用自适应加权集成模型提升短期天气预报精度
QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting
- 融合量子遗传与粒子群优化的混合网络结构
- 在多个气象数据集上实现更低的均方误差和百分比误差
- 适合需要高精度气象预测的科研与工业应用
准确的天气预报对多个产业决策至关重要。传统统计模型假设数据点独立，难以捕捉气象变量间的复杂依赖关系。本文提出基于自适应权重调整策略的GenHybQLSTM与BO-QEnsemble架构，通过混合量子遗传-粒子群优化算法与贝叶斯优化进行超参数调优，在多项性能指标（如均方误差MSE、平均绝对百分比误差MAPE）上显著提升预测准确性和可靠性，验证了优化集成方法在气象预报任务中的有效性。
原文摘要 · Abstract (English)
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.
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