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首页> 外文期刊>Ecological Modelling >Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition
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Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

机译：使用单目标和多目标回归树和合奏为植被状况的复合指数建模

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An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia-the state of Victoria. The field data were obtained using the 'habitat hectares' approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data. We explore and compare two approaches for modelling this type of data: to learn a model for each score separately (single-target approach, a regression tree), or to learn one model for all scores simultaneously (multi-target approach, a multi-target regression tree). In order to lift the predictive performance, we also employ ensembles (bagging and random forests) of regression trees and multi-target regression trees. Our results demonstrate the advantages of a multi-target over a single-target modelling approach. While there is no statistically significant difference between the multi-target and single-target models in terms of model performance, the multi-target models are smaller and faster to learn than the single-target ones. Ensembles of multi-target models, also, improve the spatial prediction of condition. The usefulness of models of vegetation condition is twofold. First, they provide an enhanced knowledge and understanding of the condition of different indigenous vegetation types, and identify possible biophysical and landscape attributes that may contribute to vegetation decline. Second, these models may be used to map the condition of indigenous vegetation, in support of biodiversity planning, management and investment decisions.
机译：保护和生物多样性规划中的一个重要考虑因素是对生态系统状况或完整性的认识。在这项研究中，我们将各种机器学习方法应用于预测整个澳大利亚东南部东南地区（维多利亚州）剩余的本土植被状况或质量的问题。实地数据是使用“栖息地”方法获得的。这种快速评估技术会产生多个分数，这些分数描述了给定地点的植被各种属性的状况。对多个地点进行了评估，随后将其与GIS和遥感数据联系起来。我们探索并比较了两种用于为此类数据建模的方法：分别为每个得分学习一个模型（单目标方法，回归树），或同时为所有得分学习一个模型（多目标方法，多目标方法目标回归树）。为了提高预测性能，我们还使用了回归树和多目标回归树的合奏（装袋和随机森林）。我们的结果证明了多目标优于单目标建模方法的优点。尽管就模型性能而言，多目标模型与单目标模型之间没有统计学上的显着差异，但多目标模型比单目标模型更小且学习速度更快。多目标模型的集合也改善了条件的空间预测。植被状况模型的有用性是双重的。首先，它们增强了对不同本土植被类型状况的知识和理解，并确定了可能导致植被衰退的生物物理和景观属性。其次，这些模型可用于绘制土著植被状况的地图，以支持生物多样性规划，管理和投资决策。

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