Comparative Study of Deep Learning LSTM and 1D-CNN Models for Real-time Flood Prediction in Red River of the North, USA-Reference-Cited by-同舟云学术

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Comparative Study of Deep Learning LSTM and 1D-CNN Models for Real-time Flood Prediction in Red River of the North, USA

Published:2023-05-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Electro Information Technology (eIT)
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Author:

Atashi Vida¹,Kardan Ramtin²,Gorji Hamed Taheri³,Lim Yeo Howe¹

Affiliation:

1. University of North Dakota,Department of Civil Engineering,Grand Forks,ND,USA,58202

2. University of North Dakota,Department of Mechanical Engineering,Grand Forks,ND,USA,58202

3. University of North Dakota,Department of Biomedical Engineering,Grand Forks,ND,USA,58202

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10187212/10187216/10187358.pdf?arnumber=10187358

Reference42 articles.

1. Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model

2. Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

3. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods

4. Predicting flood susceptibility using LSTM neural networks

5. Machine Learning from Theory to Algorithms: An Overview

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1. Speech Emotion Recognition In Continuous Space Using IEMOCAP Database;2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC);2024-05-21

2. A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon;Scientific African;2024-03

3. A Multi-step Water Level Prediction Model Based On CNN-LSTM-ATTENTION Combined With Wavelet Transform;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

4. Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data;Water;2023-11-01

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