In the context of global energy shortages, traditional energy sources face issues of limited reserves and high prices. As a result, the importance of energy storage technology is increasingly ...

Objective: To compare the application of the ARIMA model, the Long Short-Term Memory (LSTM) model and the ARIMA-LSTM model in forecasting foodborne disease incidence. Methods: Monthly case data of ...

With the in-depth digital transformation of the global shipping industry, the accurate prediction of smart port operation efficiency has become a key factor in enhancing the competitiveness of ...

Abstract: Crop yield prediction is critical for agricultural insurance, better risk management, and efficient production strategies. In this study, we propose a novel machine-learning framework to ...

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...

Abstract: In this paper, the LSTM model is used to predict the stock prices of the top 10 constituents of CSI 300 index and construct a portfolio. The empirical results show that the LSTM model ...