Angelo Casolaro, Vincenzo Capone, Francesco Camastra
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引用次数: 0
Abstract
Nitrogen dioxide pollution is an ongoing and growing environmental issue that affects human health in developed Western countries. This study introduced a Bayesian attention-based deep neural network model for predicting ground-level nitrogen dioxide concentrations. The proposed model integrates the principles of the Bayesian neural network and the attention mechanism, enabling it to produce predicted values and their associated uncertainties, expressed as standard deviations. The proposed model was validated using 2020 data collected from 520 European Environmental Agency stations, located in Italy. The performance of the model was assessed using the mean absolute error.
期刊介绍：
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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