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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687280
• 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难以组合和求解大范围尺度依赖过程方程的挑战。数值建模需要简化假设，这些假设可能不一定代表过程力学，也不一定产生符合现场获得的客观测量和观察的结果。与此同时，过程地貌学家能够作出相关的景观变化评估的基础上，通过“经验，直觉和隐性知识”发展的概念知识，但没有能力定量形式化和评估他们的概念知识和预测复杂的空间格局。目前还不可能在地理学家、工程师和规划者相关的尺度上整合和数值模拟我们对景观变化的概念性理解，也不可能将模型的结果纳入气候模型，以预测未来的气候和景观变化，无论是否有人为强迫。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，屏障岛海侵响应海平面上升和人为强迫的影响，目前不可能利用这种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，通过与遥感图像集成，形式化，实施和测试景观变化的概念模型。在本次发现资助期间，我们将使用AR和FCM来预测屏障岛在一系列空间和时间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）响应于近海水深测量和框架地质学的近岸、海滩和沙丘形态的沿岸变化，2）控制前沙丘发育和恢复的反馈机制，3）在风暴强迫和海平面上升的范围内海滩和沙丘环境的恢复，4）障壁岛海侵和随海平面上升的演化，（5）人为强迫对岛屿对海平面和风暴活动响应的影响。 在这方面，拟议的研究将解决的理论和方法上的缺陷，阻碍了模型的发展，量化和预测的空间格局的障碍岛响应自然和人为强迫使用我们的概念理解障碍岛地貌和演变结合地理空间技术和地理计算的最新进展。