| 基于传感物联网数据融合和线性支持向量算法的地表径流水质检测 |
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| 引用本文：卫荣浩1，陈航1，刘昊2，徐飞3.基于传感物联网数据融合和线性支持向量算法的地表径流水质检测[J].计算技术与自动化,2025,(1):107-112 |
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| 中文摘要:常规的地表径流水质检测方法主要结合遥感技术获取水体颜色，并通过图像处理技术分析水体质量，由于对水体等级的细化程度较低，导致检测精度较差。为此，提出了基于传感物联网数据融合和线性支持向量算法的地表径流水质检测。首先采用漂浮式传感器获取地表径流水体数据，并通过对数据进行融合处理，整理出水质数据与水质等级之间的映射关系。然后结合线性向量支持算法，构建回归函数实现地表径流水质预测。最后结合预测残差阈值以及水体等级划分标准，实现对水质等级的判断。在实验中，对提出的方法进行了检测精度的检验。最终的测试结果表明，采用提出的方法对地表径流水质进行检测时，算法的RMSE值较低，具备较为理想的检测精度。 |
| 中文关键词:数据融合 支持向量算法 地表径流 水质检测 检测精度 |
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| Water Quality Detection of Surface Runoff Based on Sensor-Internet-of-Things Data Fusion and Linear Support Vector Algorithm |
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| Abstract:The current conventional surface runoff water quality detection method mainly combines remote sensing technology to obtain the color of the water body and image processing technology to achieve the analysis of water quality, which results in poor detection accuracy due to the low degree of refinement of the water body grade. In this regard, surface runoff water quality detection based on sensing IoT data fusion and linear support vector algorithm is proposed. Firstly, floating sensors are used to acquire the surface runoff water body data, and the mapping relationship between the water quality data and the water quality grade is organized by fusion processing of the data. Then the regression function is constructed by combining the linear vector support algorithm to realize the prediction of surface runoff water quality. Finally, combining the prediction residual threshold and the water body grade classification standard, the judgment of water quality grade is realized. In the experiment, the proposed method was tested for detection accuracy. The final test results show that when the proposed method is used to detect surface runoff water quality, the algorithm has a lower RMSE value and has a more ideal detection accuracy. |
| keywords:data fusion support vector algorithm surface runoff water quality detection detection accuracy |
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