Hydrological Evaluation of Meandering River Restoration in Kushiro Wetland Using a Long Short-Term Memory (Lstm)Based Model for Groundwater Level Prediction

被引：0
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作者：
Yamaguchi, Takumi [1 ]
Miyamoto, Hitoshi [1 ]
Oishi, Tetsuya [2 ]
机构：
[1] Department of Civil Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-Ku, Tokyo,135-8548, Japan
[2] River Engineering Research Team, Civil Engineering Research Institute for Cold Region, Public Works Research Institute, 1-34 Hiragishi 1-Jo 3-Chome, Toyohira-Ku, Hokkaido, Sapporo,062-8602, Japan
来源：
SSRN | 2022年
关键词：
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I：
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学科分类号：
摘要：
Data driven - Data-driven AI model - Deep learning - Ground water level - Hydrological process - Importance analysis - Peat land - Variable importance analyse - Variable importances - Wetland restoration
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