CNN–Attention–LSTM with Bayesian Optimization for Multi-Level Sump Well Anomaly Early Warning

Reliable anomaly early warning for hydropower station sump wells remains challenging due to the strong nonlinearity of water level dynamics and the limited adaptability of conventional fixed-threshold alarms. Here, we present a hybrid deep learning framework—termed CNN–Attention–LSTM–BO—that fuses m...

Descripció completa

Dades bibliogràfiques

Autors principals:	Yining Lin, Changchun Cai
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-04-01
Col·lecció:	Mathematics
Matèries:	sump well early warning time series anomaly detection CNN–Attention–LSTM Bayesian optimization dual dynamic thresholding hydropower safety monitoring
Accés en línia:	https://www.mdpi.com/2227-7390/14/9/1528
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!