Conference Proceeding

Deep learning for construction emission monitoring with low-cost sensor network

Ha, Quang P, Azzi, Merched, Le, Trung H, Nguyen, Huynh AD

2023

Overview

Emissions from construction activities, particularly in metropolitan areas, are carefully monitored to prevent health problems and environmental degradation. The data quality of low-cost wireless sensors in construction sites remains a challenge for pollution predictive models due to uncertainties of measurement and volatile environment. In this study, we propose a hybrid model using a Long short-term memory integrated with a Bayesian neural network to infer the probabilistic forecasts of particulate matters (i.e., PM1.0, PM2.5, and PM10) emitted from construction activities. The training data are fused by two sources: (1) our developed low-cost wireless sensor network (LWSN) monitoring at a construction site located in Melrose Park, Sydney, Australia, and (2) air-quality stations (AQSs) in four suburbs nearby that monitoring site. The proposed model (LSTM-BNN) is compared with other deep learning methods, namely Gated recurrent unit (GRU), Bidirectional long short-term memory (BiLSTM) and One-dimension convolution neural network (1D-CNN), commonly used for time-series forecast. The experimental results indicate the outperformance of our model to all benchmark models and display a significant improvement at 56.3%, 27.9% and 37.9% in MAEs forecast for all three types of particles compared to a deterministic LSTM model.

• Publisher: IAARC Publications
• Subject: Artificial neural networks / Construction sites / Deep learning / Low cost / Machine learning / Mathematical models / Metropolitan areas / Neural networks / Outdoor air quality / Prediction models / Suburban areas / Wireless sensor networks