Real time image-based air quality forecasts using a 3D-CNN approach with an attention mechanism.

This study presented an image-based deep learning method to improve the recognition of air quality from images and produce accurate multiple horizon forecasts. The proposed model was designed to incorporate a three-dimensional convolutional neural network (3D-CNN) and the gated recurrent unit (GRU) with an attention mechanism. This study included two novelties; (i) the 3D-CNN model structure was built to extract the hidden features of multiple dimensional datasets and recognize the relevant environmental variables. The GRU was fused to extract the temporal features and improve the structure of fully connected layers. (ii) An attention mechanism was incorporated into this hybrid model to adjust the influence of features and avoid random fluctuations in particulate matter values. The feasibility and reliability of the proposed method were verified through the site images of the Shanghai scenery dataset with relevant air quality monitoring data. Results showed that the proposed method has the highest forecasting accuracy over other states of art methods. The proposed model can provide multi-horizon predictions based on efficient feature extraction and good denoising ability, which is helpful in giving reliable early warning guidelines against air pollutants.

Spatiotemporal air quality forecasting and health risk assessment over smart city of NEOM.

Modeling and predicting air pollution concentrations is important to provide early warnings about harmful atmospheric substances. However, uncertainty in the dynamic process and limited information about chemical constituents and emissions sources make air-quality predictions very difficult. This study proposed a novel deep-learning method to extract high levels of abstraction in data and capture spatiotemporal features at hourly and daily time intervals in NEOM City, Saudi Arabia. The proposed method integrated a residual network (ResNet) with the convolutional long short-term memory (ConvLSTM). The ConvLSTM method was boosted by a ResNet model for deeply extracting the spatial features from meteorological and pollutant data and thereby mitigating the loss of feature information. Then, health risk assessment was put forward to evaluate PM10 and PM2.5 risk sensitivity in five districts in NEOM City. Results revealed that the proposed method with effective feature extraction could greatly optimize the accuracy of spatiotemporal air quality forecasts compared to existing state-of-the-art models. For the next hour prediction tasks, the PM10 and PM2.5 of MASE were 9.13 and 13.57, respectively. The proposed method provides an effective solution to improve the prediction of air-pollution concentrations while being portable to other regions around the world.

Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm.

This data in brief presents the monitoring data measured during shield tunnelling of Guangzhou-Shenzhen intercity railway project. The monitoring data includes shield operational parameters, geological conditions, and geometry at the site. The presented data were arbitrarily split into two subsets including the training and testing datasets. The field observations are compared to the forecasting values of the disc cutter life assessed using a hybrid metaheuristic algorithm proposed for "Prediction of disc cutter life during shield tunnelling with artificial intelligent via incorporation of genetic algorithm into GMDH-type neural network" [1]. The presented data can provide a guidance for cutter exchange in shield tunnelling.