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Using word embedding for environmental violation analysis: Evidence from Pennsylvania unconventional oil and gas compliance reports
With the booming of the unconventional oil and gas industry, its inevitable damage to the environment and human health has attracted public attention.
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network
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.
Tourism Demand Forecasting: An Ensemble Deep Learning Approach
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.
AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting
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.
