AI 中文总结
该研究提出QHAdamW优化器结合ANN，针对菲律宾现有AQI预测模型的收敛、泛化等问题改进，经马尼拉实测数据验证，可用于PM2.5、PM10的AQI预测，助力环境管理。
AI 中文摘要
本研究采用人工神经网络（ANN）结合优化的自适应矩估计（Adam）算法，是菲律宾目前唯一可用的空气质量指数（AQI）预测模型。改进的QHAdamW——准双曲动量（QHAdam）与带解耦权重衰减的Adam（AdamW），均为Adam优化器的扩展，在训练ANN时各有独特优势。所提出的QHAdamW优化器解决了Adam在收敛性、泛化性及预测性能方面的问题。超参数调优结果显示，0.01和0.001是对QHAdamW泛化性能最有效的最优值。使用7种评估指标的对比分析结果表明，误差值范围更低，回归系数约等于1，提升了模型的准确率；同时，训练与验证损失得到的收敛性能结果显示，模型收敛至令人满意的性能水平。基于马尼拉实时空气质量监测站的数据，采用前馈神经网络分别预测PM2.5和PM10的AQI，该模型可用于预测颗粒物（PM），以协助环境与自然资源部环境监测局（DENR-EMB）实施全面的空气质量管理。
英文摘要
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.
Comments20 pages, 16 tables, 15 figures, Published with International Journal of Engineering Trends and Technology (IJETT)
Journal refInternational Journal of Engineering Trends and Technology, 74(7), 352-371
DOI：10.14445/22315381/IJETT-V74I7P122
