Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs)

A reliable hydrologic prediction is essential for planning, designing and management activities of water resources. This study quantifies the parametric uncertainty involved in flood forecasting using artificial neural network (ANN) models. Hourly water level forecasting models are developed and uncertainty assessment is carried out for hourly water level forecasting. Hourly water level data of five upstream gauging stations of a large river basin are considered for hourly water level forecasting. The uncertainty associated with hourly flood forecast is investigated using the bootstrap based artificial neural networks (BANNs). Ensemble prediction is made by averaging the output of member bootstrapped neural networks. Results obtained indicate that BANN-hydrologic forecasting models with confidence bounds can improve their reliability for flood forecasts. It is illustrated that the confidence intervals based on BANNs are capable of quantifying the parametric uncertainty for short as well as for long lead time forecasts. Study shows the ensemble prediction is more consistent and reproducible. The study also analyzes the effect of length of training datasets and performance of split sample validation in BANNs modeling. The results illustrate that short length of training datasets with appropriate representation can perform similar to models with long length training datasets. The results also illustrate that the bootstrap technique is capable of solving the problems of over-fitting and underfitting during training of BANN models as the results without cross validation show similar performance compared to results obtained using cross validation technique.