Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates | Paper Citation Record

Observation bf149f1b-2509-4164-a216-ec0b535d20fe · outbound

This paper cites Breathing green: Maximising health and environmental benefits for active transportation users leveraging large scale air quality data,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Breathing green: Maximising health and environmental benefits for active transportation users leveraging large scale air quality data,

Reference 1

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Observation 34a4eaf8-0dfb-4b42-bc5f-c613cfcc3197 · outbound

This paper cites Particulate matter air pollution is a significant risk factor for cardiovascular disease,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Particulate matter air pollution is a significant risk factor for cardiovascular disease,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.790487Z

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Observation 935be304-a2d6-49f0-802d-388929564b63 · outbound

This paper cites Parking behaviour analysis of shared e-bike users based on a real-world dataset - A case study in Dublin, Ireland,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Parking behaviour analysis of shared e-bike users based on a real-world dataset - A case study in Dublin, Ireland,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.776809Z

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Observation a33c0ee5-a756-4d68-8a3e-65e3e86c5296 · outbound

This paper cites PM2.5 Exposure and asthma development: The key role of oxidative stress,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates PM2.5 Exposure and asthma development: The key role of oxidative stress,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.763371Z

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Observation 2d27f57c-3caf-4e01-8aca-8e3c26ca3d15 · outbound

This paper cites Relation between pm2.5 pollution and covid-19 mor- tality in western europe for the 2020–2022 period,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Relation between pm2.5 pollution and covid-19 mor- tality in western europe for the 2020–2022 period,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.749489Z

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Observation 0c5d5c97-25f9-42a5-8d67-8368be9be973 · outbound

This paper cites Temporal evolution of pm2.5 levels and covid-19 mortality in europe for the 2020–2022 period,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Temporal evolution of pm2.5 levels and covid-19 mortality in europe for the 2020–2022 period,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.736797Z

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Observation 5be0f0bb-bc55-4467-a291-76e0955d305f · outbound

This paper cites A comparative analysis for air quality estimation from traffic and meteorological data,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A comparative analysis for air quality estimation from traffic and meteorological data,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.724172Z

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Observation 5e453ca4-39b2-4884-82b9-ec9c4da98faa · outbound

This paper cites Machine learning for air quality prediction using meteorological and traffic related features,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Machine learning for air quality prediction using meteorological and traffic related features,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.711886Z

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Observation b214699b-1997-4998-9aa3-71e2b4918f43 · outbound

This paper cites Time series analysis and forecasting of air quality index of Dhaka City of Bangladesh,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Time series analysis and forecasting of air quality index of Dhaka City of Bangladesh,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.699098Z

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Observation a3a8ba6a-be19-4d4f-ae9b-1e083f2c14b2 · outbound

This paper cites Air-pollution prediction in smart city, deep learning approach,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Air-pollution prediction in smart city, deep learning approach,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.685492Z

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Observation b8c68198-af28-46f4-9008-c184ef62f912 · outbound

This paper cites A deep learning approach for prediction of air quality index in a metropolitan city,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A deep learning approach for prediction of air quality index in a metropolitan city,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.671611Z

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Observation 7694d17f-47c2-463c-993c-544ceee47059 · outbound

This paper cites Modeling air quality prediction using a deep learning approach: Method optimization and evaluation,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Modeling air quality prediction using a deep learning approach: Method optimization and evaluation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.658311Z

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Observation a2e819d1-9a10-4f27-ae7d-82833721f4c1 · outbound

This paper cites Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion probabilistic model,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion probabilistic model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.644896Z

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Observation 359c95f0-21bb-4229-9ead-027301c7acd5 · outbound

This paper cites Short-term wind power scenario generation based on conditional latent diffusion models,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Short-term wind power scenario generation based on conditional latent diffusion models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.631267Z

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Observation 26ef9c64-134f-4b7b-8921-232c48a6b63e · outbound

This paper cites Forecasting the exceedances of PM2.5 in an urban area,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Forecasting the exceedances of PM2.5 in an urban area,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.617865Z

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Observation 29fe582c-32e3-4581-aa8d-ef7d2ad6cd44 · outbound

This paper cites A Machine Learning Approach to Predict Air Quality in California,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A Machine Learning Approach to Predict Air Quality in California,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.604313Z

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Observation 7b226a67-6269-4459-85b8-95467758663e · outbound

This paper cites A hybrid deep learning technology for PM2.5 air quality forecasting,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A hybrid deep learning technology for PM2.5 air quality forecasting,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.591062Z

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Observation 957483ca-f7c4-4cd4-ba3f-dfc4c421bc47 · outbound

This paper cites Spatio-attention embedded recurrent neural network for air quality prediction,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Spatio-attention embedded recurrent neural network for air quality prediction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.577785Z

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Observation 360154d2-b5c9-4540-87d3-e1f276a617a9 · outbound

This paper cites Multi-scale spatiotemporal graph convolution network for air quality prediction,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Multi-scale spatiotemporal graph convolution network for air quality prediction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.564196Z

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Observation 2cc85c30-522c-48ed-a3e4-68ca528b3a3d · outbound

This paper cites PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.552271Z

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Observation efa0f99a-4c95-423b-b163-2b9af8f56448 · outbound

This paper cites Regional air quality forecasting using spatiotemporal deep learning,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Regional air quality forecasting using spatiotemporal deep learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.539837Z

