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生态环境预测是环境保护的关键技术之一，同时也是应对气候变化等全球性环境问题的重要手段。随着遥感、物联网、地理信息系统等技术的快速发展，越来越多种类的环境数据得以获取。因此，高效地利用这些数据实现生态环境预测成为了研究的热点之一。文章重点讨论了气象、土壤和水质数据的融合分析和应用，包括不同模态环境数据的获取和处理方法，综述了多模态环境数据融合分析的研究，以及深度学习应用于多模态环境数据融合分析的研究，总结了当前研究存在的问题并对未来研究方向进行了展望。
Abstract：Ecological environment prediction is one of the key technologies for environmental protection and an important means to address global environmental issues such as climate change. With the rapid development of technologies such as remote sensing, the Internet of Things and geographic information systems, an increasing variety of environmental data can be obtained. Therefore, efficiently utilizing these data to achieve ecological environment prediction becomes one of the research hotspots. This paper focuses on the fusion analysis and application of meteorological, soil and water quality data, including the acquisition and processing methods of different modal environmental data, summarizes the research on multi-modal environmental data fusion analysis and the application of deep learning in multi-modal environmental data fusion analysis. This paper also summarizes the existing problems in the current research and looks forward to the future research directions.
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基本信息:
中图分类号:X321;TP18
引用信息:
[1]余俊龙,胡雯婧,梁雷,等.基于多源异构环境数据的生态环境预测算法综述[J].微型电脑应用,2025,41(04):60-63+83.
基金信息:
临港新片区高新产业和科技创新专项项目(SH-LG-GK-2020-02-11)
2025-04-20
2025-04-20