Work

Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach

Year: 2024

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s24206716

Authors: Ziqi Zhao, Luhe Wan, Lei Wang, Lina Che

Institution: Harbin Normal University

Language: English

Topic: Water Quality and Pollution Assessment

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,955