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This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to ...
Can machine learning algorithms accurately predict water quality parameters using satellite-derived spectral signatures? The study utilizes UAV multispectral ...
Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE <= 19.35%) compared to NOAPs (MRE <= 42.06%). Furthermore, spatial and temporal ...
The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, ...
2 days ago · UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments.
UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China.
An Ensemble Machine Learning Model to Estimate Urban Water Quality Parameters Using Unmanned Aerial Vehicle Multispectral Imagery · Xiangdong LeiJie Jiang +5 ...
Sep 29, 2024 · In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion.
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The ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.
This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing.