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Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Simulating California reservoir operation using the classification and regression‐tree algorithm combined with a shuffled cross‐validation scheme
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Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition
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An improved operation-based reservoir scheme integrated with Variable Infiltration Capacity model for multiyear and multipurpose reservoirs
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Identifying effective forecast horizon for real‐time reservoir operation under a limited inflow forecast
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The implicit stochastic model for reservoir yield optimization
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Deep learning
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Finding Reservoir Operating Rules
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Consistent inference of probabilities in layered networks: predictions and generalizations
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Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
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A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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Combining Grey Relational Analysis and a Bayesian Model Averaging Method to Derive Monthly Optimal Operating Rules for a Hydropower Reservoir
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
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Verification tools for probabilistic forecasts of continuous hydrological variables

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