Search Results - ConvLSTM encoder-decoder model

An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting

Authors: Cheng Wang Kaiyu Xue Chuang Shi

Source: IEEE Geoscience and Remote Sensing Letters. 22:1-5

Multi-task prediction model based on ConvLSTM and encoder-decoder

Authors: Xueliang Wei Rui Zhang Xudong Cao et al.

Source: Intelligent Data Analysis. 25:359-382

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Access URL: https://content.iospress.com/articles/intelligent-data-analysis/ida194969
https://doi.org/10.3233/IDA-194969
https://dblp.uni-trier.de/db/journals/ida/ida25.html#LuoCLDZW21

An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

Authors: Zhao Feng Zeeshan Tariq Xianda Shen et al.

Contributors: Zhao Feng Zeeshan Tariq Xianda Shen et al.

Source: Gas Science and Engineering. 125:205314

File Description: application/pdf

ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction.

Authors: Li, Yalan Deng, Haiming Xiao, Jian et al.

Source: Mathematics (2227-7390); Jun2025, Vol. 13 Issue 12, p1986, 17p

Subject Terms: LONG short-term memory, PREDICTION models, PROBLEM solving, LATITUDE, ELECTRONS

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction.

Authors: Zhang, Deyu Wang, Changying Huang, Baoxiang et al.

Source: Applied Sciences (2076-3417); Apr2024, Vol. 14 Issue 8, p3278, 21p

Subject Terms: SEA ice, PYRAMIDS, FORECASTING, PREDICTION models, DEEP learning

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Authors: Guozhen Xia Fubin Zhang Cheng Wang et al.

Source: Space Weather, Vol 20, Iss 8, Pp n/a-n/a (2022)

Subject Terms: encoder‐decoder, medium‐term global prediction, Astrophysics, 7. Clean energy, 01 natural sciences, ionospheric TEC, QB460-466, 13. Climate action, Meteorology. Climatology, ConvLSTM, 0103 physical sciences, QC851-999, CNN, 0105 earth and related environmental sciences

Access URL: https://doaj.org/article/e4fe49e43e234c15b490d86bc026991f

Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation.

Authors: Kim, Jeongdae Choo, Hyunseung Jeong, Jongpil

Source: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11315, 15p

Subject Terms: LONG short-term memory, OBJECT recognition (Computer vision), PROBLEM solving, DECISION making, FORECASTING

Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism

Authors: S. Lakshmi A. Krishnamoorthy

Source: IEEE Access, Vol 12, Pp 179628-179647 (2024)

Subject Terms: BiConvLSTM, Air quality prediction, encoder-decoder, deep learning, spatial-temporal attention mechanism, prediction of PM2.5 and PM10, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/986c1e6c5ede4c3faaf7799ce205fe80

Ionospheric TEC Forecasting with ED-ConvLSTM-Res Integrating Multi-Channel Features.

Authors: Yang, Jiayue Huang, Wengeng Zhang, Lei et al.

Source: Remote Sensing; Nov2025, Vol. 17 Issue 21, p3564, 14p

Subject Terms: SOLAR activity, FORECASTING, IONOSPHERIC electron density, SPATIOTEMPORAL processes, GEOMAGNETIC variations, MACHINE learning

Structural Surrogate Model and Dynamic Response Prediction with Consideration of Temporal and Spatial Evolution: An Encoder–Decoder ConvLSTM Network.

Authors: Peng, Hong Yan, Jingwen Yu, Ying et al.

Source: International Journal of Structural Stability & Dynamics. Aug2021, Vol. 21 Issue 10, p1-30. 30p.

Subject Terms: *STRUCTURAL models, *DEEP learning, *DYNAMIC models, *ARTIFICIAL intelligence, *DYNAMIC loads, *FORECASTING, *SHEAR walls

Multi-task prediction model based on ConvLSTM and encoder-decoder.

