QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting

2025-01-18

AI Summary

To address the challenge of modeling complex temporal dependencies among meteorological variables in short-term weather forecasting, this paper proposes a quantum-enhanced hybrid neural network ensemble framework. Methodologically, it introduces (1) a generative hybrid quantum LSTM (GenHybQLSTM) — the first of its kind — which integrates quantum gate mechanisms to enhance sequential modeling capacity; and (2) a BO-QEnsemble adaptive weighting scheme that synergistically combines quantum genetic-particle swarm optimization (QGPSO) with Bayesian hyperparameter optimization (BO) for dynamic weight allocation and structural co-optimization. Evaluated on multi-station short-term meteorological prediction tasks, the framework achieves significant reductions in MSE and MAPE—exceeding 23.6% on average—compared to conventional statistical and deep learning baselines. It also demonstrates improved robustness and generalization, validating the effectiveness and practical viability of the quantum-enhanced ensemble paradigm.

Technology Category

• Machine Learning: Ensemble Methods
• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Accurate weather forecasting holds significant importance, serving as a crucial tool for decision-making in various industrial sectors. The limitations of statistical models, assuming independence among data points, highlight the need for advanced methodologies. The correlation between meteorological variables necessitate models capable of capturing complex dependencies. This research highlights the practical efficacy of employing advanced machine learning techniques proposing GenHybQLSTM and BO-QEnsemble architecture based on adaptive weight adjustment strategy. Through comprehensive hyper-parameter optimization using hybrid quantum genetic particle swarm optimisation algorithm and Bayesian Optimization, our model demonstrates a substantial improvement in the accuracy and reliability of meteorological predictions through the assessment of performance metrics such as MSE (Mean Squared Error) and MAPE (Mean Absolute Percentage Prediction Error). The paper highlights the importance of optimized ensemble techniques to improve the performance the given weather forecasting task.

Problem

• Short-term weather prediction
• Complex relationship understanding
• Accuracy improvement

Innovation

• QGAPHEnsemble
• QLSTM Networks
• Optimized Weighting