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Rivers

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Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India.

Environmental research
This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Departm...

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Environmental science and pollution research international
Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-...

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

Science (New York, N.Y.)
We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the...

Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions.

Conservation biology : the journal of the Society for Conservation Biology
Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human-mediated changes to land use and water use. We created a transferable...

Fuzzy logic as a novel approach to predict biological condition gradient of various streams in Ceyhan River Basin (Turkey).

The Science of the total environment
Creating a method to categorize the ecological status of streams according to their biological conditions and establishing scientifically defensible nutrient criteria to protect their biotic integrity poses significant challenges. Biomonitoring of le...

Identification of pollution source and prediction of water quality based on deep learning techniques.

Journal of contaminant hydrology
Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely inform...

SCA-Former: transformer-like network based on stream-cross attention for medical image segmentation.

Physics in medicine and biology
. Deep convolutional neural networks (CNNs) have been widely applied in medical image analysis and achieved satisfactory performances. While most CNN-based methods exhibit strong feature representation capabilities, they face challenges in encoding l...

Deep learning-based total suspended solids concentration classification of stream water surface images captured by mobile phone.

Environmental monitoring and assessment
The continuous monitoring of total suspended solids (TSS) in streams plays an important role in the management of hydrological processes, and TSS is also a decisive parameter in the control of pollution in streams. Determination of TSS involves both ...

An integrated modelling framework for multiple pollution source identification in surface water.

Journal of environmental management
Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm o...

Use of machine learning and deep learning to predict particulate Cs concentrations in a nuclearized river.

Journal of environmental radioactivity
Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of Cs concentration in rivers...