Meta-learning applied to a multivariate single-step fusion model for greenhouse gas emission forecasting in Brazil

IF 2.7 4区 环境科学与生态学 Q2 WATER RESOURCES
L. Enamoto, Andre Rufino Arsenio Santos, Weigang Li, Rodolfo Meneguette, G. P. Rocha Filho
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引用次数: 0

Abstract

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.
元学习应用于巴西温室气体排放预测的多变量单步融合模型
由温室气体(GHG)排放引起的气候变化会导致极端天气事件,影响生态系统、生物多样性、人口健康和经济。预测温室气体排放对减轻这些影响和规划可持续政策至关重要。本研究提出了一种用于温室气体排放预测的新型机器学习模型。我们的模型被命名为应用于多元单步融合模型的元学习,它利用了巴西过去 60 年的温室气体历史数据。该模型可预测多变量时间序列,这意味着它可以同时考虑多种因素,从而做出更准确的预测。此外,该模型还采用了两项创新技术:(i) 融合模型将不同的数据源整合在一起,以确保兼容性并提高预测准确性;(ii) 元学习允许模型从过去的预测任务中学习,从而更好地泛化到新数据中,并减少对大型训练数据集的需求。与广泛使用的双向长短期记忆(BiLSTM)模型相比,我们的方法取得了明显更好的效果。在相同的数据集上,它将平均绝对百分比误差降低了 116.84%(置信度为 95%),证明了其卓越的性能。此外,该模型还具有灵活性,可用于预测其他多元物质,是各种环境研究的重要工具。
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来源期刊
CiteScore
4.80
自引率
10.70%
发文量
168
审稿时长
>12 weeks
期刊介绍: Journal of Water and Climate Change publishes refereed research and practitioner papers on all aspects of water science, technology, management and innovation in response to climate change, with emphasis on reduction of energy usage.
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