GreenEyes: An Air Quality Evaluating Model based on WaveNet

• URL: http://arxiv.org/abs/2212.04175v1
• Date: Thu, 8 Dec 2022 10:28:57 GMT
• Title: GreenEyes: An Air Quality Evaluating Model based on WaveNet
• Authors: Kan Huang, Kai Zhang, Ming Liu
• Abstract summary: We propose a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module. We show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set.
• Score: 11.513011576336744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accompanying rapid industrialization, humans are suffering from serious air pollution problems. The demand for air quality prediction is becoming more and more important to the government's policy-making and people's daily life. In this paper, We propose GreenEyes -- a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module for capturing the hidden interactions between features of multi-channel inputs. To evaluate the effectiveness of our proposed method, we carry out several experiments including an ablation study on our collected and preprocessed air quality data near HKUST. The experimental results show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set. We have also released our standalone dataset at https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are publicly available at https://github.com/AI-Huang/AirEvaluation

Related papers

• Satellite Streaming Video QoE Prediction: A Real-World Subjective Database and Network-Level Prediction Models [59.061552498630874]
  We introduce the LIVE-Viasat Real-World Satellite QoE Database. This database consists of 179 videos recorded from real-world streaming services affected by various authentic distortion patterns. We demonstrate the usefulness of this unique new resource by evaluating the efficacy of QoE-prediction models on it. We also created a new model that maps the network parameters to predicted human perception scores, which can be used by ISPs to optimize the video streaming quality of their networks.
  arXiv  Detail & Related papers  (2024-10-17T18:22:50Z)
• Spatio-Temporal Field Neural Networks for Air Quality Inference [13.582971831446647]
  We propose a new model, Spatio-Temporal Field Neural Network, and its corresponding framework, Pyramidal Inference. Our model achieves state-of-the-art performance in nationwide air quality inference in the Chinese Mainland.
  arXiv  Detail & Related papers  (2024-03-02T10:14:42Z)
• Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings [0.0]
  Air pollution stands as the fourth leading cause of death globally. This study proposes a novel machine learning approach for accurate air quality prediction using two months of air quality data.
  arXiv  Detail & Related papers  (2024-01-09T05:52:02Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset [4.190243190157989]
  This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset from the PurpleAir network. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. We conduct preliminary experiments using both classic and modern-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.
  arXiv  Detail & Related papers  (2023-06-24T12:10:16Z)
• Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting [0.0]
  Air pollution varies in a manner and depends on many complicated factors. An image-based representation may not be ideal as air pollution and other impact factors have natural graph structures. A Graph Convolutional Network (GCN) can efficiently represent the spatial features of air quality readings in the whole city. Our approach is superior to a hybrid GCN-based method in a real-world air pollution dataset.
  arXiv  Detail & Related papers  (2023-04-25T07:57:07Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• 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)
• MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos [74.22132693931145]
  We study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks. The second contribution is a new model, namely MSNet, which contains novel region proposal network designs.
  arXiv  Detail & Related papers  (2020-06-30T02:23:05Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

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.