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

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

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D O I：

暂无

中图分类号：

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

引用

共 3 条

[1] Memristor-Based Long and Short-Term Memory Network Models for Optimal Prediction in the IoT
Sun, Junwei
Cao, Yuhan
Yue, Yi
Wang, Yan
Wang, Yanfeng
IEEE Internet of Things Journal, 2024,
[2] Bi-directional Long Short Term Memory Neural Network for Short-Term Traffic Speed Prediction Using Gravitational Search Algorithm
Naheliya, Bharti
Redhu, Poonam
Kumar, Kranti
International Journal of Intelligent Transportation Systems Research, 2024,
[3] One Shot Model for COVID-19 Classification and Lesions Segmentation in Chest CT Scans Using Long Short-Term Memory Network with Attention Mechanism
Ter-Sarkisov A.
IEEE Intelligent Systems, 2022, 37 (03) : 54 - 64

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