Forecasting Smog Clouds With Deep Learning

• URL: http://arxiv.org/abs/2410.02759v1
• Date: Thu, 3 Oct 2024 17:59:13 GMT
• Title: Forecasting Smog Clouds With Deep Learning
• Authors: Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro,
• Abstract summary: We propose an integrated hierarchical model architecture inspired by air pollution dynamics and atmospheric science. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.
• Score: 6.144680854063938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & PM2.5) with meteorological covariates between two locations using various deep learning models, with a focus on long short-term memory (LSTM) and gated recurrent unit (GRU) architectures. In particular, we propose an integrated, hierarchical model architecture inspired by air pollution dynamics and atmospheric science that employs multi-task learning and is benchmarked by unidirectional and fully-connected models. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.

Related papers

• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Diffusion Models in Low-Level Vision: A Survey [82.77962165415153]
  diffusion model-based solutions have emerged as widely acclaimed for their ability to produce samples of superior quality and diversity. We present three generic diffusion modeling frameworks and explore their correlations with other deep generative models. We summarize extended diffusion models applied in other tasks, including medical, remote sensing, and video scenarios.
  arXiv  Detail & Related papers  (2024-06-17T01:49:27Z)
• Variable importance measure for spatial machine learning models with application to air pollution exposure prediction [2.633085745593072]
  The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health effects of air pollution. We tackle these challenges in two datasets: sulfur (S) from regulatory United States national PM2.5 sub-species data and ultrafine particles (UFP) from a new Seattle-area traffic-related air pollution dataset. Our key contribution is a leave-one-out approach for variable importance that leads to interpretable and comparable measures for a broad class of models.
  arXiv  Detail & Related papers  (2024-06-04T05:51:36Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations [8.71735078449217]
  This paper proposes a unified downscaling approach leveraging meta-learning. We trained variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS. The proposed method can be extended to downscale convective precipitation, potential, energy height, humidity CFS, S2S and CMIP6 at differenttemporal scales.
  arXiv  Detail & Related papers  (2024-04-26T06:31:44Z)
• Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes [0.7970333810038046]
  Methane concentration inversion, plume segmentation, and emission rate estimation are three subtasks of methane emission monitoring. We introduce a novel deep learning-based framework for quantitative methane emission monitoring from remote sensing images. We train a U-Net network for methane concentration inversion, a Mask R-CNN network for methane plume segmentation, and a ResNet-50 network for methane emission rate estimation.
  arXiv  Detail & Related papers  (2024-01-23T16:04:19Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds [13.200838744804942]
  We produce novel and intuitive visualizations for the organization of droplet sizes. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.
  arXiv  Detail & Related papers  (2023-10-31T04:25:00Z)
• Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators [58.728312684306545]
  Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
  arXiv  Detail & Related papers  (2022-10-31T04:04:03Z)
• A comparative study of stochastic and deep generative models for multisite precipitation synthesis [0.0]
  We compare two open-source weather generators: IBMWeathergen and RPrec, and two deep generative models: GAN and VAE, on a variety of metrics. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task.
  arXiv  Detail & Related papers  (2021-07-16T18:35:24Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.