熵模型在河流监测感知中的研究应用进展

Review on Advances in Entropy Models for River Monitoring and Sensing

摘要: 河流监测感知在水资源管理、防洪减灾及生态环境保护等领域具有重要意义。随着现代测量技术的发展，河流动态数据的获取能力显著提升，如何充分利用这些数据以实现河流系统的立体感知，仍然是数字孪生流域构建中的关键挑战之一。近年来，熵模型在河流监测感知方面得到了广泛应用，但尚缺乏系统的梳理总结。本文通过梳理熵模型的理论基础，分析国内外的研究案例，综述了熵模型在河流监测感知中的研究和应用进展，包括熵模型在河流流场监测感知、河道地形反演重构、河流水沙分析等方面。研究表明，综合运用熵模型技术可实现地形、流速场和生态特性等要素的动态监测感知，模拟精度较好。基于现有研究，提出未来可进一步强化参数自适应性，推动其与无人机、高分辨率遥感、人工智能等技术深度融合，拓展熵模型在复杂场景下的应用，丰富多元融合数据感知，为智慧水利和流域治理提供理论支持与技术支撑。

Abstract: River monitoring and sensing are of great significance in the fields of water resource management, flood control and disaster mitigation, as well as ecological environment protection. With the development of modern measurement technology, the ability to acquire dynamic data of rivers has been significantly improved. However, how to fully utilize these data to achieve three-dimensional perception of river systems remains one of the key challenges in the construction of digital twin basins. In recent years, entropy models have been widely applied in river monitoring and perception, but there is still a lack of systematic review and summary. This paper reviews the theoretical basis of entropy models, and comprehensively analyzes research cases at home and abroad, and summarizes the research and application progress of entropy models in river monitoring and perception, including entropy models in river flow field monitoring and perception, channel terrain inversion and reconstruction, river water and sediment analysis. The research shows that the comprehensive application of entropy model technology can achieve dynamic monitoring and perception of terrain, flow velocity field, and ecological characteristics, with good simulation accuracy. Based on existing research, it is proposed that in the future, parameter adaptability can be further enhanced, and its deep integration with technologies such as drones, high-resolution remote sensing, and artificial intelligence can be promoted. This will expand the application of entropy models in complex scenarios, and enrich multi-source fusion data perception, and provide theoretical and technical support for smart water conservancy and basin management.