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Rivers

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Modelling the presence and identifying the determinant factors of dominant macroinvertebrate taxa in a karst river.

Environmental monitoring and assessment
Modelling the macroinvertebrate community is important for evaluating the status of aquatic ecosystem health. Alternative to physical-based approaches, this study proposed two data-driven methods, support vector machine (SVM) and artificial neural ne...

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

Environmental monitoring and assessment
This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passin...

An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.

Environmental monitoring and assessment
A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model w...

Evidence of Water Quality Degradation in Lower Mekong Basin Revealed by Self-Organizing Map.

PloS one
To reach a better understanding of the spatial variability of water quality in the Lower Mekong Basin (LMB), the Self-Organizing Map (SOM) was used to classify 117 monitoring sites and hotspots of pollution within the basin identified according to wa...

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

Environmental monitoring and assessment
Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learnin...

ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

Environmental monitoring and assessment
The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been consid...

Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model.

Environmental monitoring and assessment
The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mine...

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment.

Environmental pollution (Barking, Essex : 1987)
Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation stud...