Source-reported events for the cited work

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Observation e57b138c-b494-4527-b593-f5a760e43b34 · outbound

This paper cites A novel multi-pollutant space-time learning network for air pollution inference,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A novel multi-pollutant space-time learning network for air pollution inference,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.527072Z

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Observation 357c8732-ccf1-400d-a6cc-c1796cf3d20e · outbound

This paper cites Spatiotemporal deep learning model for citywide air pollution interpolation and prediction,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Spatiotemporal deep learning model for citywide air pollution interpolation and prediction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.513855Z

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Observation ad63975e-99bd-428e-a967-1ec994cac971 · outbound

This paper cites Linking of open and private data in dataspace: A case study of air quality monitoring and forecasting,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Linking of open and private data in dataspace: A case study of air quality monitoring and forecasting,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.500655Z

Source-reported events for the cited work

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Observation eaa4b309-7e7e-4389-a012-9ffa518c3a34 · outbound

This paper cites Quantifying road traffic impact on air quality in urban areas: A covid19- induced lockdown analysis in Italy,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Quantifying road traffic impact on air quality in urban areas: A covid19- induced lockdown analysis in Italy,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.487752Z

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Observation cf1df10b-5b89-4f96-ad6b-388b44228f22 · outbound

This paper cites Optimized feature selection for air quality index forecasting using GPR and SARIMA models,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Optimized feature selection for air quality index forecasting using GPR and SARIMA models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.474737Z

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Observation 401348ed-d256-42ae-b1b6-f04f4c8184a2 · outbound

This paper cites Comparison of missing data imputation methods in time series forecasting,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Comparison of missing data imputation methods in time series forecasting,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.461566Z

Source-reported events for the cited work

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Observation 4eff2446-a3e0-4297-8e1b-db7e84c7c36c · outbound

This paper cites A graph-based approach for missing sensor data imputation,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates A graph-based approach for missing sensor data imputation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.448509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c2d00cc-bdd4-4612-9786-dbbf6ae34b65 · outbound

This paper cites Random forest spatial interpolation,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Random forest spatial interpolation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.435038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6bc0fc40-f84b-4d3e-ae1d-3c8a7216b408 · outbound

This paper cites Spatiotempo- ral high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Spatiotempo- ral high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.421736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2fc083ad-fe1c-490c-b3f5-2e045bd8d32d · outbound

This paper cites Machine learning based approaches for imputation in time series data and their impact on forecasting,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Machine learning based approaches for imputation in time series data and their impact on forecasting,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.408353Z

Source-reported events for the cited work

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Observation 61d7ddc1-3e64-46ee-b612-a3fdeb4f5140 · outbound

This paper cites An air quality forecasting model based on improved convnet and RNN,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates An air quality forecasting model based on improved convnet and RNN,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.394074Z

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Observation ec133bd7-1989-46df-b276-d61feb1cd49d · outbound

This paper cites Air pollution prediction using lstm deep learning and metaheuristics algorithms,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Air pollution prediction using lstm deep learning and metaheuristics algorithms,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.380901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e9fdcd5-262e-4374-9968-cbb6c662fdff · outbound

This paper cites Google airview data - Dublin City,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Google airview data - Dublin City,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.368460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8074c60-6d8b-41cc-a56d-0945434a0a73 · outbound

This paper cites EPA open data,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates EPA open data,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.355418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e9156de-e424-491b-9b4d-84589f9cb536 · outbound

This paper cites Spatial distribution of pm2.5 mass and number concentrations in paris (france) from the pollutrack network of mobile sensors during 2018–2022,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Spatial distribution of pm2.5 mass and number concentrations in paris (france) from the pollutrack network of mobile sensors during 2018–2022,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.342423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation be0473c4-a028-46d7-ac6c-db368e71136c · outbound

This paper cites High resolution mapping of pm2.5 concentrations in paris (france) using mobile pollutrack sensors network in 2020,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates High resolution mapping of pm2.5 concentrations in paris (france) using mobile pollutrack sensors network in 2020,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.328457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 043684fb-f2d4-4b05-b9d4-0c57edd77f92 · outbound

This paper cites DCC SCATS detector volume (jan-jun 2022),.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates DCC SCATS detector volume (jan-jun 2022),

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.314002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdfc57a3-e19b-46b4-a481-a9e73a5d6dfe · outbound

This paper cites DCC SCATS detector volume (jul-dec 2022),.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates DCC SCATS detector volume (jul-dec 2022),

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.299661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ae1f21e3-b2bc-45ed-bb4a-0450a48ff1e7 · outbound

This paper cites Historical climate data,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Historical climate data,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.284740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 644eba4c-97c1-4a73-9139-cb6e0f4c131e · outbound

This paper cites Air pollution index levels - Europe,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Air pollution index levels - Europe,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.270919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e4817be0-6f97-48bf-ac7b-964022d28895 · outbound

This paper cites Geneva: World Health Organization,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Geneva: World Health Organization,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.256887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0af49d18-8862-4462-a5e0-9daa810cfe6f · outbound

This paper cites SMOTE: Synthetic minority over-sampling technique,.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates SMOTE: Synthetic minority over-sampling technique,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.228570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d4c5deca-7889-42cb-abf2-b3fcc4932c13 · outbound

This paper cites Available: https://www.who.int/publications/i/item/9789 240034228.

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates Available: https://www.who.int/publications/i/item/9789 240034228

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:40:30.242741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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