Data Integration for ML-CNPM_2.5: A Public Sample Dataset Based on Machine Learning Models and Remote Sensing Technology Applied for Estimating Ground-level PM_2.5 in China

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 15

Published: Jan. 1, 2024

Ambient fine particulate matter (PM _2.5 ) has significant adverse effects on human health, thereby urgent hunger for accurate monitoring of ground-level PM , especially its space distribution. Since satellites can observe the Earth a large spatial scale, remote sensing technology be applied to estimate concentrations at national level. Based it and machine learning (ML) methods, numerous studies mapped high-accuracy, wholesale continuous . However, different models data in these made their results incomparable, more samples were needed provided. Here, large-column long-term sample dataset (ML-CNPM ML-based was constructed with 5,076,608 records 24 features from 2014 2023 China. Multiple approaches used guarantee quantity quality dataset. Due comprehensiveness objectivity, ML-CNPM train validate models, further improving accuracy estimating. Using eight basic also as baseline judging other derivative models. These daily full-coverage most performed well, 10-fold cross-validation RMSE 16.94-11.21μg/m ³ R xmlns:xlink="http://www.w3.org/1999/xlink">2 0.71-0.89, which is consistent previous effectively capture trends period suffered high pollution. Overall, our construct, validate, compare various estimation, helping develop new algorithms higher robustness.

Language: Английский

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Progress in Current Research and Applications of the Geostationary Ocean Color Imager Series (GOCI and GOCI-II): A Bibliometric Analysis

Korean Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 40(5-2), P. 727 - 739

Published: Oct. 16, 2024

Language: Английский

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A deep learning-based combination method of spatio-temporal prediction for regional mining surface subsidence

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 19, 2024

In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, caused by can be monitored predicted using various methods, e.g., probability integral method deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due insufficient consideration of spatial correlation temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for subsidence. Our employs K-means clustering partition data, allowing the application gate recurrent unit (GRU) model capture nonlinear relationships time series within each partition. Optimization snake optimization (SO) further enhances globally. Validation shows our outperforms traditional Long Short-Term Memory (LSTM) GRU models, achieving 99.1% sample pixels with less than 8 mm absolute error.

Language: Английский

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Estimating Spatiotemporal Aerosol Index between MODIS and Sentinel 5 in Medan City

Journal of Physics Conference Series, Journal Year: 2023, Volume and Issue: 2672(1), P. 012007 - 012007

Published: Dec. 1, 2023

Abstract In this paper, long-term variability and spatially contiguous aerosols were primarily responsible for air pollution in Medan, Indonesia. Medan quality is become more threatening the last few years. Estimating most polluted vulnerable to climate change, ambient aerosol, can control adverse effects of poor negative impact on human health (e.g., asthma). This study estimates algorithmic analytical approaches that compared Aerosol Optical Depth (AOD) data from MODIS (Moderate-Resolution Imaging Spectroradiometer) a series MCD19A2 at 0.55 microns Absorbing Index (AAI) Sentinel-5P variations 0.34 0.380 wavelengths. High-temporal-resolution imagery projected based wavelength-dependent changes light interacting with aerosol particles atmosphere 2020 2023. Results comparison between different index products are derived growth values 58.81 percent AOD area over city, indicating relatively hazy or heavy 2023, exceeding total value increase 45.24 AAI amount during until Overall, highlights estimation indicate seasonal location-specific would exacerbate serious problems Medan.

Language: Английский

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Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 28

Published: Oct. 3, 2024

Language: Английский

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Ground subsidence prediction with high precision: a novel spatiotemporal prediction model with Interferometric Synthetic Aperture Radar technology

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Oct. 4, 2024

Language: Английский

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