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Sampling strategies based on sets of behavioural parameters. Application to a simple pesticide fate model

机译:基于行为参数集的采样策略。在简单农药归宿模型中的应用

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Sampling strategies are necessary to minimise the cost of data acquisition. We consider the case when several samples must be analysed at the same time, knowing rainfall data, optionally discharge data. We assume also that a rainfall-runoff and pesticide transfer model is available. Three methods are presented based on behavioural sets calculated on prior events, which provide optimal sampling times with respect to different statistical criteria, for a fixed number of samples. A methodology to compare these strategies, adapted to a context of data scarcity is proposed for proper validation. We consider that the combination of a model and a strategy is efficient if it does better than a strategy without a model based on equal volumes. Even with a simple and relatively poor model, it can be concluded that the different strategies are efficient and show similar efficiency in spite of their different levels of conceptual and algorithmic complexity.
机译:采样策略对于最小化数据采集成本是必要的。我们考虑以下情况:必须同时分析多个样品,了解降雨数据,还可以选择排放数据。我们还假设有降雨径流和农药转移模型。基于在先前事件上计算出的行为集,提出了三种方法,它们针对固定数量的样本提供了针对不同统计标准的最佳采样时间。提出了一种方法来比较这些策略,以适应数据短缺的情况,以进行正确的验证。我们认为,如果模型和策略的组合比没有基于等量模型的策略要好,则将模型和策略组合是有效的。即使使用简单且相对较差的模型,也可以得出结论,尽管概念和算法复杂度不同,但不同的策略仍然有效且显示出相似的效率。

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