Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study

In recent decades, natural calamities such as drought and flood have caused widespread economic social damage. Climate change rapid urbanization contribute to the occurrence of disasters. addition, their destructive impact has been altered, posing significant challenges efficiency, equity, sustainability water resources allocation management. Uncertainty estimation in hydrology is essential for By quantifying associated uncertainty reliable hydrological forecasting, an efficient management plan obtained. Moreover, forecasting provides future information assist risk assessment. Currently, majority forecasts utilize deterministic approaches. Nevertheless, models cannot account intrinsic forecasted values. Using Bayesian deep learning approach, this study developed a probabilistic model that covers pertinent subproblem univariate time series multi-step ahead daily streamflow quantify epistemic aleatory uncertainty. The new implements sampling Long short-term memory (LSTM) neural network by using variational inference approximate posterior distribution. proposed method verified with three case studies USA horizons. LSTM point models, LSTM-BNN, BNN, Monte Carlo (MC) dropout (LSTM-MC), were applied comparison model. results show long (BLSTM) outperforms other terms reliability, sharpness, overall performance. reveal all outperformed lower RMSE value. Furthermore, BLSTM can handle data higher variation peak, particularly long-term compared models.