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.

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，并将其应用于了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。虽然直接观察迁移的鸟类仅限于少数戴着跟踪设备的鸟类，但其他数据来源提供了有关迁移的部分信息，这些信息在适当地组合后会以先前无法想象的规模洞悉迁移。这些资料包括由志愿鸟类观察者组成的整个大陆范围内的网络，由声学监测站网络捕获的夜间飞行电话，由天气站网络收集的大陆规模的天气模式以及WSR-88D Weather Radar Stations在夜间检测到的鸟类云。为了分析这些数据，团队将开发两种创新的机器学习技术 - 美感图形模型（CGM）和半参数潜在过程模型（SLPMS）。最终的模型将能够确定迁移行为动态的复杂条件，包括选择迁移途径，鸟类迁移时影响的因素以及每晚运动的速度和持续时间。 CGM大大扩展了可以使用图形模型捕获的现象范围。在适当的条件下，CGM能够仅使用集体观察结果恢复个体的行为模型。对于小鸟播，它将从观鸟者，声学和气象站以及天气雷达的集体观察中构建单个鸟动力学模型。构建模型后，它将应用于实时数据供稿（鸟类目击，声学探测，雷达检测和天气预报），以实时预测鸟类的迁移。 SLPM是潜在过程模型的扩展，例如用于鸟类迁移的CGM，其中一个过程的动力学由仅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都是使用机器学习的灵活的非参数方法（例如增强的回归树）建模的。将这种灵活的方法（例如CGM和SLPM）引入潜在变量模型为模型拟合和验证带来了困难的挑战。防止过度拟合将需要创建新型信息正则化和潜在模型交叉验证方法来强制执行潜在的可变语义。拟议的工作将首次允许对鸟类迁移的实时预测：当它们迁移，迁移的地方以及它们会飞行的程度。准确的迁移模型通过允许研究人员了解迁移的行为方面，移民时机和途径如何应对气候条件的变化以及迁移时机的年变化与随后的年龄跨度变化之间的联系以及人口规模的年度变化之间是否存在联系。鸟类群会扩大公众参与数据的集合和分析。 现有的数据集具有超过6000万的观察结果，并且规模呈指数增长。去年，志愿者贡献了超过130万小时的观察鸟类。 学生参与研究也很重要。