轻量XGBoost模型实现低资源环境下的高精度空气质量预测
Lightweight ML-Based Air Quality Prediction for IoT and Embedded Applications
- 用轻量版XGBoost替代完整模型，兼顾预测精度与计算效率
- 轻量模型推理时间与内存占用大幅降低，适合嵌入式设备部署
- 在物联网场景中实现实时空气质量监测，兼顾性能与资源约束
本研究评估了两种XGBoost回归模型（全容量与轻量版）在城市环境一年采集的AirQualityUCI数据集上预测一氧化碳（CO）和二氧化氮（NO2）浓度的性能。基于平均绝对误差（MAE）、均方根误差（RMSE）、平均偏差误差（MBE）和决定系数（R²）等指标进行综合评价，并考察了推理时间、模型大小和峰值内存使用等资源指标。全容量模型对两种污染物预测精度更高，而轻量模型虽略低但显著降低推理时间和存储需求。结果表明，简化模型可在不牺牲预测质量的前提下，在资源受限环境中可靠部署，适用于物联网与嵌入式应用中的实时空气质量监测。
原文摘要 · Abstract (English)
This study investigates the effectiveness and efficiency of two variants of the XGBoost regression model, the full-capacity and lightweight (tiny) versions, for predicting the concentrations of carbon monoxide (CO) and nitrogen dioxide (NO2). Using the AirQualityUCI dataset collected over one year in an urban environment, we conducted a comprehensive evaluation based on widely accepted metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), and the coefficient of determination (R2). In addition, we assessed resource-oriented metrics such as inference time, model size, and peak RAM usage. The full XGBoost model achieved superior predictive accuracy for both pollutants, while the tiny model, though slightly less precise, offered substantial computational benefits with significantly reduced inference time and model storage requirements. These results demonstrate the feasibility of deploying simplified models in resource-constrained environments without compromising predictive quality. This makes the tiny XGBoost model suitable for real-time air-quality monitoring in IoT and embedded applications.
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