Zhao Ziqi, Wan Luhe, Wang Lei, Che Lina
College of Geographical Sciences, Harbin Normal University, Harbin 150025, China.
Heilongjiang Wuyiling Wetland Ecosystem National Observation and Research Station, Yichun 153000, China.
Sensors (Basel). 2024 Oct 18;24(20):6716. doi: 10.3390/s24206716.
Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.
了解水质对于环境管理和政策制定至关重要。然而，现有的水质评估方法往往无法与多源遥感数据充分整合。本研究介绍了一种在谷歌地球引擎（GEE）中采用堆叠算法对松花江流域（SHRB）水质等级进行分类的方法。通过利用多个机器学习模型的优势，堆叠泛化（SG）模型的准确率达到了91.67%，与传统方法相比，显著提高了分类性能。此外，分析还揭示了归一化植被指数（NDVI）和降水量与水质等级之间存在显著相关性。这些发现强调了该方法在有效水质监测方面的有效性及其对理解自然因素对水污染影响的意义。
