Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction

• URL: http://arxiv.org/abs/2105.13125v1
• Date: Mon, 24 May 2021 15:57:31 GMT
• Title: Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
• Authors: Zhengjing Ma, Gang Mei, Salvatore Cuomo, Francesco Piccialli
• Abstract summary: This paper proposes a deep learning method for fusing heterogeneous collected data from multiple monitoring points using graph convolutional networks (GCNs) In the application scenario of air quality prediction, it is observed that the fused data derived from the RBF-based fusion approach achieve satisfactory consistency. The proposed method is applicable for similar scenarios where continuous heterogeneous data are collected from multiple monitoring points scattered across a study area.
• Score: 4.9960351797442515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsistencies and imperfections in heterogeneous data while also failing to consider the (1) spatial correlations among monitoring points or (2) predictions for the entire study area. To address the above problems, this paper proposes a deep learning method for fusing heterogeneous data collected from multiple monitoring points using graph convolutional networks (GCNs) to predict the future trends of some observations and evaluates its effectiveness by applying it in an air quality predictions scenario. The essential idea behind the proposed method is to (1) fuse the collected heterogeneous data based on the locations of the monitoring points with regard to their spatial correlations and (2) perform prediction based on global information rather than local information. In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations. In the application scenario of air quality prediction, it is observed that (1) the fused data derived from the RBF-based fusion approach achieve satisfactory consistency; (2) the performances of the prediction models based on fused data are better than those based on raw data; and (3) the STGCN model achieves the best performance when compared to those of all baseline models. The proposed method is applicable for similar scenarios where continuous heterogeneous data are collected from multiple monitoring points scattered across a study area.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Sub-graph Based Diffusion Model for Link Prediction [43.15741675617231]
  Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities. We build a novel generative model for link prediction using a dedicated design to decompose the likelihood estimation process via the Bayesian formula. Our proposed method presents numerous advantages: (1) transferability across datasets without retraining, (2) promising generalization on limited training data, and (3) robustness against graph adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-13T02:23:55Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• CODA: Temporal Domain Generalization via Concept Drift Simulator [34.21255368783787]
  In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends. We propose the COncept Drift simulAtor framework incorporating a predicted feature correlation matrix to simulate future data for model training.
  arXiv  Detail & Related papers  (2023-10-02T18:04:34Z)
• Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models [22.24457254575906]
  In order for robots to safely navigate in unseen scenarios, it is important to accurately detect out-of-training-distribution (OoD) situations online. We propose a novel approach to embed existing domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on receding-horizon predictions.
  arXiv  Detail & Related papers  (2023-09-13T01:02:42Z)
• A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction [16.337207503536384]
  We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm. We show that the proposed method yields accuracy improvements as high as 44.5% and 39.3% for state-of-health estimation and remaining useful life estimation.
  arXiv  Detail & Related papers  (2023-05-13T07:20:31Z)
• Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data [68.62134204367668]
  This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace. We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated. The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
  arXiv  Detail & Related papers  (2023-02-14T17:02:35Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation [51.083573770706636]
  This work uses relational inference to fill in the incomplete data. We propose Omni-Relational Network (OR-Net) to model the pointwise relativity in two aspects.
  arXiv  Detail & Related papers  (2021-05-02T06:05:54Z)
• Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
  This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
  arXiv  Detail & Related papers  (2021-04-27T18:17:42Z)
• Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms [2.266704469122763]
  This paper proposes a new methodology for performing Bayesian inference in imaging inverse problems where the prior knowledge is available in the form of training data. We establish the existence and well-posedness of the associated posterior moments under easily verifiable conditions. A model accuracy analysis suggests that the Bayesian probability probabilities reported by the data-driven models are also remarkably accurate under a frequentist definition.
  arXiv  Detail & Related papers  (2021-03-18T11:34:08Z)

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.