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Rivers

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Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran.

Journal of environmental management
Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Cho...

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches.

The Science of the total environment
Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but information about their transformation products (TPs) is very limited. Here, we investigated occurrence and transformation of pharmaceuticals and TPs...

Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

PloS one
This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), U...

Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River.

Journal of environmental management
Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehens...

Forecasting of compound ocean-fluvial floods using machine learning.

Journal of environmental management
Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or...

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

Journal of environmental management
One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time seri...

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Journal of environmental management
Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are ...

Ensemble machine learning using hydrometeorological information to improve modeling of quality parameter of raw water supplying treatment plants.

Journal of environmental management
Source and raw water quality may deteriorate due to rainfall and river flow events that occur in watersheds. The effects on raw water quality are normally detected in drinking water treatment plants (DWTPs) with a time-lag after these events in the w...

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Journal of environmental management
Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep...

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

PloS one
Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of variou...