Data Science of the Natural Environment

自然环境数据科学

基本信息

批准号：
EP/R01860X/1
负责人：
David Leslie
金额：
$ 338.48万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR01860X%2F1
关键词：
Data Science Natural Environment

项目摘要

We will develop a data science of the natural environment, deploying modern machine learning and statistical techniques to enable better-informed decision-making as our climate changes. While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change. This project brings together world-leading statisticians, computer scientists and environmental scientists alongside an extensive array of key public and private stakeholder organisations to effect a step change in data culture in the environmental sciences.The project will develop a new approach to data science of the natural environment driven by three representative grand challenges of environmental science: predicting ice sheet melt, modelling and mitigating poor air quality, and managing land use for maximal societal benefit. In each motivational challenge, there is already an extensive scientific expertise, with intricate models of processes at multiple scales. However this sophisticated modelling of system components is usually let down by naive integration of these components together, and inadequate calibration to observed data. The consequence is poor predictions with a high level of uncertainty and hence poorly-informed policy making. As new forms of environmental data become available, and the pressures on our natural environment from climate change increase, this gap is becoming a pressing concern, and we bring an impressive team to bear on the problem.A key theme of the project is integration, developing a suite of novel data science tools which work together in a modular fashion, and with existing scientifically-informed process models. By building a team that spans the inter-disciplinary divisions between data and environmental scientists we can ensure the necessary interoperability of methods that is currently lacking. Working with the full range of stakeholder environmental organisations will enable continual co-design of the programme and training of end-user scientists to ensure a reduction of the skills gap in this area. The resultant culture shift in the data literacy of the environmental sciences will enable better decision-making as climate change places ever greater strains on our society.

我们将开发自然环境的数据科学，部署现代机器学习和统计技术，以便在气候变化时做出更明智的决策。尽管数据科学研究的爆炸式增长推动了电子商务和营销、智慧城市、物流和运输、健康和福祉等领域的巨大进步，但这些工具尚未充分部署来解决人类面临的最紧迫的问题之一，即减缓和适应气候变化。该项目汇集了世界领先的统计学家、计算机科学家和环境科学家以及广泛的主要公共和私人利益相关者组织，以实现环境科学数据文化的重大变革。该项目将开发一种新的数据科学方法自然环境受到环境科学三大代表性重大挑战的驱动：预测冰盖融化、建模和缓解不良空气质量以及管理土地利用以实现最大社会效益。在每一个激励挑战中，都已经具备了广泛的科学专业知识，以及多个尺度的复杂过程模型。然而，这种复杂的系统组件建模通常会因为这些组件的简单集成以及对观测数据的校准不足而失败。其后果是预测不佳，不确定性很高，从而导致政策制定缺乏信息。随着新形式的环境数据的出现，以及气候变化对我们自然环境的压力的增加，这种差距正成为一个紧迫的问题，我们派出了一支令人印象深刻的团队来解决这个问题。该项目的一个关键主题是整合，开发一套新颖的数据科学工具，这些工具以模块化方式协同工作，并与现有的科学信息流程模型一起工作。通过建立一个跨越数据和环境科学家之间跨学科部门的团队，我们可以确保目前缺乏的方法的必要互操作性。与各种利益相关者环境组织合作将能够持续共同设计该计划并培训最终用户科学家，以确保缩小该领域的技能差距。随着气候变化给我们的社会带来越来越大的压力，环境科学数据素养方面的文化转变将有助于做出更好的决策。