Reducing uncertainty in ecosystem service modelling through weighted ensembles
Reducing uncertainty in ecosystem service modelling through weighted ensembles
复制标题
DOI：
10.1016/j.ecoser.2021.101398
复制
发表时间：
2022-02-01
影响因子：
7.6
通讯作者：
Willcock, Simon
中科院分区：
文献类型：
--
作者：
Hooftman, Danny A. P.;Bullock, James M.;Willcock, Simon
在过去的十年中，许多生态系统服务（ES）模型已经开发，为可持续的土地和水资源利用规划提供信息。然而，任何单一模型在任何特定情况下的预测的不确定性都可能破坏其决策效用。一种解决方案是创建集合预测，这可能会提高准确性，但如何最好地创建ES集合以减少不确定性是未知的，未经测试。使用10个模型的碳储存和9个供水，我们测试了一系列的合奏方法对测量验证数据在英国。集成的准确性至少比随机选择的单个模型高5-17%，一般来说，在模型共识中加权的集成比未加权的集成提供更好的预测。为了支持可持续发展的稳健决策并减少这些决策的不确定性，我们的分析表明，应根据数据质量（例如，如果有验证数据）应用各种集成方法。
Over the last decade many ecosystem service (ES) models have been developed to inform sustainable land and water use planning. However, uncertainty in the predictions of any single model in any specific situation can undermine their utility for decision-making. One solution is creating ensemble predictions, which potentially increase accuracy, but how best to create ES ensembles to reduce uncertainty is unknown and untested. Using ten models for carbon storage and nine for water supply, we tested a series of ensemble approaches against measured validation data in the UK. Ensembles had at minimum a 5-17% higher accuracy than a randomly selected individual model and, in general, ensembles weighted for among model consensus provided better predictions than unweighted ensembles. To support robust decision-making for sustainable development and reducing uncertainty around these decisions, our analysis suggests various ensemble methods should be applied depending on data quality, for example if validation data are available.