Retrieval-Augmented Water Level Forecasting for Everglades

TL;DR

This paper introduces a retrieval-augmented framework for water level forecasting in the Everglades, significantly improving accuracy by leveraging historical hydrological data without retraining models.

Contribution

It proposes a novel retrieval-augmented forecasting framework that enhances hydrological predictions by retrieving relevant historical data, addressing generalization issues in deep learning models.

Findings

Substantial accuracy improvements in water level forecasting.

Effective retrieval methods enhance model contextual understanding.

Framework applicable to real-world hydrological data.

Abstract

Accurate water level forecasting is crucial for managing ecosystems such as the Everglades, a subtropical wetland vital for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent advances in deep learning, particularly time series foundation models, have demonstrated success in general-domain forecasting, their application in hydrology remains underexplored. Furthermore, they often struggle to generalize across diverse unseen datasets and domains, due to the lack of effective mechanisms for adaptation. To address this gap, we introduce Retrieval-Augmented Forecasting (RAF) into the hydrology domain, proposing a framework that retrieves historically analogous multivariate hydrological episodes to enrich the model input before forecasting. By maintaining an external archive of past observations, RAF identifies and incorporates relevant patterns from historical data, thereby enhancing contextual awareness and predictive accuracy without requiring the model for task-specific retraining or fine-tuning. Furthermore, we explore and compare both similarity-based and mutual information-based RAF methods. We conduct a comprehensive evaluation on real-world data from the Everglades, demonstrating that the RAF framework yields substantial improvements in water level forecasting accuracy. This study highlights the potential of RAF approaches in environmental hydrology and paves the way for broader adoption of adaptive AI methods by domain experts in ecosystem management. The code and data are available at https://github.com/rahuul2992000/WaterRAF.