AIMC Topic: Rivers

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Combination of LEDs and cognitive modeling to quantify sheep cheese whey in watercourses.

Talanta May 22, 2019
The concentration of sheep cheese whey (CW) in water obtained from two Spanish reservoirs, two Spanish rivers, and distilled water has been estimated by combining spectroscopic measurements, obtained with light-emitting diodes (LEDs), and linear or n...
Spectrometry, Fluorescence Lighting Sheep Neural Networks, Computer Animals Whey Water Pollutants Rivers Linear Models
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Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Environmental monitoring and assessment May 19, 2019
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...
Biological Oxygen Demand Analysis Water Pollutants Rivers Multivariate Analysis Neural Networks, Computer Machine Learning Regression Analysis Environmental Monitoring Iran Support Vector Machine Water Quality
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Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China.

Environmental science and pollution research international May 15, 2019
Water quality prediction is an effective method for managing and protecting water resources by providing an early warning against water quality deterioration. In general, the existing water quality prediction methods are based on a single shallow mod...
Neural Networks, Computer Environmental Monitoring Water Quality Rivers Forecasting China Models, Theoretical
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Do reductions in agricultural field drainage during the growing season impact bacterial densities and loads in small tile-fed watersheds?

Water research Nov 30, 2018
Predicting bacterial levels in watersheds in response to agricultural beneficial management practices (BMPs) requires understanding the germane processes at both the watershed and field scale. Controlling subsurface tile drainage (CTD) is a highly ef...
Escherichia coli Bacteria Seasons Agriculture Rivers
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Performance assessment of artificial neural networks and support vector regression models for stream flow predictions.

Environmental monitoring and assessment Nov 8, 2018
Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modelin...
Rivers Water Movements Hydrology Support Vector Machine Pakistan Neural Networks, Computer Artificial Intelligence Algorithms Environmental Monitoring
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Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

Environmental science and pollution research international Nov 7, 2018
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network model...
Cluster Analysis Models, Chemical Water Quality Environmental Monitoring Fuzzy Logic Rivers Neural Networks, Computer Machine Learning Algorithms Temperature Water
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Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China.

International journal of environmental research and public health Sep 23, 2018
Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status withi...
China Soil Environmental Monitoring Support Vector Machine Environmental Restoration and Remediation Animals Seasons Rivers Aquatic Organisms Hydrology Biodiversity
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Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.

International journal of environmental research and public health Sep 21, 2018
In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and eco...
Harmful Algal Bloom Chlorophyll Republic of Korea Neural Networks, Computer Environmental Monitoring Rivers Machine Learning
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Four Major South Korea's Rivers Using Deep Learning Models.

International journal of environmental research and public health Jun 24, 2018
Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to ...
Water Quality Rivers Harmful Algal Bloom Republic of Korea Deep Learning Models, Biological
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Use of ultraviolet-visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index.

Environmental monitoring and assessment May 2, 2018
The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to s...
Rivers Neural Networks, Computer Brazil Spectrophotometry, Ultraviolet Environmental Monitoring Water Pollution, Chemical Water Pollutants, Chemical Ultraviolet Rays Principal Component Analysis Water Quality
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