CAREER: GLOW: Sequencing rivers with machine learning and bioinformatics

河流是景观的动脉，深深植根于世界各地的经济和生态系统。它们是淡水和能源的可靠来源;鱼类和其他野生动物的重要栖息地;运输管道;以及污染和洪水危害的热点。河流的一个基本方面是，它们很少以直线流动，而是蜿蜒通过不同形状的河流弯曲。例如，一些弯曲是对称的，像钟形曲线，而其他弯曲像香蕉一样向一侧倾斜。这些形状在理论和实践中被广泛使用，但基于主观解释，这使得预测河流将如何应对变化的环境的努力变得复杂。这项工作将调整计算方法，如语音识别和DNA测序中使用的方法，以测试河流形状是否可分为几何“字母表”，如果是这样，这些形状如何从上游到下游排序。通过与弗吉尼亚大学音乐作曲和科学教育专家的合作，该项目还将把河流的几何形状转化为音乐，以阐明创造力在研究中的作用。这种艺术与科学的融合将通过地球科学教育者的教材与弗吉尼亚州夏洛茨维尔的当地社区和全国分享。数十个理论模型描述了河流的形状，但主要的知识差距仍然存在于模型和观测之间的复杂河流形状的比较。该研究将通过融合地球科学，机器学习和生物信息学的方法来解决这一需求，以获得一种新的河流语言。这项工作将使用无监督机器学习分析超过100，000个曲流弯曲;将受DNA测序启发的方法应用于河道;并测试弯曲几何形状是否对沉积物，基岩和植被等河岸材料敏感。这个地球科学部-其他世界的地球科学经验教训（GLOW）奖还将研究月球，金星和火星上的大约1,000个通道弯曲，与整体蜿蜒弯曲分类进行比较。教育活动将通过音乐创造一个新的和可访问的科学切入点。这项工作还将支持PI深入参与两个组织-全国黑人地球科学家协会和全国地球科学教师协会-这两个组织处于为该领域建立包容性未来的最前沿。ML在地球科学中的进步。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

Rivers are the arteries of landscapes and are deeply embedded in economies and ecosystems worldwide. They are reliable sources of fresh water and energy; vital habitats for fish and other wildlife; conduits for transportation; and hotspots for hazards from pollution and flooding. A fundamental aspect of rivers is that they rarely flow in straight lines, and instead meander through river bends of different shapes. For example, some bends are symmetric like bell curves, while other bends skew to one side like bananas. These shapes are widely used in both theory and practice but are based on subjective interpretation, which complicates efforts to predict how rivers will respond to a changing environment. This work will adapt computing methods, like those used in speech recognition and in DNA sequencing, to test whether river shapes are divisible into a geometric “alphabet,” and if so, how these shapes are ordered in sequences from upstream to downstream. Through partnerships with experts in music composition and science education at the University of Virginia, the project will also translate the geometry of rivers to music to illuminate the role of creativity in research. This fusion of art and science will be shared with the local community in Charlottesville, VA, and nationally through teaching materials for geoscience educators.Dozens of theoretical models describe the shapes of rivers, yet major knowledge gaps persist in comparing complex river shapes between models and observations. The research will address this need by blending approaches from geoscience, machine learning, and bioinformatics to derive a new language of rivers. The work will analyze over 100,000 meander bends using unsupervised machine learning; apply methods inspired by DNA sequencing to river channels; and test whether bend geometries are sensitive to bank materials such as sediment, bedrock and vegetation. This Division of Earth Sciences – Geoscience Lessons for and from Other Worlds (GLOW) award will also examine roughly 1,000 channel bends on the Moon, Venus and Mars in comparison to the overall meander bend classification. The educational activities will create a new and accessible entry point to science through music. The work will also support the PI’s deepened participation in two organizations – the National Association of Black Geoscientists and National Association of Geoscience Teachers – that are at the forefront of building an inclusive future for the field.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.