AIMC Topic: Rivers
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ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.
Environmental monitoring and assessment
Jul 9, 2015
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
May 21, 2015
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...
Comment on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations A. Šiljić & D. Antanasijević & A. Perić-Grujić & M. Ristić & V. Pocajt. Environ Sci Pollut Res (2014) 22: 4230-4241".
Environmental science and pollution research international
May 13, 2015
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)
Apr 26, 2015
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...
A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.
Environmental monitoring and assessment
Mar 19, 2015
Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The pres...
Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks.
PloS one
Mar 16, 2015
Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard me...
Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata.
Environmental monitoring and assessment
Feb 3, 2015
As one of the most vulnerable coasts in the continental USA, the Lower Mississippi River Basin (LMRB) region has endured numerous hazards over the past decades. The sustainability of this region has drawn great attention from the international, natio...
Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations.
Environmental science and pollution research international
Oct 5, 2014
Biological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eur...
Machine Learning-Assisted Tissue-Residue-Based Risk Assessment for Protecting Threatened and Endangered Fishes in the Yangtze River Basin.
Environmental science & technology
Aug 12, 2025
Assessing pollutant risks to threatened and endangered (T&E) species is crucial for their conservation. However, traditional risk assessment methods for bioaccumulative pollutants to T&E fishes is challenging due to uncertainties in exposure-based to...
Multi-scale transformation and evolutionary factors of ecological security patterns in the Yangtze River Economic Belt.
Journal of environmental management
Aug 1, 2025
Ecological security is vital for ecosystem sustainability and varies across scales. Macro-scale assessments often miss local details, whereas micro-scale evaluations may overlook broader patterns. Multi-scale analysis of ecological security patterns ...
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