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Rivers

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Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter.

Environmental science & technology
Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...

Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach.

Journal of environmental management
Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Y...

Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin.

Journal of environmental management
The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic and environmental stability. This study presents a novel approach to flood susceptibility (FS)...

Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.

Environmental monitoring and assessment
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...

Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review.

Journal of contaminant hydrology
Water quality (WQ) is a metric for assessing the overall health and safety of water bodies like a river. Owing to the habitation of anthropogenic habitation around its basin, the rivers can become one of the most contaminated water sources globally. ...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...

Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations.

Journal of environmental management
Rivers play a crucial role in in global matter cycling and energy flow, contributing significantly to biogeochemical cycles and the development of human civilization. Reservoirs, as prevalent artificial water bodies, modify river flow and impact ener...

Statistical analysis and prediction via neural networks of water quality in the Middle Paraíba do Sul (Rio de Janeiro State, Brazil) region in the period (2012-2022).

Environmental science and pollution research international
The aim of this study is to accurately predict the water quality at these points over a decade through the combined use of statistical tools and artificial intelligence. This study brings the innovative use of neural networks implemented with the GRN...

Feasibility study of real-time virtual sensing for water quality parameters in river systems using synthetic data and deep learning models.

Journal of environmental management
With water quality management crucial for environmental sustainability, multiple techniques for real-time monitoring and estimation of water quality parameters have been developed. However, certain data types, such as airborne images, are only access...

Water quality parameters-based prediction of dissolved oxygen in estuaries using advanced explainable ensemble machine learning.

Journal of environmental management
The dissolved oxygen (DO) is crucial for the ecological health of estuaries and bays. However, human activities, land-sea interactions, and the unclear impact mechanisms of water quality parameters (WQPs) pose challenges to DO prediction. Water quali...