Deep learning, hydrological processes and the uniqueness of place
Deep learning, hydrological processes and the uniqueness of place
复制标题
深度学习、水文过程和地方的独特性
作者：
K. Beven
我们从科学史中学到的一件事是，除了科学哲学家所钟爱的一些显著的例外，知识和理解是随着时间的推移而进步的。回顾过去，我们看到对自然世界的理解（大部分）有了进步。有时替代理论需要等待实验的证实；有时，一项新的实验技术会导致重大的理论进步。当然，我们希望看到一些进步，并在我们自己的科学事业的时间尺度上为之做出贡献。因此，当你30多年前写的东西被引用时（在closer et al., 2020），就好像这些评论与今天相关一样，这有点令人不安。即使在水文学领域，情况也应该有所改变。背景是机器学习和深度学习新技术的可用性及其在水文数据中的应用。（2020）表明，自从我在1987年写过水文建模需要一种新范式以来，在许多方面实际上并没有太大变化（Beven, 1987）。他们接着指出，机器学习和深度学习可以产生与概念模型和基于过程的水文模型（包括未测量的集水区）表现一样好的模型，如果不是更好的话（另见Kratzert， Klotz， Shalev等人，2019;Kratzert, Klotz， Herrnegger等人，2019）。这应该被认为令人惊讶吗？对于单个集水区来说不一定是这样——如果集水区数据中存在一致的异常或认知上的不确定性，例如，水平衡约束没有得到很好的满足，那么深度学习（DL）模型可以以受水平衡约束的概念模型无法补偿的方式补偿这些异常。如果在特定流域的水文模型的概念结构与该流域水文过程的性质之间存在一致的异常，那么DL模型可能比缺陷过程描述更好地捕获该行为（尽管值得注意的是，DL模型也受到结构和多个隐藏参数的选择的影响；这就是使它们在拟合训练数据方面具有灵活性的原因）。near et al.（2020）指出，有一些技术可以将守恒约束纳入物理约束的深度学习模型（另见Wang， Zhang, Chang, & Li, 2020），但考虑到水和能量平衡中的认知不确定性，如果观测数据本身不能提供一致的质量和能量平衡闭合，那么这可能不一定有利于获得更好的深度学习预测。事实上，认识到这一点，以及如何应对，可能已经代表了一种进步（例如，Beven， 2019年的讨论）。
One of the things that we learn from the history of science is that, with some notable exceptions beloved of philosophers of science, knowledge and understanding progress over time. Looking back, we see that understanding of the natural world has (mostly) progressed. Sometimes alternative theories have awaited experimental confirmation; sometimes a new experimental technique has led to significant theoretical advances. We hope, of course, to see some of that progression, and to make a contribution to it, over the time scale of our own careers in science. It is therefore somewhat disconcerting to have something you wrote more than 30 years ago cited (in Nearing et al., 2020) as if the comments were relevant today. Things should have changed, even in hydrology. The context is that of the availability of the new techniques of machine learning and deep learning and their application to hydrological data. Nearing et al. (2020) suggest that in many respects not much has actually changed, since I wrote about the need for a new paradigm in hydrological modelling in 1987 (Beven, 1987). They go on to suggest that machine learning and deep learning can produce models that perform just as well, if not better, than conceptual models and process-based hydrological models, including for catchments treated as ungauged (see also Kratzert, Klotz, Shalev et al., 2019; Kratzert, Klotz, Herrnegger, et al., 2019). Should this be considered surprising? Not necessarily in the case of individual catchments—if there are consistent anomalies or epistemic uncertainties in catchment data that mean, for example, that water balance constraints are not well met, then a deep learning (DL) model can compensate for those anomalies in ways that a conceptual model, constrained by water balance cannot. If there are consistent anomalies between the conceptual structure of a hydrological model in a particular catchment and the nature of the hydrological processes in that catchment then again a DL model might well be able to capture that behaviour better than a deficient process description (although it is worth noting that DL models are also subject to choices in structure and multiple hidden parameters; that is what gives them flexibility in fitting the training data). Nearing et al. (2020) point out that there are techniques for incorporating conservation constraints into physically constrained DL models (see also Wang, Zhang, Chang, & Li, 2020), but given the epistemic uncertainties in water and energy balances, then this might not necessarily be advantageous in obtaining better DL predictions if, for example, the observational data do not themselves provide consistent mass and energy balance closure. Indeed, recognizing this, and how to respond to it, might already represent an advance (e.g., the discussion of Beven, 2019).
登录
查看更多内容
影响因子：
6.3
作者：
K. Beven
通讯作者：
K. Beven
影响因子：
3.2
作者：
J. Davies;K. Beven
通讯作者：
J. Davies;K. Beven
年龄排序蓄水关系：水文系统中空间集总流量和水龄的统一描述
影响因子：
5.4
作者：
Harman, C. J.
通讯作者：
Harman, C. J.
影响因子：
8.2
作者：
Beven, Keith J.
通讯作者：
Beven, Keith J.