Article

Quantifying Predictive Uncertainty in Deep-Sequence Modeling for Early Warnings of Embankment Dam Failure

2025-12-11

Abstract excerpt

<title>Abstract</title> <p>Reliable early warning of embankment dam instability remains a critical challenge due to nonlinear hydrological forcing, soil–structure interaction, and sparse monitoring data. This study introduces a probabilistic deep-sequence modeling framework that integrates a Mixture Density Network (MDN), Monte Carlo dropout, and hybrid ANN–LSTM architecture to predict settlement evolution while...

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Literature Corpus work: db8f12af-1c22-54b9-9e6f-32b01cadafdc
DOI: 10.21203/rs.3.rs-8277761/v1

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Quantifying Predictive Uncertainty in Deep-Sequence Modeling for Early Warnings of Embankment Dam FailureDOI 10.21203/rs.3.rs-8277761/v1Read

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