Online Ensemble Modeling for Real Time Water Level Forecasts

Accurate and reliable flood forecasting is essential to mitigate the threats brought by floods. Ensemble approaches have been used in limited studies to improve the forecasts of component models. In this paper an ensemble model based on neural-fuzzy inference system (NFIS) and three real time updating approaches were used to synthesize the water level forecasts from a Adaptive-Network-based Fuzzy Inference System (ANFIS) model and the Unified River Basin Simulator (URBS) model for three stations in Lower Mekong. The NFIS ensemble model results are compared with the simple average model (SAM) which is adopted as a benchmark ensemble model. The ensemble model of offline learning without real time updating (EN-OFF), ensemble model with real time updating using offline learning (EN-RTOFF), ensemble model with real time updating using online learning (EN-RTON1) and ensemble model with real time updating using online learning and sub-models (EN-RTON2) were studied in this paper. Statistical analysis of the models for all the three stations indicated the superiority of the EN-RTON2 model over EN-RTOFF, EN-RTON1 models, SAM and the EN-OFF model. Not only the spikes in the URBS model were eliminated, but also the time shift problems in the ANFIS model results were decreased.