Abstract
Weather conditions are closely related to people’s daily life. Accurate weather prediction is conducive to people’s preparation for travel, company’s personnel and vehicle arrangement, disaster prevention and so on. Deep learning methods are confirmed to be helpful to improve the accuracy of weather prediction. In this work, weather prediction based on the hybrid model using 1D CNN (1-Dimensional Convolution) and Bi-LSTM (Bidirectional Long Short-Term Memory) is proposed, which could capture more useful features. Experiment results show that the model combining Bi-LSTM and 1D-CNN algorithm can achieve higher precision of prediction compared with FNN, 1D-CNN, LSTM, Bi-LSTM algorithm. In addition, encoding station ID with One-Hot Encoding is utilized to realize multi-stations weather prediction.
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