[HTML][HTML] Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
Measurement of total dissolved solids and total suspended solids in water systems: A review of the issues, conventional, and remote sensing techniques
This study provides a comprehensive review of the efforts utilized in the measurement of
water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total …
water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total …
Overview of the application of remote sensing in effective monitoring of water quality parameters
This study provides an overview of the techniques, shortcomings, and strengths of remote
sensing (RS) applications in the effective retrieval and monitoring of water quality …
sensing (RS) applications in the effective retrieval and monitoring of water quality …
Monitoring water quality using proximal remote sensing technology
X Sun, Y Zhang, K Shi, Y Zhang, N Li, W Wang… - Science of the Total …, 2022 - Elsevier
Accurate, high spatial and temporal resolution water quality monitoring in inland waters is
vital for environmental management. However, water quality monitoring in inland waters by …
vital for environmental management. However, water quality monitoring in inland waters by …
Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms
Remote sensing has long been an effective method for water quality monitoring because of
its advantages such as high coverage and low consumption. For non-optically active …
its advantages such as high coverage and low consumption. For non-optically active …
An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery
The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-
makers to manage water resources. Although various machine learning (ML) models have …
makers to manage water resources. Although various machine learning (ML) models have …
Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive
Remote sensing provides full-coverage and dynamic water quality monitoring with high
efficiency and low consumption. Deep learning (DL) has been progressively used in water …
efficiency and low consumption. Deep learning (DL) has been progressively used in water …
UAV multispectral image-based urban river water quality monitoring using stacked ensemble machine learning algorithms—A case study of the Zhanghe river, China
Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote
sensing is critical for water environmental conservation and management. In this study, two …
sensing is critical for water environmental conservation and management. In this study, two …
[HTML][HTML] An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai
L Li, M Gu, C Gong, Y Hu, X Wang, Z Yang… - Science of The Total …, 2023 - Elsevier
The optical insensitivity of non-optically active water quality parameters (NAWQPs) presents
a significant challenge for remote sensing-based quantitative monitoring, which is an …
a significant challenge for remote sensing-based quantitative monitoring, which is an …
A machine learning approach for the estimation of total dissolved solids concentration in lake mead using electrical conductivity and temperature
Total dissolved solids (TDS) concentration determination in water bodies is sophisticated,
time-consuming, and involves expensive field sampling and laboratory processes. TDS …
time-consuming, and involves expensive field sampling and laboratory processes. TDS …