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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750947
• 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.

在大量依赖规模依赖性过程中组合和求解方程的数学棘手性和求解方程的数学棘手性挑战，预测对气候变化和人为强迫变化的景观反应的挑战。数值建模需要简化可能不一定代表过程力学的假设，或产生符合在现场获得的客观测量和观察结果的结果。同时，过程地貌学家能够根据他们通过“经验，直觉和默认知识”开发的概念知识对景观变化进行相关评估，但没有能力对其概念知识进行定量正式化和评估的能力，并预测复杂的空间模式。目前不可能在与地理学家，工程师和规划师有关的尺度上整合和数值了解景观变化，或将模型的结果包括在气候模型中，以预测未来的气候和景观变化，无论有或没有人为强迫。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互动，以响应海平面上升和人为强迫的影响，屏障岛的侵害以及目前无法通过这种理解来预测沿海对天然和人为强迫的沿海反应。分析推理（AR）和模糊认知图（FCM）代表了一种新兴的地理计算方法，用于通过与远程感知的图像集成，以形式化，实施和测试景观变化的概念模型。在这个发现赠款期间，我们将使用AR和FCM来预测屏障岛对一系列空间和临时尺度上自然和人为强迫的反应。具体而言，我们将使用AR和FCM来预测：1）近岸，海滩和沙丘形态的变化，以响应近海的测深和框架地质，2）控制前美的发展和恢复的反馈机制，3）恢复海滩和沙丘环境的恢复，在暴风雨和海平面上升高的范围内，遇到山脉的范围，4）在野外岛屿上的范围，4）在野外岛屿上的范围，4）载有河水的河流和境内的河流，并在野外岛屿上升级，并在野外岛屿上升级，而遇到了距离的山脉，并且是在野外岛屿上的崛起，以及无效的范围。对海平面和风暴活动的反应。在这方面，拟议的研究将解决理论和方法论上的缺点，这些缺点阻碍了模型的发展，以量化和预测屏障岛对自然和人为强迫的反应的空间模式，利用我们对障碍岛的地貌学的概念性理解以及与最近的地质技术和地质构成的最新进步相结合的概念性理解。