Adaptive Multi-Source Transfer Learning Approaches for Environmental Challenges

环境分析传感技术的改进大大提高了数据生成的数量和质量。这导致了数据包含的模式的复杂性增加。因此，这种情况需要超越传统统计和物理模型的高级机器学习(ML)方法，以便了解地球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。由于ML算法在各种场景下强大的数据拟合能力，许多环境问题已经开始积极地寻求ML社区的输入，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，以实现海岸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的过去和未来变化。这只是见证AI/ML帮助人们了解自然和应对环境挑战的成功的开始。更多的人仍然很费力，缺乏准确的建模方法。让ML导致环境问题的一个关键障碍是跨地区的数据质量和数量不一致。许多问题都面临这样的困难，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区域获得，尽管可能存在分布差异、特征不匹配等问题。因此，本项目的目的是研究和开发针对此类环境问题的迁移学习方法，该方法可以将来自不同区域(即多源数据领域)的有用知识转移到感兴趣区域(即目标领域)的准确预测模型。要成功地将相关数据领域的知识转移到目标领域，需要解决两个具体的学习挑战：类别失衡和概念漂移。在一些自然事件中，如洪水、地震和热浪，数据分布可能会非常不平衡。这就是所谓的班级不平衡，导致学习者对少数群体事件的概括性很差。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。这被称为概念漂移，会显著降低学习成绩。本项目旨在通过开发先进的目标语方法来解决这两个基本的学习挑战。它们将通过与合作伙伴的密切合作，用于为两个具体的环境问题--早期冰堵预测和多工厂废水流入预测--培训准确的模型。开创性工作将通过四个精心设计的工作包(WPS)进行，每个工作包都针对一个拟议的目标。-WP1：用于类别不平衡数据的TL。-WP2：用于时间漂移数据的TL。-WP3：使用TL预测早期冰塞。-WP4：使用TL预测废水流入。以上将带来创新的解决方案，通过可证明的案例研究，为当前EPSRC的世界级影响目标增加价值。同时，它们将不限于这两种应用。它们有可能惠及广泛的环境问题，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、交通和制造业。该项目将招聘一名PDRA。一些关键活动包括双向研究访问、定期团队会议、研究讲习班和传播活动。

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.