Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis

Accurately modeling rainfall–runoff (R–R) transform remains a challenging task despite that a wide range of modeling techniques, either knowledge-driven or data-driven, have been developed in the past several decades. Amongst data-driven models, artificial neural network (ANN)-based R–R models have received great attentions in hydrology community owing to their capability to reproduce the highly nonlinear nature of the relationship between hydrological variables. However, a lagged prediction effect often appears in the ANN modeling process. This paper attempts to eliminate the lag effect from two aspects: modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). Two watersheds from China are explored with daily collected data. Results show that MANN does not exhibit significant advantages over ANN. However, it is demonstrated that SSA can considerably improve the performance of prediction model and eliminate the lag effect. Moreover, ANN or MANN with antecedent runoff only as model input is also developed and compared with the ANN (or MANN) R–R model. At all three prediction horizons, the latter outperforms the former regardless of being coupled with/without SSA. It is recommended from the present study that the ANN R–R model coupled with SSA is more promisings.