Ozone level forecasting in Mexico City with temporal features and interactions

• URL: http://arxiv.org/abs/2411.07259v1
• Date: Mon, 04 Nov 2024 20:08:08 GMT
• Title: Ozone level forecasting in Mexico City with temporal features and interactions
• Authors: J. M. Sánchez Cerritos, J. A. Martínez-Cadena, A. Marín-López, J. Delgado-Fernández,
• Abstract summary: This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City. Our findings show that incorporating temporal features and interactions improves the accuracy of the models.
• Score: 0.0
• License:
• Abstract: Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• 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)
• Cluster-Segregate-Perturb (CSP): A Model-agnostic Explainability Pipeline for Spatiotemporal Land Surface Forecasting Models [5.586191108738564]
  This paper introduces a pipeline that integrates principles from both perturbation-based explainability techniques like LIME and global marginal explainability like PDP. The proposed pipeline simplifies the undertaking of diverse investigative analyses, such as marginal sensitivity analysis, marginal correlation analysis, lag analysis, etc., on complex land surface forecasting models.
  arXiv  Detail & Related papers  (2024-08-12T04:29:54Z)
• VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting [58.12667617617306]
  We propose VegeDiff for the geospatial vegetation forecasting task. VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes. By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods.
  arXiv  Detail & Related papers  (2024-07-17T14:15:52Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs [2.7708222692419735]
  We use a Convolutional LSTM (ConvLSTM) architecture to address this task. We predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography. Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines.
  arXiv  Detail & Related papers  (2022-10-24T23:03:36Z)
• Assessing the Lockdown Effects on Air Quality during COVID-19 Era [8.733926566837676]
  In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2) The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome) The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities.
  arXiv  Detail & Related papers  (2021-06-25T16:39:44Z)
• A data-driven approach to the forecasting of ground-level ozone concentration [0.0]
  We present a machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration in southern Switzerland. We show how weighting helps in increasing the accuracy of the forecasts for specific ranges of ozone's daily peak values.
  arXiv  Detail & Related papers  (2020-10-14T09:35:48Z)
• A Novel CMAQ-CNN Hybrid Model to Forecast Hourly Surface-Ozone Concentrations Fourteen Days in Advance [0.19573380763700707]
  Currently available numerical modeling systems for air quality predictions can forecast 24 to 48 hours in advance. We develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.
  arXiv  Detail & Related papers  (2020-08-13T16:02:05Z)

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.