Model-Agnostic Meta-Learning for Data Integration Across Heterogeneous Hydrological Datasets

Integrating heterogeneous hydrological datasets remains a significant challenge in environmental modelling due to variations in feature spaces, data distributions, and temporal and spatial scales across sources. This study introduces a Model-Agnostic Meta-Learning (MAML) approach to address the challenge of integrating heterogeneous hydrological datasets, leveraging a collection of datasets compiled from diverse sources. These datasets, characterized by varying features, distributions, and temporal and spatial scales, provide an ideal basis for evaluating MAML's ability to handle real-world data heterogeneity.

MAML’s unique capability to learn shared representations across datasets with minimal feature overlap and significant variability allows it to effectively transfer knowledge between subsets, offering a flexible and scalable solution for integrating hydrological data with diverse characteristics.

The proposed approach trains a base model on one subset of the data while utilizing MAML's meta-learning capabilities to adapt and transfer knowledge to other subsets with differing feature distributions. To test the model's adaptability, we simulate scenarios with varying degrees of feature overlap. Model performance is assessed using metrics such as mean squared error (MSE), both before and after fine-tuning on unseen data subsets.

Preliminary results demonstrate that MAML effectively learns shared representations across datasets, achieving significant improvements in prediction accuracy. Fine-tuning further enhances the model's adaptability, particularly for datasets with minimal feature overlap. These findings highlight MAML's potential as a powerful and flexible tool for integrating and predicting across heterogeneous hydrological datasets.

This study bridges the gap between advanced meta-learning techniques and hydrological applications, providing new insights into scalable and adaptable data integration methods for environmental sciences.

Keywords: Model-Agnostic Meta-Learning, hydrological datasets, data integration, heterogeneous data, meta-learning, environmental modelling, machine learning.