Climate Change Data Portal
| DOI | 10.1007/s41060-024-00547-4 |
| Time series forecasting of wheat crop productivity in Egypt using deep learning techniques | |
| 发表日期 | 2024 |
| ISSN | 2364-415X |
| EISSN | 2364-4168 |
| 英文摘要 | Egypt's agricultural sector plays a critical role in the country's economy, with wheat cultivation being vital for ensuring food security. However, the challenges faced by wheat farming in Egypt, such as climate change, water scarcity, and pest infestations, contribute to yield fluctuations, highlighting the need for accurate and timely predictions of wheat productivity. To address this need, time series analysis is employed, which involves analyzing data collected at regular intervals over a specific time frame. Time series data can encompass multiple variables recorded simultaneously at each interval, known as multivariate time series data. Traditional statistical methods have been commonly used for time series analysis. However, these methods have limitations when dealing with nonlinear and complex data patterns, especially in agricultural data exhibiting spatiotemporal characteristics. Deep learning techniques have emerged as a promising solution to address these limitations. In this paper, we have developed the multivariate multi-head attention temporal convolutional networks (MATCN) model specifically to handle the spatiotemporal nature of multivariate time series data in the context of wheat crop productivity prediction in Egypt. The experimental results revealed that our proposed MATCN model exhibited the most effective prediction performance when compared to other state-of-the-art models, achieving a mean absolute percentage error (MAPE) values ranging from 0.091 to 5.5%. |
| 英文关键词 | Time series; Deep learning; Long short-term memory (LSTM); Gated recurrent units (GRU); Temporal convolutional networks (TCN); Attention mechanism |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
| WOS记录号 | WOS:001226717500001 |
| 来源期刊 | INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/296966 |
| 作者单位 | Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank (EKB); Agricultural Research Center - Egypt |
| 推荐引用方式 GB/T 7714 | . Time series forecasting of wheat crop productivity in Egypt using deep learning techniques[J],2024. |
| APA | (2024).Time series forecasting of wheat crop productivity in Egypt using deep learning techniques.INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS. |
| MLA | "Time series forecasting of wheat crop productivity in Egypt using deep learning techniques".INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
