Machine learning for ecosystem services
Machine learning for ecosystem services
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用于生态系统服务的机器学习
DOI：
10.1016/j.ecoser.2018.04.004
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发表时间：
2018-10
影响因子：
7.6
通讯作者：
S. Willcock;Javier Martínez-López;D. Hooftman;K. Bagstad;S. Balbi;A. Marzo;Carlo G. Prato;S. Sciandrello;G. Signorello;B. Voigt;F. Villa;J. Bullock;I. Athanasiadis
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文献类型：
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作者：
S. Willcock;Javier Martínez-López;D. Hooftman;K. Bagstad;S. Balbi;A. Marzo;Carlo G. Prato;S. Sciandrello;G. Signorello;B. Voigt;F. Villa;J. Bullock;I. Athanasiadis
机器学习的最新发展扩大了数据驱动建模（DDM）的能力，使人工智能能够通过计算和利用系统内观测变量之间的相关性来推断系统的行为。机器学习算法可以使越来越多的可用“大数据”得以使用，并有助于跨尺度应用生态系统服务模型，分析和预测这些服务向分类受益者的流动。我们使用的Weka和ARIES软件产生两个例子的DDM：在南非的木柴使用和生物多样性价值在西西里，分别。我们的南非的例子表明，DDM（64-91%的准确性）可以确定的地区，其中木柴的使用是在前四分之一与传统的建模技术（54-77%的准确性）相当的准确性。西西里岛的例子突出了如何使DDM更容易为决策者所用，他们表现出参与不确定性信息的能力和意愿。作为DDM过程的一部分，不确定性估计使决策者能够确定他们可以接受的不确定性水平，并利用自己的专业知识做出可能有争议的决策。我们的结论是，DDM在模拟生态系统服务，帮助产生跨学科的模型和复杂的社会生态问题的整体解决方案时发挥了明确的作用。
Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.