Artificial Neural Networks: A New Landscape Characterization Toolbox

人工神经网络：新的景观表征工具箱

• 批准号: 0534036
• 负责人: Audrey Huerta
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534036&HistoricalAwards=false
• 关键词: Artificial; Neural; Networks; New; Landscape

This project uses self-organizing maps, a form of artificial neural network techniques, to determine the role of glacial processes in uplift of the Transantarctic Mountains (TAM). The link between erosion and mountain building is known, but difficult to resolve because the processes are dynamically coupled. The TAM offer a unique opportunity to explore this relationship because they have been tectonically quiescent since the late Oligocene, but have undergone various forms of glaciation. Previous investigations of their landscape evolution have been based primarily on field inspection of landforms and surface features, a time intensive and logistically difficult undertaking. While these studies have documented the varying climatic influences on the TAM and the importance of fluvial, glacial, and desert processes in shaping the landscape, we are still left without an accounting of the impact of glacial erosion along the TAM as a whole. This project uses digital elevation models and self-organizing map analysis to provide quantitative metrics that characterize glacial landforms and determine the overall role of glaciation in mountain-building.The broader impacts of this work are improving society's understanding of global climate change by demonstrating links between climate and landform.

该项目使用自组织地图--一种人工神经网络技术--来确定冰川过程在横贯北极山脉隆起中的作用()。侵蚀和造山之间的联系是已知的，但很难解决，因为这两个过程是动态耦合的。提供了一个探索这种关系的独特机会，因为它们自渐新世晚期以来一直处于构造静止期，但经历了各种形式的冰川作用。以前对其地貌演变的调查主要是基于对地貌和地表特征的实地考察，这是一项耗时和后勤困难的工作。虽然这些研究记录了气候对的不同影响，以及河流、冰川和沙漠过程在塑造地貌中的重要性，但我们仍然没有考虑到冰川侵蚀作为一个整体的影响。该项目使用数字高程模型和自组织地图分析来提供描述冰川地貌特征的量化指标，并确定冰川在造山中的总体作用。这项工作的更广泛影响是，通过展示气候和地貌之间的联系，提高社会对全球气候变化的理解。