ForestAdvisor:基于碳排放的多模式森林决策系统

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

通过区域森林管理有效平衡碳减排与经济可行性是全球生态系统面临的一项重大挑战。本文介绍了一种创新的多模式森林决策系统,该系统集成了深度学习和自然语言处理技术,旨在优化森林管理策略。该系统在三个不同的森林地区进行了实验验证。利用深度学习模型,该系统分析并预测了每日碳排放数据。实验结果表明,该模型在所有三个地区的数据集上的判定系数(R2)分别高达 0.94、0.98 和 0.99,准确度极高,因此可以用于预测未来几个月的碳排放趋势。随后,该系统利用自然语言处理来评估各种收集的森林管理策略的重要性。最后,该系统根据预测的碳排放趋势对这些策略组合进行微调,确保在应对碳排放波动的复杂动态时具有灵活性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions

Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was conducted in three distinct forested regions. Utilizing a deep learning model, the system analyzed and predicted daily carbon emissions data. The experiments demonstrated remarkable accuracy, with the model achieving a coefficient of determination (R2) of up to 0.94, 0.98, and 0.99 across datasets from all three regions, thereby justifying its use for forecasting carbon emission trends over the following months. Subsequently, the system employed natural language processing to assess the importance of various collected forest management strategies. Finally, the system fine-tuned these strategy combinations in response to the predicted carbon emission trends, ensuring flexibility and effectiveness in addressing the complex dynamics of carbon emission fluctuations.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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