Shaolong Sun

Dan Bi, Ju-e Guo, Erlong Zhao et al.

With the booming of the unconventional oil and gas industry, its inevitable damage to the environment and human health has attracted public attention. We applied text mining on a total 6057 the type of Environmental Health and Safety compliance reports from 2008 to 2018 lunched by the Department of Environmental Protection in Pennsylvania, USA, to discover the intern mechanism of environmental violations.

Fuxin Jiang, Chengyuan Zhang, Shaolong Sun et al.

For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modelling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.

Shaolong Sun, Yanzhao Li, Ju-e Guo et al.

The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting, but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four countries. The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms benchmark models in terms of level accuracy, directional accuracy and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models. The ensemble deep learning model we propose contributes to tourism forecasting literature and benefits relevant government officials and tourism practitioners.

Shaolong Sun, Dongchuan Yang, Ju-e Guo et al.

Accurate and timely metro passenger flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to propose an efficient and robust forecasting approach due to the inherent randomness and variations of metro passenger flow. In this study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows, and it combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network. The AdaEnsemble learning approach consists of three important stages. The first stage applies VMD to decompose the metro passenger flows data into periodic component, deterministic component and volatility component. Then we employ SARIMA model to forecast the periodic component, LSTM network to learn and forecast deterministic component and MLP network to forecast volatility component. In the last stage, the diverse forecasted components are reconstructed by another MLP network. The empirical results show that our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system and several standard evaluation measures.