Streamflow prediction using LASSO-FCM-DBN approach based on hydro-meteorological condition classification

Streamflow prediction is a challenging task due to the different processes involved in streamflow generation. These different processes have different characteristics of the relationships between hydro-meteorological variables and streamflow, which make it a challenging task to develop single data-driven stream flow prediction models that can map the input-output relationships for all different streamflow regimes. To improve the performance of streamflow prediction, we proposed a flow-regime-dependent approach to map the relationships between hydro-meteorological variables and streamflow based on hydro-meteorological condition classification. This approach integrates the least absolute shrinkage and selection operator (LASSO), Fuzzy C-means (FCM) and Deep Belief Networks (DBN) and therefore referred to as the LASSO-FCM-DBN approach. This approach employs LASSO to select the hydro-meteorological variables which have a significant impact on streamflow, FCM to identify different streamflow regimes, and DBN as a data-driven model to map the nonlinear and complex relationships between the selected hydro-meteorological variables and streamflow within different flow regimes. To assess the performance of the proposed approach, two comparative studies were carried out - 1) the multivariable FCM was compared to the traditional single-variable threshold-based method; and 2) the performance of the DBN was compared to a traditional Artificial Neural Networks (ANNs) model. Two stations in the Tennessee River, USA were used as the case study. The results demonstrate that the performance of the multivariable-based FCM classification method is better and more stable than the traditional threshold-based single-variable method, due to the sensitivity of the single-variable method to different threshold values. In addition, DBNs performed better than traditional ANNs in all three statistical measures considered. Overall, the LASSO-FCM-DBN multi-model system significantly improved the performance of streamflow prediction and is therefore a valuable tool for water resources management and planning.