We used the collected data to train the algorithm for weather prediction. We used LSTM algorithm which is a type of RNN with more memory capabilities such that the previous data can be used to predict ...

To forecast future weather conditions effectively, it is crucial to include variations in weather patterns from previous years. The proposed approach utilises random forest algorithm and LSTM to ...

This paper focuses on how insurance companies can use Random Forest and LSTM models to make underwriting decisions, model real estate location and identify protected buildings when faced with the ...

QLSTM-Prediction This repository pertains to our investigatory project that delves into the potential applications of implementing a predictive algorithm to forecast the weather, chances of rain in ...

Finally, by combining the corrected NWP wind speeds with real-time wind farm power output data, a KOA-CNN-LSTM-Attention combination prediction model is constructed, which incorporates the KOA ...

Accurate time-keeping is essential for satellite navigation, as even minor time deviations can result in significant positioning errors. Traditional systems rely heavily on ground-based atomic clocks, ...