Results for model hyperparameter values.

<div><p>Debris flows represent a persistent challenge for disaster prediction in mountainous regions due to their highly nonlinear and multivariate triggering mechanisms. This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional...

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Bibliographic Details
Main Author:	Hao Yang (328526) (author)
Other Authors:	Tianlong Wang (794339) (author), Nikita Igorevich Fomin (21594630) (author), Shuoting Xiao (21594633) (author), Liang Liu (50022) (author)
Published:	2025
Subjects:	Ecology Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified validated using data targeted risk mitigation sup >&# 8722 nujiang river basin multivariate triggering mechanisms mountainous regions due integrating deep convolutional hyperparameters autonomously optimized debris flows represent convolutional neural network adaptive early warning 51 × 10 33 × 10 159 debris flow div >< p china </ p yunnan section transferable framework study proposes strong resilience shap explainability shap ), robust generalizability quantitative basis prone gullies primary determinants persistent challenge maximum 24 key factors input noise hour rainfall highly nonlinear fold cross findings provide disaster prediction based architectures 006 %.
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Results for model hyperparameter values.

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