通过深度强化学习进行连接保护规划

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-02-22 DOI:10.1111/2041-210X.14300
Julián Equihua, Michael Beckmann, Ralf Seppelt
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引用次数: 0

摘要

联合国已宣布 2021-2030 年为 "生态系统恢复十年",目的是预防、阻止和扭转世界生态系统的退化,而这种退化往往是由自然景观的破碎化造成的。人类活动将栖息地分割和包围,使它们变得太小,无法维持可生存的动物种群,或者相隔太远,无法进行觅食和基因流动。尽管需要制定战略来解决破碎化问题,但如何有效地重新连接自然仍是一个未知数。在本文中,我们阐述了深度强化学习(DRL)在解决连通性保护规划的空间优化方面的潜力。空间优化问题的复杂性会随着输入变量及其状态的数量而急剧增加,这已经成为并将继续成为空间优化问题最严重的障碍之一。DRL 是一类新兴的方法,主要用于训练深度神经网络以解决决策任务,并已被用于学习复杂优化问题的良好启发式方法。虽然 DRL 在优化保护决策方面潜力巨大,但其应用实例却寥寥无几。我们将 DRL 应用于两个真实世界的栅格数据集,以基于图的连通性指数为优化目标,进行连通性规划。我们表明,DRL 在一个小例子中收敛到了已知的最佳值,该例子的目标是整体改善连通性积分指数,唯一的约束条件是预算。我们还表明,在一个大型实例中,DRL 近似于高质量解决方案,该实例具有额外的成本和空间配置约束,目标是更复杂的连接性概率指数。据我们所知,目前还没有软件能针对这一指数对如此规模的栅格数据进行优化。在复杂的空间优化问题中,DRL 可用于近似求得良好的解决方案,即使保护特征是非线性的,如基于图形的指数。此外,我们的方法将优化过程与指数计算分离开来,因此它有可能针对当前或未来软件中实施的任何其他保护特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Connectivity conservation planning through deep reinforcement learning

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来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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