A Meta-Learning Approach for Adaptive Model Selection in Hydrological Time Series Forecasting

Accurate hydrological prediction faces challenges due to diverse datasets and the absence of universally applicable models. This study investigates meta-learning's role in identifying optimal models for hydrological time series forecasting in specific contexts. We explore limitations in model identification and introduce meta-learning as a solution.

The proposed methodology formalizes the model selection process by using meta-features from a dataset that includes time series, geology, and the experimental configuration and results of predictions . These features are crucial for empowering the meta-learner to make informed model selections.

The study encompasses localized and national analyses, offering insights into the meta-learner's performance across distinct geographic regions.

In this initial phase of the experiment, we present the results of the Meta-Learners within the context of a localized evaluation in Ireland, we evaluated the Neagh Bann River Basin District ( NB RBD) which covers an area of around 5740 km2. It includes all of County Armagh, large parts of Counties Antrim, Londonderry, Down and Tyrone and a small area County Fermanagh in Ireland.  Using the NB RBD dataset which comprises 16 monitoring stations, we constructed the details of 12  prediction models. 

The second phase of the experiment widens its scope by encompassing data from multiple locations across Ireland. This broader approach allows us to draw upon a more extensive and diverse dataset, comprehensively evaluating meta-learner performance within a national context. By incorporating data from various regions in Ireland, we aim to capture a more holistic understanding of the meta-learners' adaptability and effectiveness in a nationally diverse landscape.  We used data from 249 hydrometric stations in Ireland, each corresponding to a distinct geographical location. This comprehensive dataset encompasses various locations across the country, offering a rich and diverse perspective for our analysis.

Evaluation results demonstrate the performance of 12 models. Models like Random Forest and K-Nearest Neighbors show promise, while Support Vector Machine struggles consistently. Addressing class imbalance through resampling techniques proves effective, underscoring the importance of tailored model selection strategies.

Key findings highlight model performance variability, concerns about overfitting, and the significance of appropriate resampling strategies. The meta-learner's application showcases its value in leveraging strengths across classifiers and mitigating weaknesses.

In conclusion, this study contributes to advancing adaptive model selection methodologies in hydrological time series forecasting. 

Keywords: Meta-learning, model selection, hydrologic time-series prediction.