Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions

Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions

[Objectives] This study aims to analyze the applicability of existing precipitation, temperature, and runoff data in data-scarce regions, and to develop and evaluate a deep learning hybrid model driven by multi-source information for improving the accuracy of monthly runoff forecasting. [Methods] Ba...

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Bibliographic Details
Main Author: YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying
Format: Article
Language:zho
Published: Editorial Office of Journal of Changjiang River Scientific Research Institute 2025-07-01
Series:长江科学院院报
Subjects:
runoff forecasting|data-scarce regions|shap interpretable machine learning analysis|ipso-cnn-bigru-attention model|prediction accuracy
Online Access:http://ckyyb.crsri.cn/fileup/1001-5485/PDF/1001-5485(2025)07-0052-08.pdf
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