An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input
Shanghai Ocean University · China Institute of Water Resources and Hydropower Research · +2 more institutions
Abstract
Accurate prediction of river runoff is of great significance for water resources management, flood prevention and mitigation. The causes of runoff are complex and the mechanisms behind them are difficult to grasp. Building a data-driven deep learning model for runoff prediction is an effective solution. To achieve the fusion of multi-source information, prediction accuracy and wide applicability, a hybrid model based on CNN-LSTM & GRU-ISSA is proposed in this study. In this paper, meteorological data, hydrological data and runoff data are selected and the maximum information coefficient (MIC) is used to calculate the relationship between each variable and runoff in order to reduce the dimensionality of the…
Citation impact
- FWCI
- 21.12
- Percentile
- 100%
- References
- 68
Authors
5Topics & keywords
- Computer science
- Algorithm
- Surface runoff
- Weighting
- Convolutional neural network
- Data mining
- Inverse distance weighting
- Time series
- Climate action