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High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2018-08, Vol.10 (8), p.1180

2018

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Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

The Science of the total environment, 2020-11, Vol.741, p.140162-140162, Article 140162

2020

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A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

Remote sensing of environment, 2019-11, Vol.233, p.111350, Article 111350

2019

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Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage

Water research (Oxford), 2021-09, Vol.203, p.117483, Article 117483

2021

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Using convolutional neural network for predicting cyanobacteria concentrations in river water

Water research (Oxford), 2020-11, Vol.186, p.116349-116349, Article 116349

2020

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Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

Remote sensing of environment, 2020-01, Vol.236, p.111517, Article 111517

2020

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Estimation of aquatic ecosystem health using deep neural network with nonlinear data mapping

Ecological informatics, 2024-07, Vol.81, p.102588, Article 102588

2024

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Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration

Journal of membrane science, 2019-10, Vol.587, p.117164, Article 117164

2019

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Replacing the internal standard to estimate micropollutants using deep and machine learning

Water research (Oxford), 2021-01, Vol.188, p.116535, Article 116535

2021

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Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models

The Science of the total environment, 2021-11, Vol.794, p.148592-148592, Article 148592

2021

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Analysis of micropollutants in a marine outfall using network analysis and decision tree

The Science of the total environment, 2022-02, Vol.806 (Pt 4), p.150938-150938, Article 150938

2022

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Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea

Water research (Oxford), 2017-12, Vol.126, p.319-328

2017

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Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

The Science of the total environment, 2021-11, Vol.797, p.149040-149040, Article 149040

2021

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Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Environmental modelling & software : with environment data news, 2023-10, Vol.168, p.105805, Article 105805

2023

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Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

The Science of the total environment, 2024-02, Vol.912, p.169540-169540, Article 169540

2024

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Identification and enumeration of cyanobacteria species using a deep neural network

Ecological indicators, 2020-08, Vol.115, p.106395, Article 106395

2020

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Dynamic calibration of phytoplankton blooms using the modified SWAT model

Journal of cleaner production, 2022-04, Vol.343, p.131005, Article 131005

2022

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A novel method for micropollutant quantification using deep learning and multi-objective optimization

Water research (Oxford), 2022-04, Vol.212, p.118080-118080, Article 118080

2022

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Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models

Journal of environmental management, 2021-09, Vol.294, p.112988-112988, Article 112988

2021

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Drone-borne sensing of major and accessory pigments in algae using deep learning modeling

GIScience and remote sensing, 2022-12, Vol.59 (1), p.310-332

2022

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Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling

Journal of hazardous materials, 2021-05, Vol.409, p.124587, Article 124587

2021

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Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT

Journal of hydrology (Amsterdam), 2016-04, Vol.535, p.377-385

2016

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Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

International journal of applied earth observation and geoinformation, 2022-11, Vol.114, p.103053, Article 103053

2022

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Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

GIScience and remote sensing, 2022-12, Vol.59 (1), p.547-567

2022

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Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery

Forests, 2022-12, Vol.13 (12), p.2170

2022

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High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage

Using convolutional neural network for predicting cyanobacteria concentrations in river water

Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

Estimation of aquatic ecosystem health using deep neural network with nonlinear data mapping

Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration

Replacing the internal standard to estimate micropollutants using deep and machine learning

Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models

Analysis of micropollutants in a marine outfall using network analysis and decision tree

Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

Identification and enumeration of cyanobacteria species using a deep neural network

Dynamic calibration of phytoplankton blooms using the modified SWAT model

A novel method for micropollutant quantification using deep learning and multi-objective optimization

Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models

Drone-borne sensing of major and accessory pigments in algae using deep learning modeling

Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling

Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery

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