasOpenAccess W4282007141 @default.
• W4282007141 hasPrimaryLocation W42820071411 @default.
• W4282007141 hasRelatedWork W2033914206 @default.
• W4282007141 hasRelatedWork W2109698133 @default.
• W4282007141 hasRelatedWork W2136485282 @default.
• W4282007141 hasRelatedWork W2145393847 @default.
• W4282007141 hasRelatedWork W2159543292 @default.
• W4282007141 hasRelatedWork W2386387936 @default.
• W4282007141 hasRelatedWork W2546871836 @default.
• W4282007141 hasRelatedWork W3093742557 @default.

• next