AIMC Topic: Rivers

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Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability.

Computational intelligence and neuroscience Jul 26, 2021
The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two...
Neural Networks, Computer Rivers
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The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods.

Sensors (Basel, Switzerland) Jul 20, 2021
Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bri...
Artificial Intelligence Rivers Hydrodynamics Floods Vibration
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Novel approach for predicting groundwater storage loss using machine learning.

Journal of environmental management Jul 16, 2021
Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major grou...
Artificial Intelligence Machine Learning Groundwater Environmental Monitoring Rivers
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Unsupervised water scene dehazing network using multiple scattering model.

PloS one Jun 28, 2021
In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmos...
Neural Networks, Computer Water Image Interpretation, Computer-Assisted Rivers
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Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Environmental pollution (Barking, Essex : 1987) Jun 6, 2021
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying t...
Deep Learning Environmental Monitoring Water Quality Rivers Chlorophyll A
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Application of ANN and SVM for prediction nutrients in rivers.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering Jun 1, 2021
This paper presents the results of predicting nutrients in rivers on national level by the use of two artificial intelligence methodologies. Artificial neural network (ANN) and support vector machine (SVM) were used to predict annual concentration of...
Humans Neural Networks, Computer Artificial Intelligence Support Vector Machine Nutrients Rivers
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A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment May 27, 2021
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...
Algorithms China Environmental Monitoring Support Vector Machine Least-Squares Analysis Water Quality Rivers
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Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.

Environmental science and pollution research international May 14, 2021
Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of ...
Neural Networks, Computer Forecasting Deep Learning Water Quality Rivers
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An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates.

Journal of environmental management May 1, 2021
Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability ...
Machine Learning Republic of Korea Ecosystem Fresh Water Rivers
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Using a deep convolutional network to predict the longitudinal dispersion coefficient.

Journal of contaminant hydrology Mar 19, 2021
Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of ...
Neural Networks, Computer Water Quality Rivers
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