ASTGC: Attention-based Spatio-temporal Fusion Graph Convolution Model for Fine-grained Air Quality Analysis

Zhao, Yong; Fan, Shurui; Xia, Kewen; Jia, Yingmiao; Wang, Li; Yang, Wenbiao

doi:10.1007/s11869-023-01369-2

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…There is already a wide variety of approaches for ML-based prediction of PM concentrations in the scientific literature (Chae et al, 2021;Chang et al, 2020;Dhakal et al, 2021;Enebish et al, 2021;Kang et al, 2019;Karimian et al, 2019;Klingner & Sähn, 2008;McKendry, 2002;Nicklaß, 2010;Pérez, 2012;Pérez et al, 2000;Raimondo et al, 2007;Stadlober, 2013;Xayasouk et al, 2020;Zhao et al, 2023) , but their quality is difficult to assess and compare since they each differ in several aspects. For example, Pérez et al (2000) use linear regression and a feedforward neural network (FNN) to predict PM 2.5 concentrations in Santiago (Chile) in hourly resolution and evaluate the resulting models with a relative error measure.…”

Section: Introductionmentioning

confidence: 99%

evalPM: a framework for evaluating machine learning models for particulate matter prediction

Woltmann,

Deepe,

Hartmann

et al. 2023

Environ Monit Assess

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Air pollution through particulate matter (PM) is one of the largest threats to human health. To understand the causes of PM pollution and enact suitable countermeasures, reliable predictions of future PM concentrations are required. In the scientific literature, many methods exist for machine learning (ML)-based PM prediction, though their quality is difficult to compare because, among other things, they use different data sets and evaluate the resulting predictions differently. For a new data set, it is not apparent which of the existing prediction methods is best suited. In order to ease the assessment of said models, we present evalPM, a framework to easily create, evaluate, and compare different ML models for immission-based PM prediction. To achieve this, the framework provides flexibility regarding data sets, input features, target variables, model types, hyperparameters, and model evaluation. It has a modular design consisting of several components, each providing at least one required flexibility. The individual capabilities of the framework are demonstrated using 16 different models from the related literature by means of temporal prediction of PM concentrations for four European data sets, showing the capabilities and advantages of the evalPM framework. In doing so, it is shown that the framework allows fast creation and evaluation of ML-based PM prediction models.

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