Long Short-Term Memory with Encoder-Decoder and Skill Optimization Algorithm based Weather Radar Echo Prediction

This research proposes a novel implementation of Long Short-Term Memory with Encoder-Decoder and Skill Optimization Algorithm (LSTM-ED-SOA) seeking to forecast Weather Radar Echoes. It presents a model that seeks to strengthen ‘the weak’ by introducing an optimization algorithm that combines the advantages of LSTM in handling sequential data and improves the performance of the predictions over time. Temporal dependencies are appropriately modeled with the encoder-decoder structure which is coupled with a skill optimization technique that enables the model to mitigate against capricious weather conditions. Forecasting models such as EPD, TrajGRU, and ConvLSTM as well as, and PredRNN put together show weaknesses when compared to LSTM-ED-SOA. Thus, it comes out convincingly that the model performs exceptionally achieving an accuracy of 99.8%, precision of 99.6%, and an F1 score of 99.7% way ahead of the best conventional models which were achieved accuracy of 85% at the very best. These results underscore the stability and effectiveness of the LSTM-ED-SOA system, for more advanced radars, wherein, it will greatly enhance the accuracy and timeliness of weather forecasting.