A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network

• URL: http://arxiv.org/abs/2012.03781v1
• Date: Mon, 7 Dec 2020 15:22:01 GMT
• Title: A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network
• Authors: Fuxin Jiang, Chengyuan Zhang, Shaolong Sun, Jingyun Sun
• Abstract summary: This study develops a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise. The forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.
• Score: 2.2175470459999636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modelling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.

Related papers

• Enhancing PM2.5 Data Imputation and Prediction in Air Quality Monitoring Networks Using a KNN-SINDy Hybrid Model [0.0]
  Air pollution, particularly particulate matter (PM2.5), poses significant risks to public health and the environment. This study explores the application of Sparse Identification of Dynamics (SINDy2.5) for imputing missing PM2.5 data by predicting, using training data from 2016, and comparing its performance with the established Soft Impute (SI) and K-Nearest Neighbors (KNN) methods.
  arXiv  Detail & Related papers  (2024-09-18T02:08:17Z)
• Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet) [0.3848364262836075]
  We propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system.
  arXiv  Detail & Related papers  (2024-06-27T13:14:20Z)
• CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding [62.075029712357]
  This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models and weight the guidance with precision weights estimated by the inherent property of diffusion models. We apply CogDPM to real-world prediction tasks using the United Kindom precipitation and surface wind datasets.
  arXiv  Detail & Related papers  (2024-05-03T15:54:50Z)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation [16.540748935603723]
  We propose a Swin Transformer-based Variational Recurrent Neural Network (SwinVRNN), which is a weather forecasting model combining a SwinRNN predictor with a perturbation module. SwinVRNN surpasses operational ECMWF Integrated Forecasting System (IFS) on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days.
  arXiv  Detail & Related papers  (2022-05-26T05:11:58Z)
• A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks [7.131106953836335]
  The prediction system of PM2.5 propagation provides more detailed and accurate information as an early warning system to reduce health impacts on the community. This research was conducted by using dataset of air quality monitoring systems in Taiwan. In general, the model is able to provide accurate predictive results by considering the bonds among measurement nodes in both spatially and temporally.
  arXiv  Detail & Related papers  (2021-01-15T17:00:04Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)
• PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting [15.587337304295819]
  We develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. The proposed model has been deployed online to provide free forecasting service.
  arXiv  Detail & Related papers  (2020-02-10T03:33:54Z)

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.