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A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction
https://orcid.org/0000-0001-7667-4093
Accurate and reliable runoff prediction is essential for the efficient operation of hydropower systems. This paper presented a runoff probability prediction model that utilizes an enhanced long short-term memory (LSTM) network. The model incorporates a combination of a long and short-term memory network, a quantile regression module and an interval correction module. The proposed model utilizes the LSTM network to effectively capture the time-series characteristics of the runoff data. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Furthermore, the inclusion of an interval correction module helps refine the prediction results, leading to improved accuracy and a narrower prediction interval. The integration of these three modules greatly enhances the precision of the predictions and brings the probability estimates closer to the true distribution. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Jinsha River and Lancang River were selected to evaluate the performance of the model because of the availability of long-term reliable data, geographical representation, and socioeconomic importance. The prediction results demonstrate superior performance compared with other existing models. Moreover, the model enables obtaining probabilistic predictions with appropriate prediction intervals and high reliability.
A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction
https://orcid.org/0000-0001-7667-4093
Accurate and reliable runoff prediction is essential for the efficient operation of hydropower systems. This paper presented a runoff probability prediction model that utilizes an enhanced long short-term memory (LSTM) network. The model incorporates a combination of a long and short-term memory network, a quantile regression module and an interval correction module. The proposed model utilizes the LSTM network to effectively capture the time-series characteristics of the runoff data. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Furthermore, the inclusion of an interval correction module helps refine the prediction results, leading to improved accuracy and a narrower prediction interval. The integration of these three modules greatly enhances the precision of the predictions and brings the probability estimates closer to the true distribution. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Jinsha River and Lancang River were selected to evaluate the performance of the model because of the availability of long-term reliable data, geographical representation, and socioeconomic importance. The prediction results demonstrate superior performance compared with other existing models. Moreover, the model enables obtaining probabilistic predictions with appropriate prediction intervals and high reliability.
A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction
https://orcid.org/0000-0001-7667-4093
Accurate and reliable runoff prediction is essential for the efficient operation of hydropower systems. This paper presented a runoff probability prediction model that utilizes an enhanced long short-term memory (LSTM) network. The model incorporates a combination of a long and short-term memory network, a quantile regression module and an interval correction module. The proposed model utilizes the LSTM network to effectively capture the time-series characteristics of the runoff data. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Furthermore, the inclusion of an interval correction module helps refine the prediction results, leading to improved accuracy and a narrower prediction interval. The integration of these three modules greatly enhances the precision of the predictions and brings the probability estimates closer to the true distribution. By incorporating the quantile regression module, the model allows for probability predictions without the need for prior assumptions. Jinsha River and Lancang River were selected to evaluate the performance of the model because of the availability of long-term reliable data, geographical representation, and socioeconomic importance. The prediction results demonstrate superior performance compared with other existing models. Moreover, the model enables obtaining probabilistic predictions with appropriate prediction intervals and high reliability.
A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction
J. Hydrol. Eng.
Zhu, Shuang (author) / Zhang, Maoyu (author) / Wang, Chao (author) / Guo, Jun (author) / Chen, Xudong (author) / Xie, Mengfei (author)
2024-08-01
Article (Journal)
Electronic Resource
English
Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation
| DOAJ | 2020
| Elsevier | 2024