Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction
This work provides an incremental improvement for air pollution management by applying existing optimization methods to deep learning models on a specific dataset.
This research tackled the problem of forecasting Beijing PM2.5 levels by optimizing hyperparameters for three deep learning models (LSTM, CNN, MLP) using Particle Swarm Optimization (PSO), resulting in the PSO-LSTM model achieving the best performance in terms of RMSE and MAPE compared to baselines.
Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models