Analytical Reasoning and Fuzzy Cognitive Maps of Barrier Island Response to Natural and Anthropogenic Forcing

障壁岛对自然和人为强迫响应的分析推理和模糊认知图

• 批准号: RGPIN-2017-04920
• 负责人: Houser, Chris
• 金额: $ 2.84万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653898
• 关键词: Analytical; Reasoning; Fuzzy; Cognitive; Maps

Predicting landscape response to a changing climate and anthropogenic forcing is challenged by the mathematical intractability of combining and solving equations for a large range of scale-dependent processes. Numerical modeling requires simplifying assumptions that may not necessarily be representative of process mechanics, or produce results that conform to objective measurements and observations obtained in the field. At the same time, process geomorphologists are able to make relevant assessments of landscape change based on their conceptual knowledge developed through "experience, intuition and tacit knowledge”, but do not have the ability to quantitatively formalize and evaluate their conceptual knowledge and predict complex spatial patterns. It is currently not possible to integrate and numerically model our conceptual understanding of landscape change at scales relevant to geographers, engineers and planners, or to include the results of the model into climate models to predict future climate and landscape change with or without anthropogenic forcing. For example, through our research we have been able to develop robust conceptual models beach-dune interaction, barrier island transgression in response to sea level rise and the impact of anthropogenic forcing?, it is currently not possible to predict coastal response to natural and anthropogenic forcing using this understanding. Analytical Reasoning (AR) and Fuzzy Cognitive Maps (FCMs) represent an emerging geo-computational approach to formalize, implement, and test conceptual models of landscape change4, through integration with remotely sensed imagery. In this Discovery Grant period, we will use AR and FCMs to predict the response of barrier islands to natural and anthropogenic forcing at a range of spatial and temporal scales. Specifically, we will use AR and FCMs to predict: 1 ) alongshore variation in nearshore, beach and dune morphology, in response to offshore bathymetry and framework geology, 2) feedback mechanisms controlling foredune development and recovery, 3) recovery of beach and dune environments over a range of storm forcing and sea level rise, 4) barrier island transgression and evolution with sea level rise, and 5) impact of anthropogenic forcing on island response to sea level and storm activity. In this respect, the proposed research will address the theoretical and methodological shortcomings that have hindered the development of models to quantify and predict spatial patterns of barrier island response to natural and anthropogenic forcing using our conceptual understanding of barrier island geomorphology and evolution in combination with recent advances in geospatial technologies and geo-computation.

预测景观对气候变化和人为强迫的响应受到数学上的困难的挑战，这些困难需要结合和求解大范围尺度相关过程的方程。数值模拟需要简化假设，这些假设不一定能代表过程力学，也不一定能产生符合现场客观测量和观察结果的结果。与此同时，过程地貌学家能够基于“经验、直觉和隐性知识”形成的概念知识对景观变化进行相关评价，但不具备定量形式化和评价其概念知识和预测复杂空间格局的能力。目前还不可能在与地理学家、工程师和规划师相关的尺度上对我们对景观变化的概念理解进行整合和数值模拟，也不可能将模型的结果纳入气候模型，以预测未来有或没有人为强迫的气候和景观变化。例如，通过我们的研究，我们已经能够开发出强大的概念模型，海滩-沙丘相互作用，堰洲岛海侵响应海平面上升和人为强迫的影响。在美国，目前还不可能利用这一认识来预测沿海地区对自然和人为强迫的反应。分析推理（AR）和模糊认知地图（fcm）代表了一种新兴的地理计算方法，通过与遥感图像的集成来形式化、实施和测试景观变化的概念模型4。在这个发现基金期间，我们将使用AR和fcm来预测堰洲岛在一定时空尺度上对自然和人为强迫的响应。具体来说，我们将使用AR和fcm来预测：1)近岸、海滩和沙丘形态的沿海变化，以响应近海水深和框架地质；2)控制前丘发育和恢复的反馈机制；3)一系列风暴强迫和海平面上升对海滩和沙丘环境的恢复；4)堰洲岛的海侵和演变，以及5)人为强迫对岛屿对海平面和风暴活动的响应的影响。在这方面，建议的研究将利用我们对堰洲岛地貌和演化的概念理解，结合地理空间技术和地理计算的最新进展，解决阻碍模型量化和预测堰洲岛对自然和人为强迫响应的空间格局的理论和方法上的缺陷。