Influence of lag time on event-based rainfall-runoff modeling using the data driven approach

Talei A., Chua L.C.H, Journal of Hydrology, Vol 438-439, pp. 223-233., 2012

This study investigated the effect of lag time on the performance of data-driven models, specifically the
adaptive network-based fuzzy inference system (ANFIS), in event-based rainfall–runoff modeling. Rainfall
and runoff data for a catchment in Singapore were chosen for this study. For the purpose of this study,
lag time was determined from cross-correlation analysis of the rainfall and runoff time series. Rainfall
antecedents were the only inputs of the models and direct runoff was the desired output. An ANFIS model
with three sub-models defined based on three different ranges of lag times was developed. The performance
of the sub-models was compared with previously developed ANFIS models and the physicallybased
Storm Water Management Model (SWMM). The ANFIS sub-models gave significantly superior
results in terms of the RMSE, r2, CE and the prediction of the peak discharge, compared to other ANFIS
models where the lag time was not considered. In addition, the ANFIS sub-models provided results that
were comparable with results from SWMM. It is thus concluded that the lag time plays an important role
in the selection of events for training and testing of data-driven models in event-based rainfall–runoff