Collaborative Research: CDI-Type II: BirdCast: Novel Machine Learning Methods for Understanding Continent-Scale Bird Migration

合作研究：CDI-Type II：BirdCast：用于理解大陆规模鸟类迁徙的新型机器学习方法

• 批准号: 1125228
• 负责人: Thomas Dietterich
• 金额: $ 98.21万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1125228&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Type; II

An interdisciplinary team of computer scientists, statisticians, and ornithologists will develop novel computer science methods and apply them to the challenge of understanding the annual migration of birds across North America, which is one of the most complex and dynamic natural phenomena on the planet. While direct observation of migrating birds is limited to a handful of birds wearing tracking devices, other sources of data provide partial information about migration that, when appropriately combined, will provide insight into migration at a scale previously unimaginable. These sources include a continent-wide network of volunteer bird watchers, night flight calls captured by a network of acoustic monitoring stations, continent-scale weather patterns gathered by a network of weather stations, and clouds of migrating birds detected at night by WSR-88D weather radar stations. To analyze these data, the team will develop two innovative machine learning techniques-Collective Graphical Models (CGMs) and Semi-Parametric Latent Process Models (SLPMs). The resulting model will be able to identify the complex conditions governing the dynamics of migration behavior including the choice of migratory pathways, the factors that influence when birds migrate, and the speed and duration of each night's movements. CGMs greatly extend the scope of phenomena that can be captured with graphical models. Under suitable conditions, a CGM is able to recover a model of the behavior of individuals using only collective observations.For BirdCast, it will construct a model of individual bird dynamics from the collective observations provided by birders, acoustic and weather stations, and weather radar. Once the model is constructed, it will be applied to live data feeds (bird sightings, acoustic detections, radar detections, and weather forecasts) to predict bird migration in real time. SLPMs are an extension of latent process models, such as the CGM for bird migration, in which the dynamics of a process is represented by latent variables that are observed only indirectly. In an SLPM, the conditional probability distribution of each variable is modeled using flexible, non-parametric methods from machine learning, such as boosted regression trees. Introducing such flexible methods such as CGMs and SLPMs into latent variable models raises difficult challenges for model fitting and validation. Preventing over-fitting will require the creation of novel information regularization and latent model cross-validation methods to enforce latent variable semantics.The proposed work will allow, for the first time, real-time predictions of bird migrations: when they migrate, where they migrate, and how far they will be flying. Accurate models of migration have broad application for basic research by allowing researchers to understand behavioral aspects of migration, how migration timing and pathways respond to variation in climatic conditions, and whether linkages exist between annual variation in migration timing and subsequent inter-annual changes in population size.BirdCast will expand opportunities for the public to participate in the gathering of data and its analysis. The existing data set has more than 60 million observations, and the size is growing exponentially. Last year, volunteers contributed more than 1.3 million hours observing birds. Student engagement in the research is significant as well.

一个由计算机科学家、统计学家和鸟类学家组成的跨学科团队将开发新的计算机科学方法，并将其应用于理解北美鸟类年度迁徙的挑战，这是地球上最复杂、最动态的自然现象之一。虽然对候鸟的直接观察仅限于少数戴着追踪装置的候鸟，但其他数据来源提供了关于迁徙的部分信息，当这些信息适当地结合起来时，将以以前难以想象的规模深入了解迁徙。这些来源包括一个全大陆范围的志愿鸟类观测者网络，由声学监测站网络捕获的夜间飞行叫声，由气象站网络收集的全大陆范围的天气模式，以及由WSR-88D天气雷达站在夜间探测到的候鸟云。为了分析这些数据，该团队将开发两种创新的机器学习技术——集体图形模型（cgm）和半参数潜过程模型（slpm）。由此产生的模型将能够识别控制迁徙行为动态的复杂条件，包括迁徙路径的选择，影响鸟类迁徙的因素，以及每晚迁徙的速度和持续时间。cgm极大地扩展了可以用图形模型捕获的现象的范围。在适当的条件下，CGM能够仅使用集体观察恢复个体行为的模型。对于BirdCast，它将从鸟类、声学和气象站以及气象雷达提供的集体观测中构建单个鸟类动态模型。一旦模型建立，它将被应用于实时数据馈送（鸟类观测、声学探测、雷达探测和天气预报），以实时预测鸟类迁徙。slpm是潜在过程模型的扩展，例如鸟类迁徙的CGM模型，其中过程的动力学由只能间接观察到的潜在变量表示。在SLPM中，每个变量的条件概率分布使用来自机器学习的灵活的非参数方法建模，例如增强回归树。将这种灵活的方法，如cgm和slpm引入潜在变量模型，对模型拟合和验证提出了困难的挑战。防止过度拟合将需要创建新的信息正则化和潜在模型交叉验证方法来强制潜在变量语义。这项提议的工作将首次允许对鸟类迁徙进行实时预测：它们何时迁徙，迁徙地点，以及它们将飞多远。准确的迁移模型在基础研究中有广泛的应用，可以让研究人员了解迁移的行为方面，迁移时间和路径如何响应气候条件的变化，以及迁移时间的年度变化与随后的人口规模年际变化之间是否存在联系。BirdCast将扩大公众参与数据收集和分析的机会。现有的数据集有超过6000万的观测数据，而且其规模还在呈指数级增长。去年，志愿者们观察鸟类的时间超过了130万小时。学生参与研究也很重要。