Adaptive Multi-Source Transfer Learning Approaches for Environmental Challenges

应对环境挑战的自适应多源迁移学习方法

• 批准号: EP/Y002539/1
• 负责人: Shuo Wang
• 金额: $ 20.97万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY002539%2F1
• 关键词: Adaptive; Multi; Source; Transfer; Learning

Improvements in environmental analytical sensing technologies has led to a dramatic increase in the quantity and quality of data generation. This has resulted in an increased complexity of patterns that the data contain. This situation thus demands advanced machine learning (ML) approaches beyond traditional statistical and physical models, in order to understand how the Earth's climate and ecosystem have been changing and how they are being impacted by human behaviours. Many environmental problems have begun actively seeking input from the ML community, due to the powerful data fitting abilities of ML algorithms in various scenarios without human intervention. For example, the Plymouth Marine Laboratory has recently been developing data-driven approaches to automate coastal observation and marine management. It effectively lowers the cost of environmental observation and records past and future change in the ocean climate at an unprecedented scale. This is only the beginning of witnessing the success of AI/ML to help people understand nature and tackle environmental challenges. Many more are still laborious and lack of accurate modelling approaches. One key obstacle of having ML contribute to environmental problems is the inconsistent data quality and quantity across regions. Many problems suffer the difficulty that, the data from the region of interest is insufficient for building an accurate learner. However, relevant data can be available from other regions, although there may exist distribution differences, feature mismatches, etc. This project is thus motivated to study and develop transfer learning (TL) approaches for such environmental problems, which can transfer the useful knowledge from various regions (i.e. multi-source data domains) to build an accurate predictive model for the region of interest (i.e. the target domain).To successfully transfer knowledge from related data domains to the target domain, two specific learning challenges need to be addressed: class imbalance and concept drift. The data distribution can be very skewed in some natural events, such as flooding, earthquakes and heatwaves. This is called class imbalance and leads to poor generalization of a learner on the minority events. Environmental data is often collected over time, so that distribution changes in data may happen at some point. This is called concept drift and can deteriorate the learning performance significantly.This project aims to tackle these two fundamental learning challenges by developing advanced TL approaches. They will be used to train accurate models for two concrete environmental problems - early ice jam prediction and multi-plant wastewater inflow prediction, through close collaboration with the partner. Pioneering work will be conducted through four carefully designed work packages (WPs), each of which aims at one proposed objective. - WP1: TL for class imbalanced data.- WP2: TL for time drifting data.- WP3: Early ice jam prediction using TL.- WP4: Wastewater inflow prediction using TL. The above will lead to innovative solutions that add values to the current EPSRC's world-class impact targets with demonstrable case studies. In the meantime, they will not be limited to these two applications. They have the potential to benefit a wide range of environmental problems, such as climate pattern discovery and flood risk estimation, and even other fields, such as agricultural planning, transportation and manufacturing. This project will recruit one PDRA. Some key activities include two-way research visits, regular team meetings, research workshops and dissemination activities.

环境分析传感技术的改进导致数据生成的数量和质量大幅提高。这导致了数据所包含的模式的复杂性增加。因此，这种情况需要先进的机器学习（ML）方法超越传统的统计和物理模型，以了解地球的气候和生态系统如何变化以及它们如何受到人类行为的影响。许多环境问题已经开始积极寻求ML社区的输入，这是由于ML算法在各种场景中的强大数据拟合能力，而无需人工干预。例如，普利茅斯海洋实验室最近一直在开发数据驱动的方法，以实现沿海观测和海洋管理的自动化。它有效地降低了环境观测的成本，并以前所未有的规模记录了海洋气候过去和未来的变化。这只是见证AI/ML成功帮助人们了解自然和应对环境挑战的开始。更多的仍然是费力的，缺乏准确的建模方法。让ML有助于解决环境问题的一个关键障碍是各地区数据质量和数量不一致。许多问题都存在这样的困难，即来自感兴趣区域的数据不足以构建准确的学习器。然而，相关数据可以从其他地区获得，尽管可能存在分布差异，特征不匹配等。因此，本项目的动机是研究和开发针对此类环境问题的迁移学习（TL）方法，它可以将来自不同地区的有用知识（即多源数据域）来构建感兴趣区域的准确预测模型（即目标领域）。为了成功地将知识从相关数据领域转移到目标领域，需要解决两个具体的学习挑战：类别不平衡和概念漂移。在一些自然事件中，数据分布可能非常偏斜，例如洪水，地震和热浪。这就是所谓的类不平衡，导致学习者对少数事件的概括能力差。环境数据通常是随着时间的推移而收集的，因此数据的分布可能会在某个时候发生变化。这个项目旨在通过开发先进的TL方法来解决这两个基本的学习挑战。通过与合作伙伴的密切合作，它们将用于训练两个具体环境问题的精确模型-早期冰塞预测和多工厂废水流入预测。将通过四个精心设计的工作包（WP）开展开创性工作，每个工作包都针对一个拟议目标。- WP 1：TL用于类不平衡数据。WP 2：TL用于时间漂移数据。WP 3：使用TL进行早期冰塞预测。WP 4：使用TL进行废水流入预测。以上将导致创新的解决方案，增加价值，目前EPSRC的世界级的影响目标与可证明的案例研究。在此期间，它们将不仅限于这两个应用程序。它们有可能造福于广泛的环境问题，如气候模式发现和洪水风险估计，甚至其他领域，如农业规划，运输和制造业。本项目将招募一名PDRA。一些主要活动包括双向研究访问、定期小组会议、研究讲习班和传播活动。