An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science
An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science
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DOI：
10.1093/biosci/biy069
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发表时间：
2018-09-01
期刊：
影响因子：
10.1
通讯作者：
Vivoni, Enrique R.
中科院分区：
文献类型：
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作者：
Peters, Debra P. C.;Burruss, N. Dylan;Vivoni, Enrique R.
地球是一个复杂的系统，由许多相互作用的空间和时间尺度组成。我们开发了一种跨学科数据模型集成(TDMI)方法来理解、预测和管理这些复杂的动态，重点是时空建模和跨尺度交互。我们的方法采用了以人为中心的机器学习策略，并由数据科学集成系统(DSIS)支持。应用于生态问题，我们的方法集成了关于(A)生物过程，(B)陆地表面模板的空间异质性，以及(C)环境驱动因素的可变性的知识和数据，使用来自多条证据线(抄送，观察，实验操作，分析和数值模型，图像产品，概念模型推理和理论)的数据和知识。我们将这种跨学科的方法应用于一系列日益复杂的生态相关问题，然后讨论信息管理系统将如何演变为决策支持系统，以便在未来解决其他跨学科的问题。
The Earth is a complex system comprising many interacting spatial and temporal scales. We developed a transdisciplinary data-model integration (TDMI) approach to understand, predict; and manage for these complex dynamics that focuses on spatiotemporal modeling and cross-scale interactions. Our approach employs human-centered machine-learning strategies supported by a data science integration system (DSIS). Applied to ecological problems, our approach integrates knowledge and data on (a) biological processes, (b) spatial heterogeneity in the land surface template, and (c) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (cc., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, and theory). We apply this transdisciplinary approach to a suite of increasingly complex ecologically relevant problems and then discuss how information management systems will need to evolve into DSIS to allow other transdisciplinary questions to be addressed in the future.