The dark art of interpretation in geomorphology
The dark art of interpretation in geomorphology
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DOI：
10.1016/j.geomorph.2021.107870
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发表时间：
2021-07-28
期刊：
影响因子：
3.9
通讯作者：
Williams, Richard
中科院分区：
文献类型：
--
作者：
Brierley, Gary;Fryirs, Kirstie;Williams, Richard
解释的过程，以及知识建立在解释之上的方式，在科学和管理方面具有深远的影响。尽管这些问题的重要性，地貌学家通常很少考虑这样的审议。地貌学不是一门线性的、因果关系的科学。固有的复杂性和不确定性促使人们将地貌学的解释过程视为一种令人沮丧的巫术或巫术--一种黑暗的艺术。或者，承认这些挑战是认识到解谜遇到的乐趣，即应用溯因推理来理解物理景观，寻求以可靠的证据基础产生知识。精心设计的解释方法将从遥感数据分析中得出的一般性理解与实地观察/测量和当地知识联系起来，以支持适当结合具体情况的基于地点的应用。在本文中，我们开发了一种认知方法（描述解释预测）来解释景观。解释建立在有意义的描述之上，从而支持可靠的预测，采用多线证据方法。解释将数据转换为知识，以提供支持特定论点的证据。河流地貌学的实例展示了用于分析河流特征、行为和演变的数据-解释-知识序列。虽然大数据和机器学习应用程序在将地貌学转变为数据丰富、预测性越来越强的科学方面具有巨大的潜力，但我们概述了允许规定性和综合性工具进行思考的固有危险，因为解释局部差异是地貌调查的重要因素。皇冠版权（c）2021由Elsevier B. V.发布。这是CC BY许可证下的开放获取文章（http：//creativecommons.org/licenses/by/4.0/）。
The process of interpretation, and the ways in which knowledge builds upon interpretations, has profound implications in scientific and managerial terms. Despite the significance of these issues, geomorphologists typically give scant regard to such deliberations. Geomorphology is not a linear, cause-and-effect science. Inherent complexities and uncertainties prompt perceptions of the process of interpretation in geomorphology as a frustrating form of witchcraft or wizardry - a dark art. Alternatively, acknowledging such challenges recognises the fun to be had in puzzle-solving encounters that apply abductive reasoning to make sense of physical landscapes, seeking to generate knowledge with a reliable evidence base. Carefully crafted approaches to interpretation relate generalised understandings derived from analysis of remotely sensed data with field observations/measurements and local knowledge to support appropriately contextualised place-based applications. In this paper we develop a cognitive approach (Describe-Explain-Predict) to interpret landscapes. Explanation builds upon meaningful description, thereby supporting reliable predictions, in a multiple lines of evidence approach. Interpretation transforms data into knowledge to provide evidence that supports a particular argument. Examples from fluvial geomorphology demonstrate the data-interpretation-knowledge sequence used to analyse river character, behaviour and evolution. Although Big Data and machine learning applications present enormous potential to transform geomorphology into a data-rich, increasingly predictive science, we outline inherent dangers in allowing prescriptive and synthetic tools to do the thinking, as interpreting local differences is an important element of geomorphic enquiry. Crown Copyright (c) 2021 Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).