AIMC Topic: Rivers

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Artificial Intelligence in Geospatial Analysis for Flood Vulnerability Assessment: A Case of Dire Dawa Watershed, Awash Basin, Ethiopia.

TheScientificWorldJournal Nov 22, 2021
This study presents the novelty artificial intelligence in geospatial analysis for flood vulnerability assessment in Dire Dawa, Ethiopia. Flood-causing factors such as rainfall, slope, LULC, elevation NDVI, TWI, SAVI, K-factor, R-factor, river distan...
Floods Rivers Ethiopia Artificial Intelligence Environmental Monitoring Cities Geographic Information Systems
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Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH.

Environmental science and pollution research international Nov 13, 2021
River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study ...
Hydrology Linear Models Support Vector Machine Rivers Least-Squares Analysis
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An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.

Environmental monitoring and assessment Nov 13, 2021
Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the imp...
Neural Networks, Computer Oxygen Rivers Environmental Monitoring Artificial Intelligence
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Multivariate Streamflow Simulation Using Hybrid Deep Learning Models.

Computational intelligence and neuroscience Oct 27, 2021
Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep ...
Neural Networks, Computer Algorithms Water Supply Deep Learning 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 Oct 4, 2021
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...
Machine Learning Algorithms Rivers Environmental Monitoring Decision Trees Water Quality
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Optimized Design of Neural Networks for a River Water Level Prediction System.

Sensors (Basel, Switzerland) Sep 29, 2021
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,...
Rivers Water Quality Water Neural Networks, Computer
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The assessment of emerging data-intelligence technologies for modeling Mg and SO surface water quality.

Journal of environmental management Sep 21, 2021
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...
Intelligence Magnesium Least-Squares Analysis Rivers Water Quality Humans Artificial Intelligence Environmental Monitoring Water
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Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach.

The Science of the total environment Aug 5, 2021
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...
Rivers Water Quality Water Polycyclic Aromatic Hydrocarbons Deep Learning
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Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data.

Sensors (Basel, Switzerland) Jul 31, 2021
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 ...
Rivers Machine Learning Algorithms
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Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Environmental science and pollution research international Jul 28, 2021
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 Byråns V...
Rivers Artificial Intelligence Environmental Monitoring Hydrology Water Movements
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