Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation
Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation
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通过归纳方法促进假设生成，推动河流廊道科学超越学科界限
DOI：
10.1002/hyp.14540
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发表时间：
2022
影响因子：
3.2
通讯作者：
Kurz, Marie
中科院分区：
文献类型：
--
作者：
Ward, Adam S.;Packman, Aaron;Bernal, Susana;Brekenfeld, Nicolai;Drummond, Jen;Graham, Emily;Hannah, David M.;Klaar, Megan;Krause, Stefan;Kurz, Marie
河流走廊的统一概念框架需要综合不同地点、方法和学科的研究结果。河流研究界已经发展了大量的观察和特定过程的解释，但我们仍然缺乏一个全面的模型来将这些知识提炼成基本的可转移概念。我们面临的挑战是，一门经典地围绕演绎模型组织的学科如何开始综合过程。演绎模型是系统地收集现场、尺度和机制的具体观察。机器学习特别适合于假设的归纳生成。在这项研究中，我们原型了一种归纳的方法来对河流廊道观测进行整体综合，使用支持向量机回归来识别传统方法不一定会产生的潜在耦合或反馈。这种方法产生了672个关系，链接了一套157个变量，每个变量在五级河流网络的62个位置测量。其中84%的关系以前没有被调查过，代表了潜在的(假设的)过程联系。我们记录了与当前理解一致的关系，包括水文交换过程、微生物生态学和河流连续体概念，支持该方法可以识别数据中的有意义的关系。此外，我们突出了两个新的研究问题的例子，这些问题源于对归纳生成关系的解释。这项研究展示了机器学习的实施，以筛选复杂的数据集，并确定值得进一步研究的一小部分候选关系，包括通常不一起测量的数据类型。这种有条理的方法是对传统调查模式的补充，这些模式往往受到纪律观点的限制，倾向于谨慎地追求简朴。最后，我们强调，这种方法应该被视为对科学发现的更传统的演绎方法的补充，而不是取代。
A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.
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影响因子：
3.3
作者：
Fondi M;Karkman A;Tamminen MV;Bosi E;Virta M;Fani R;Alm E;McInerney JO
通讯作者：
McInerney JO
受损河流：了解历史上人类对河流恢复的影响
DOI：
--
发表时间：
2005
期刊：
影响因子：
--
作者：
E. Wohl
通讯作者：
E. Wohl
影响因子：
5.4
作者：
J. Czuba;Scott R. David;D. Edmonds;A. Ward
通讯作者：
J. Czuba;Scott R. David;D. Edmonds;A. Ward
在网络尺度上测量河流与地下水界面内的生物地球化学：对两种空间采样策略的初步评估
DOI：
10.1002/lom3.10277
发表时间：
2018
期刊：
Limnology and Oceanography: Methods
影响因子：
--
作者：
Lee‐Cullin, J. A.;Zarnetske, J. P.;Ruhala, S. S.;Plont, S.
通讯作者：
Plont, S.
空间统计网络模型在流数据中的应用
DOI：
--
发表时间：
2014
期刊：
影响因子：
--
作者：
D. Isaak;E. Peterson;J. V. Ver Hoef;S. Wenger;Jeffrey A. Falke;C. Torgersen;Colin D. Sowder;E. Steel;M. Fortin;Chris E. Jordan;A. Ruesch;Nicholas A. Som;P. Monestiez
通讯作者：
P. Monestiez