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Rivers

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Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Environmental monitoring and assessment
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), a...

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

Environmental science and pollution research international
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a ...

Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall-snowmelt regime Munzur River, Türkiye.

Environmental science and pollution research international
Revealing the dynamic link between rainfall and runoff, which are the main components of the hydrological cycle, is significant for the planning and managing water resources, disaster risk management, and construction of water structures. This study ...

Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue.

Environmental science and pollution research international
While some robust artificial intelligence (AI) techniques such as Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) have been frequently employed in the field of water resources, documents aimed to...

Two-Stream Network One-Class Classification Model for Defect Inspections.

Sensors (Basel, Switzerland)
Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in vari...

Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning.

Sensors (Basel, Switzerland)
Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once v...

Application of deep learning approaches to predict monthly stream flows.

Environmental monitoring and assessment
Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gate...

Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Environmental monitoring and assessment
Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine re...

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery.

Environmental science and pollution research international
Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images ...

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China.

Environmental science and pollution research international
In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, w...