Our work on flood forecasting using dynamic neural networks (and wavelets) and real-time satellite precipitation forcings (TRMM 3B42RT) got published in Journal of Hydrology.

Title: A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

Abstract: Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the TRMM and TRMM-RT rainfalls at 1–3 days lead times. The results confirm the robustness of the WNARX model with only the satellite-based (TRMM-RT) rainfall (without use of gauge data) to provide reasonably good real-time flood forecasts. The utility of the TRMM-RT solves the real-time flood forecasting issues, since this is the only rainfall product disseminated in real-time. Hence, the WNARX model with the TMPA rainfall products can offer an exciting new horizon to provide flood forecasting and early warning in the flood prone catchments.

Citation: Nanda, T., B. Sahoo, H. Beria, and C. Chatterjee (2016), A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products, J. Hydrol., 539, 57–73, doi:10.1016/j.jhydrol.2016.05.014.

Link:

http://www.sciencedirect.com/science/article/pii/S0022169416302815