基于自编码器的层叠LSTM迁移学习模型

IF 2.7 4区地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Earth Science Informatics Pub Date : 2023-09-08 DOI:10.1007/s12145-023-01096-3

Abdullahi Uwaisu Muhammad, Hamza Djigal, T. Muazu, Jibril Muhammad Adam, Abdoul Fatakhou Ba, Umar Sani Dabai, Sani Tijjani, Muhammad Sabo Yahaya, Aliyu Ashiru, Umar Muhammad Mustapha Kumshe, Saddam Aliyu, Faruwa Ajibola Richard

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An autoencoder-based stacked LSTM transfer learning model for EC forecasting

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来源期刊

Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY

CiteScore

4.60

自引率

3.60%

发文量

157

审稿时长

4.3 months

期刊介绍： The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.