We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于 SSA-LSTM 模型的黄河水位预测研究.
- Authors
王 军; 马小越; 张宇航; 崔云烨
- Abstract
The allocation and regulation of water resources in the Yellow River Basin is crucial for the economic development and people's lives of various regions. In order to improve the prediction accuracy of the Yellow River water level, a water resources planning and prediction model of the Yellow River was proposed, which combined the sparse search algorithm (SSA) and the long short-term memory (LSTM) network. In other words, after optimizing the hyper-parameters of LSTM model by SSA algorithm, the Yellow River water level was predicted. The results show that the EMAP(Mean Absolute Percentage Error), ERMS (Root Mean Square Error), EMA (Mean Absolute Error) and R² of SSA-LSTM model are 0.006 3, 0.030 4, 0.024 7 and 0.994 5 respectively. Compared to Multi-Layer Perception (MLP) and LSTM control models, the EMAP, ERMS and EMA of the SSA-LSTM model are significantly reduced, while R² improved. The difficult issue of manual parameter selection in LSTM model is solved by using SSA automatic parameter selection. This method can not only greatly reduce the training time of the model, but also find the optimal network parameters, so as to exercise the best performance of the model. The SSA-LSTM model has good accuracy and robustness in predicting the water level of the Yellow River, which can provide a basis for the regulation of water resources in the Yellow River.
- Publication
Yellow River, 2023, Vol 45, Issue 9, p65
- ISSN
1000-1379
- Publication type
Article
- DOI
10.3969/j.issn.1000-1379.2023.09.011