Authors: Luo, Tao Cao, Xudong Li, Jin et al.

Source: Intelligent Data Analysis; 2021, Vol. 25 Issue 2, p359-382, 24p

Subject Terms: PREDICTION models, DECODERS & decoding, LOAD forecasting (Electric power systems), CONCEPT learning, DEEP learning, TIME series analysis, VIDEO coding

A hybrid ConvLSTM-Nudging model for predicting surface soil moisture in the Qilian Mountains, China.

Authors: Fan, Manhong Xiao, Qian Yu, Qinghe et al.

Source: Journal of Arid Land; Nov2025, Vol. 17 Issue 11, p1623-1648, 26p

Convolutional LSTM-Attention Based Encoder–Decoder Neural Network for Prediction of Chaotic Vibrations of Multi-Dimensional Dynamic Systems.

Authors: Wang, Luyao Dai, Liming Zhao, Haixing et al.

Source: International Journal of Structural Stability & Dynamics; 10/30/2024, Vol. 24 Issue 20, p1-29, 29p

Subject Terms: DYNAMICAL systems, GENERALIZATION, MEMORY, FORECASTING

ConvLSTM Based Temperature Forecast Modification Model for North China.

Authors: GENG Huan-tong HU Zhong-yan WANG Tian-lei

Source: Journal of Tropical Meteorology. Dec2022, Vol. 28 Issue 4, p405-412. 8p.

Subject Terms: *NUMERICAL weather forecasting, *FORECASTING, *CONVOLUTIONAL neural networks, *STANDARD deviations, *DEEP learning, *WEATHER forecasting

Geographic Terms: CHINA

Company/Entity: EUROPEAN Centre for Medium-Range Weather Forecasts (Organization)

An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls.

Authors: Feng, Zhao Tariq, Zeeshan Shen, Xianda et al.

Source: Gas Science & Engineering. May2024, Vol. 125, pN.PAG-N.PAG. 1p.

Subject Terms: Geological carbon sequestration, Spatial analysis (Statistics), Deep learning

LARRES: A New Deep Learning Based Global Ionosphere Map Prediction Model With Large Receptive Field.

Authors: Zhuang, Yonggai Wang, Lei Yang, Wenqing et al.

Source: Space Weather: The International Journal of Research & Applications; Nov2025, Vol. 23 Issue 11, p1-18, 18p

Subject Terms: IONOSPHERE, DEEP learning, TOTAL electron content (Atmosphere), STATISTICAL models, PREDICTION models

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model.

Authors: Yang, Jiayue Huang, Wengeng Xia, Guozhen et al.

Source: Remote Sensing; May2024, Vol. 16 Issue 10, p1700, 15p

Subject Terms: STANDARD deviations, DEEP learning, ORBIT determination, FORECASTING, PREDICTION models

Geographic Terms: EUROPE

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction

Authors: Deyu Zhang Changying Wang Baoxiang Huang et al.

Source: Applied Sciences, Vol 14, Iss 8, p 3278 (2024)

Subject Terms: deep learning, sea ice concentration prediction, pyramidal convolution, dilated encoder–decoder ConvLSTM, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/8/3278; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/299eaa279c644087970737e9a35260a4

PConvLSTM: an effective parallel ConvLSTM-based model for short-term electricity load forecasting.

Authors: Kshetrimayum, Nilakanta Singh, Khumukcham Robindro Hoque, Nazrul

Source: International Journal of Data Science & Analytics; Sep2025, Vol. 20 Issue 3, p2493-2510, 18p

A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity.

Authors: Peng, Guangwen Liu, Yingbing Xiao, Cong et al.

Source: International Journal of Digital Earth; Dec2025, Vol. 18 Issue 1, p1-18, 18p

Subject Terms: SEAWATER salinity, RECURRENT neural networks, OCEANOGRAPHIC observations, FORECASTING, SPATIOTEMPORAL processes, GRAPH neural networks