L. Enamoto, Andre Rufino Arsenio Santos, Weigang Li, Rodolfo Meneguette, G. P. Rocha Filho
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Meta-learning applied to a multivariate single-step fusion model for greenhouse gas emission forecasting in Brazil
Climate change, driven by greenhouse gas (GHG) emissions, causes extreme weather events, impacting ecosystems, biodiversity, population health, and the economy. Predicting GHG emissions is crucial for mitigating these impacts and planning sustainable policies. This research proposes a novel machine learning model for GHG emission forecasting. Our model, named the meta-learning applied to multivariate single-step fusion model, utilizes historical GHG data from Brazil over the past 60 years. It predicts multivariate time series, meaning it can consider multiple factors simultaneously, leading to more accurate forecasts. Additionally, the model employs two innovative techniques: (i) fusion model aligns different data sources to ensure compatibility and improve prediction accuracy and (ii) meta-learning allows the model to learn from past prediction tasks, generalizing better to new data and reducing the need for large training datasets. Compared to the widely used Bidirectional Long Short-Term Memory (BiLSTM) model, our approach achieves significantly better results. On the same dataset, it reduces the mean absolute percentage error by 116.84% with 95% confidence, demonstrating its superior performance. Furthermore, the model's flexibility allows it to be adapted for predicting other multivariate substances, making it a valuable tool for various environmental studies.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.