%0 Journal Article
%T 基于多源数据融合的河流流量全量程自动监测研究
Research on Full-Range Automatic Monitoring of River Flow Based on Multi-Source Data Fusion
%A 文宏展
%A 潘仁红
%A 吴宇浩
%A 刘华锋
%A 刘炳义
%J Journal of Water Resources Research
%P 169-183
%@ 2166-5982
%D 2025
%I Hans Publishing
%R 10.12677/jwrr.2025.142018
%X 河流流量自动监测数据是水旱灾害防御和生态文明建设的重要依据。受水文监测技术发展限制等影响，单一的流量自动监测方法，难以实现河流流量全量程自动监测。本文以基于动态方差的卡尔曼滤波算法为基础，辅以缺值处理、方法筛选等算法，提出一种基于多源数据融合的河流流量全量程自动监测方法，可以对多种方法给出的流量自动监测数据进行融合，选出最优的流量自动监测数据。通过利用西江梧州水文站2024年4种流量自动监测数据进行融合，并与实测流量和资料整编成果进行对比验证，结果表明，该融合方法获得的流量自动监测数据质量优于单一方法，满足《水文资料整编规范》(SL/T 247-2020)规定的精度要求，既解决了单一方法只能适用部分水位变化范围的问题，又融合了多种方法在高水、中水、低水等不同水位范围的优势，实现河流流量全量程自动监测，可为水旱灾害防御、水资源管理、水环境保护和水生态修复提供更加优质的流量自动监测数据支持。
The automatic monitoring data of river flow is an important basis for flood and drought disaster prevention as well as ecological construction. Due to limitations in the development of hydrological monitoring technology, a single automatic flow monitoring method can hardly achieve comprehensive automatic monitoring of river flow across all ranges. This paper proposes a method for full-range automatic monitoring of river flow based on multi-source data fusion, which is grounded in a dynamic variance-based Kalman filtering algorithm, supplemented by algorithms for missing value processing and method selection. This approach is the integration of automatic monitoring data from various methods to select the optimal flow monitoring data. By utilizing four types of automatic monitoring data from the Wuzhou hydrological station on the Xijiang River in 2024 and validating the results against measured flow and the processed hydrological data, the findings indicate that the quality of the flow monitoring data obtained through this fusion method surpasses that of single methods. It meets the accuracy requirements specified in the “Code for hydrological data processing” (SL/T 247-2020) effectively addressing the issue where single methods can only apply to certain ranges of water level changes. Furthermore, it integrates the advantages of multiple methods across different water level ranges-high, medium, and low-achieving comprehensive automatic monitoring of river flow. This can provide higher qualified automatic monitoring data support for flood and drought disaster prevention, water resource management, water environment protection, and water ecological restoration.
%K 河流流量测验，
%K 流量自动监测，
%K 全量程，
%K 数据融合，
%K 卡尔曼滤波
River Flow Measurement
%K Automatic Flow Monitoring
%K Full-Range
%K Data Fusion
%K Kalman Filtering
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=115856