用改进的QHAdamW优化神经网络，提升空气质量预测精度。
Enhanced Artificial Neural Networks Using QHAdamW in Air Quality Forecasting
- 结合准双曲动量与解耦权重衰减，改进Adam优化器训练效率。
- 在马尼拉实测数据上，模型误差更低，相关系数接近1，收敛更快。
- 适合环境部门用于颗粒物污染预报，可支撑空气质量管理决策。
本研究采用前馈神经网络结合优化的自适应矩估计（Adam）算法，构建菲律宾首个可用于空气质量指数（AQI）预测的模型。提出的QHAdamW优化器融合了准双曲动量（QHAdam）与解耦权重衰减（AdamW），有效解决传统Adam在收敛性、泛化能力和预测性能上的不足。超参数调优结果显示，0.01和0.001分别为QHAdamW在泛化性能上的最优取值。基于马尼拉实时空气质量监测站数据，模型分别对PM2.5和PM10的AQI进行预测，七项评估指标显示误差范围更低，回归系数接近1，表明模型预测精度显著提升。训练与验证损失均降至较低水平，验证了其良好收敛性。该模型可为菲律宾环境与自然资源部环境监测局（DENR-EMB）提供颗粒物污染预报支持，助力全面空气质量管理。
原文摘要 · Abstract (English)
The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (Adam) algorithm, currently the only AQI forecasting model available in the Philippines. The modified QHAdamW - Quasi-Hyperbolic Momentum (QHAdam) and Adam with decoupled weight decay (AdamW) were both extensions of the Adam optimizer, and both offer unique advantages for training ANN. The proposed QHAdamW optimizer addresses the issues on convergence, generalization, and forecasting performance of Adam. Hyperparameter tuning results revealed that 0.01 and 0.001 were the most effective optimal values for the generalization performance of QHAdamW. The comparative analysis results using seven evaluation metrics revealed that the error value range is lower, and the regression coefficient, having a value approximately equal to 1, improved the model accuracy performance. Likewise, the model converges to a satisfactory level of performance with the convergence performance results of lower loss values as obtained from training and validation losses. Based on data from a real-time air quality tracking station in Manila, a feed-forward neural network is used to predict the AQI of PM2.5 and PM10 separately. This model can be used to forecast Particulate Matter (PM), to help the Department of Environment and Natural Resources-Environmental Monitoring Bureau (DENR-EMB) implement a comprehensive air quality management.
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