LIM Laboratory, Faculty of Sciences and Technics Hassan II University of Casablanca, Mohammedia, Morocco.
Higher School of Technology, University Ibn Zohr, Agadir, Morocco.
PLoS One. 2024 Aug 22;19(8):e0307214. doi: 10.1371/journal.pone.0307214. eCollection 2024.
Urbanization and industrialization have led to a significant increase in air pollution, posing a severe environmental and public health threat. Accurate forecasting of air quality is crucial for policymakers to implement effective interventions. This study presents a novel AIoT platform specifically designed for PM2.5 monitoring in Southwestern Morocco. The platform utilizes low-cost sensors to collect air quality data, transmitted via WiFi/3G for analysis and prediction on a central server. We focused on identifying optimal features for PM2.5 prediction using Minimum Redundancy Maximum Relevance (mRMR) and LightGBM Recursive Feature Elimination (LightGBM-RFE) techniques. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters of popular machine learning models for the most accurate PM2.5 concentration forecasts. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). Our results demonstrate that the LightGBM model achieved superior performance in PM2.5 prediction, with a significant reduction in RMSE compared to other evaluated models. This study highlights the potential of AIoT platforms coupled with advanced feature selection and hyperparameter optimization for effective air quality monitoring and forecasting.
城市化和工业化导致了空气污染的显著增加，对环境和公共健康构成了严重威胁。空气质量的准确预测对于政策制定者实施有效的干预措施至关重要。本研究提出了一个专门用于监测摩洛哥西南部 PM2.5 的新型 AIoT 平台。该平台使用低成本传感器收集空气质量数据，通过 WiFi/3G 传输到中央服务器进行分析和预测。我们专注于使用最小冗余最大相关性 (mRMR) 和 LightGBM 递归特征消除 (LightGBM-RFE) 技术来识别 PM2.5 预测的最佳特征。此外，贝叶斯优化被用于微调流行机器学习模型的超参数，以实现最准确的 PM2.5 浓度预测。使用均方根误差 (RMSE)、平均绝对误差 (MAE) 和决定系数 (R2) 评估模型性能。我们的结果表明，LightGBM 模型在 PM2.5 预测方面表现出色，与其他评估模型相比，RMSE 显著降低。这项研究强调了 AIoT 平台与先进的特征选择和超参数优化相结合，用于有效空气质量监测和预测的潜力。
