Fusion of Conceputual reservoir and LSTM model
Description
Training and analysing code and data for CRO-LSTM model
Files
Steps to reproduce
1. Hyperparam_search.py. Using the Optuna tool for hyperparameter searching.(Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019.Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD.).Detailed information for Optuna can be found in https://optuna.org. 2.MultiTrain.py. Taking a multi-round training and testing process for the CRO-LSTM, LSTMr, and LSTMc models. 3.Shapley.py. Using the Shap tool for Shapley analyse.Detailed information for Shap can be found in https://shap.readthedocs.io (i)raw_data file. runoff, rain and reservoir data collected from the management authority of the Minjiang basin. (2) data file. The processed data for model construction. Resampling and averaging methods were used to handle missing values and mitigate fluctuations.
Institutions
- Wuhan University