Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model

Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this study proposes a model that integrates an autoencoder, the Convolutional Neural Networks (CNN), and the Long Short Term Memory (LSTM) networks. Thirty years daily dataset were served to the Autoencoder Convolutional Neural Network Long Short Term Memory (AE-CNN-LSTM) and the baseline models. To evaluate the model's accuracy, 80% of the dataset was used for training and the remaining 20% was used to test the performance of these models. Statistical metrics, for instance, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Nash–Sutcliffe Efficiency (NSE), and the Coefficient of determination (R2) were employed to evaluate the model’s performance. In terms of train RMSE, test RMSE, train MAE, test MAE, train MAPE, test MAPE, train NSE, test NSE, train R2, and test R2, the proposed model significantly obtained the best results with values of 2.6299, 2.7971, 0.1676, 0.1881, 16.76, 18.81, 0.98, 0.97, 0.98, and 0.96, respectively.