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: 1125098
• 负责人: Steven Kelling
• 金额: $ 121.79万
• 依托单位: Cornell 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 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1125098&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颖的计算机科学方法，并将其应用于理解北美鸟类年度迁徙的挑战，这是地球上最复杂和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佩戴跟踪设备的鸟类，但其他数据来源提供了有关迁徙的部分信息，如果适当结合，将以前所未有的规模深入了解迁徙。这些来源包括全大陆范围的志愿鸟类观察者网络，声学监测站网络捕获的夜间飞行呼叫，气象站网络收集的大陆尺度天气模式，以及WSR-88 D气象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（CGMs）和半参数潜在过程模型（SLPMs）。由此产生的模型将能够识别管理迁移行为动态的复杂条件，包括迁移路径的选择，影响鸟类迁移的因素以及每晚运动的速度和持续时间。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能够仅使用集体观测来恢复个体行为的模型。对于BirdCast，它将根据观鸟者、声学和气象站以及天气雷达提供的集体观测来构建个体鸟类动力学模型。一旦模型构建完成，它将被应用于实时数据（鸟类目击、声学探测、雷达探测和天气预报），以真实的时间预测鸟类迁徙。SLPM是潜过程模型的扩展，例如鸟类迁徙的CGM，其中过程的动态由仅间接观察的潜变量表示。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，例如提升回归树。将CGM和SLPM等灵活的方法引入潜变量模型，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，以执行潜在变量Semantics.The拟议的工作将首次允许实时预测鸟类迁徙：它们何时迁徙，在哪里迁徙，以及它们将飞多远。准确的迁移模型可广泛应用于基础研究，使研究人员了解迁移的行为方面，迁移时间和路径如何对气候条件的变化作出反应，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。 现有的数据集有超过6000万个观测结果，而且规模正在呈指数级增长。去年，志愿者贡献了130多万小时观察鸟类。 学生参与研究也很重要。