Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional...Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach.The dataset consists of temperature,pressure,U-wind,V-wind,and precipitation,preprocessed to handle missing values and normal-ized to improve model performance.Performance metrics indicate that BiLSTM outperforms LSTM,achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4%and 6.5%,respectively along with improved threshold scores.It demonstrated better overall prediction accuracy.It also im-proves moderate precipitation detection(TS3.0)by 16.9%compared to LSTM.These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns,making BiLSTM a more effective approach for precipitation forecasting.The findings contribute to the development of im-proved deep learning models for early warning systems and climate risk man-agement.展开更多
文摘Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach.The dataset consists of temperature,pressure,U-wind,V-wind,and precipitation,preprocessed to handle missing values and normal-ized to improve model performance.Performance metrics indicate that BiLSTM outperforms LSTM,achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4%and 6.5%,respectively along with improved threshold scores.It demonstrated better overall prediction accuracy.It also im-proves moderate precipitation detection(TS3.0)by 16.9%compared to LSTM.These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns,making BiLSTM a more effective approach for precipitation forecasting.The findings contribute to the development of im-proved deep learning models for early warning systems and climate risk man-agement.
