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Rivers

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Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed.

Water environment research : a research publication of the Water Environment Federation
Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green Ri...

Optimized Design of Neural Networks for a River Water Level Prediction System.

Sensors (Basel, Switzerland)
In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that,...

The assessment of emerging data-intelligence technologies for modeling Mg and SO surface water quality.

Journal of environmental management
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can imp...

Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach.

The Science of the total environment
Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid sur...

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data.

Sensors (Basel, Switzerland)
In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not ...

Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Environmental science and pollution research international
Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska ByrÄns V...

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

Computational intelligence and neuroscience
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...

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods.

Sensors (Basel, Switzerland)
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...

Novel approach for predicting groundwater storage loss using machine learning.

Journal of environmental management
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...

Unsupervised water scene dehazing network using multiple scattering model.

PloS one
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...