Machine learning models have proven to be very successful in many fields of research. Yet, in natural resource management, modeling with algorithms such as gradient boosting or artificial neural networks is virtually nonexistent. The current state of research on existing applications of machine learning in the field of social-ecological systems is outlined in a systematic literature review. For this purpose, a short introduction on fundamental concepts of neural network modeling is provided. The data set used, a prototypical case study collection of social-ecological systems—the common–pool resources database from the Ostrom Workshop—is described. I answer the question of whether neural networks are suitable for the kind of data and problems in this field, and whether they or other machine learning algorithms perform better than standard statistical approaches such as regressions. The results indicate a large performance gain. In addition, I identify obstacles for adapting machine learning and provide suggestions on how to overcome them. By using a freely available data set and open source software, and by providing the full code, I hope to enable the community to add machine learning to the existing tool box of statistical methods.

中文翻译：

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将机器学习用于自然资源管理——提高模型性能

机器学习模型已被证明在许多研究领域都非常成功。然而，在自然资源管理中，几乎不存在使用梯度提升或人工神经网络等算法进行建模。系统文献综述概述了机器学习在社会生态系统领域的现有应用的研究现状。为此，简要介绍了神经网络建模的基本概念。描述了所使用的数据集，即社会生态系统的典型案例研究集合——来自 Ostrom Workshop 的公共池资源数据库。我回答神经网络是否适合这个领域的数据类型和问题，以及它们或其他机器学习算法的性能是否优于回归等标准统计方法。结果表明性能提升很大。此外，我还确定了适应机器学习的障碍，并提供有关如何克服这些障碍的建议。通过使用免费的数据集和开源软件，并通过提供完整的代码，我希望能够让社区将机器学习添加到现有的统计方法工具箱中。