SHAP-enhanced interpretive MGTWR-CNN-BILSTM-AM framework for predicting surface subsidence: a case study of Shanghai municipality

SHAP-enhanced interpretive MGTWR-CNN-BILSTM-AM framework for predicting surface subsidence: a case study of Shanghai municipality

Abstract Urban expansion and subsurface resource exploitation have intensified ground subsidence, posing significant geological risks. Conventional prediction models often overlook multi-scale spatiotemporal effects that critically influence accuracy. This study proposes an integrated MGTWR-CNN-BiLS...

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Bibliographic Details
Main Authors: Long Wen-Jiang, Yu Xue-Xiang, Zhu Ming-Fei, Xue Li, Zhang Guang-Hui, Wang Lin-Lin
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Surface settlement
MGTWR-CNN-BILSTM-AM method
Temporal and spatial variability
Geographically weighted regression
SHAP
Online Access:https://doi.org/10.1038/s41598-025-95694-4
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https://doi.org/10.1038/s41598-025-95694-4

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