Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster

Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year-12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96-94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling.