AIMC Topic: Rivers

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Simulating the fate of indigenous antibiotic resistant bacteria in a mild slope wastewater polluted stream.

Journal of environmental sciences (China) May 3, 2017
The fate of indigenous surface-water and wastewater antibiotic resistant bacteria in a mild slope stream simulated through a hydraulic channel was investigated in outdoor experiments. The effect of (i) natural (dark) decay, (ii) sunlight, (iii) cloud...
Water Microbiology Enterococcus Bacteria Wastewater Drug Resistance, Bacterial Biofilms Escherichia coli Rivers
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Predicting and communicating flood risk of transport infrastructure based on watershed characteristics.

Journal of environmental management Aug 13, 2016
This research aims to identify and communicate water-related vulnerabilities in transport infrastructure, specifically flood risk of road/rail-stream intersections, based on watershed characteristics. This was done using flooding in Värmland and Väst...
Models, Statistical Regression Analysis Least-Squares Analysis Sweden Models, Theoretical Decision Making Risk Geography Floods Transportation Weather Rivers Conservation of Natural Resources Neural Networks, Computer
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Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

Environmental monitoring and assessment Jun 9, 2016
Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydr...
Algorithms Lakes Environmental Monitoring Chad Rivers Meteorology Neural Networks, Computer Artificial Intelligence
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Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA.

Environmental science and pollution research international May 24, 2016
This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and...
Oxygen Water Quality Rivers Neural Networks, Computer Water Pollutants, Chemical Linear Models Multivariate Analysis Phycocyanin
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Modelling the presence and identifying the determinant factors of dominant macroinvertebrate taxa in a karst river.

Environmental monitoring and assessment May 2, 2016
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...
Neural Networks, Computer Animals Ecosystem China Invertebrates Environmental Monitoring Support Vector Machine Rivers
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Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

Environmental monitoring and assessment Apr 19, 2016
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...
Romania Cities Environmental Monitoring Water Quality Rivers Serbia Biological Oxygen Demand Analysis Neural Networks, Computer Spatio-Temporal Analysis
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An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.

Environmental monitoring and assessment Jan 16, 2016
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...
Water Supply Neural Networks, Computer Water Movements Queensland Environmental Monitoring Hydrology Ecosystem Indian Ocean Water Rivers Agriculture
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Evidence of Water Quality Degradation in Lower Mekong Basin Revealed by Self-Organizing Map.

PloS one Jan 5, 2016
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...
Vietnam Water Movements Cambodia Laos Neural Networks, Computer Thailand Seasons Humans Hydrogen-Ion Concentration Nitrates Ammonia Agriculture Rivers Water Pollution Water Quality Geography Wetlands Water Pollutants, Chemical Phosphorus Oxygen
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Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Šiljić et al., Environ Sci Pollut Res (2015) 22: 4230-4241].

Environmental science and pollution research international Dec 21, 2015
Rivers Neural Networks, Computer Biological Oxygen Demand Analysis Monte Carlo Method
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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 Dec 19, 2015
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...
Decision Trees Iran Rivers Groundwater Environmental Monitoring Machine Learning Models, Statistical Geographic Information Systems ROC Curve Models, Theoretical Water Resources Geology
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