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Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Scientific reports, 2023-06, Vol.13 (1), p.9296-9296, Article 9296

2023

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Generalizability evaluations of heterogeneous ensembles for river health predictions

Ecological informatics, 2024-09, Vol.82, p.102719, Article 102719

2024

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Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Ecological indicators, 2022-04, Vol.137, p.108737, Article 108737

2022

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Hybrid model for daily streamflow and phosphorus load prediction

Water science and technology, 2023-08, Vol.88 (4), p.975-990

2023

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Multisite algal bloom predictions in a lake using graph attention networks

Environmental Engineering Research, 2024, 29(2), , pp.1-11

2024

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Machine learning-based prediction of harmful algal blooms in water supply reservoirs

Water quality research journal, 2022-11, Vol.57 (4), p.304-318

2022

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Willingness to Pay for Improved Water Supply Services Based on Asset Management: A Contingent Valuation Study in South Korea

Water (Basel), 2021-08, Vol.13 (15), p.2040

2021

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Prediction of cyanobacteria blooms in the lower Han River (South Korea) using ensemble learning algorithms

Desalination and water treatment, 2017-07, Vol.84, p.31-39

2017

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Assessing Land-Cover Effects on Stream Water Quality in Metropolitan Areas Using the Water Quality Index

Water (Basel), 2020-11, Vol.12 (11), p.3294

2020

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An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2020-04, Vol.12 (7), p.1073

2020

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A Bayesian network incorporating observation error to predict phosphorus and chlorophyll a in Saginaw Bay

Environmental modelling & software : with environment data news, 2014-07, Vol.57, p.90-100

2014

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Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Remote sensing (Basel, Switzerland), 2017-06, Vol.9 (6), p.542

2017

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Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model

Water research (Oxford), 2010-05, Vol.44 (10), p.3270-3282

2010

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Evaluation of temperature effects on brake wear particles using clustered heatmaps

Environmental Engineering Research, 2019, 24(4), , pp.680-689

2019

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The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers

Water research (Oxford), 2017-11, Vol.124, p.11-19

2017

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Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

Water research (Oxford), 2024-02, Vol.249, p.120928-120928, Article 120928

2024

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Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Sustainable cities and society, 2022-12, Vol.87, p.104255, Article 104255

2022

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Multisite algal bloom predictions in a lake using graph attention networks

Environmental engineering research, 2024, Vol.29 (2), p.70-80

2024

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Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

Journal of cleaner production, 2023-08, Vol.414, p.137564, Article 137564

2023

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Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie

Environmental science & technology, 2015-03, Vol.49 (6), p.3392-3400

2015

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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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An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates

Journal of environmental management, 2021-08, Vol.291, p.112719-112719, Article 112719

2021

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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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Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

Water research (Oxford), 2022-05, Vol.215, p.118289-118289, Article 118289

2022

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Are Chlorophyll a–Total Phosphorus Correlations Useful for Inference and Prediction?

Environmental science & technology, 2013-04, Vol.47 (8), p.3768-3773

2013

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Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Generalizability evaluations of heterogeneous ensembles for river health predictions

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Hybrid model for daily streamflow and phosphorus load prediction

Multisite algal bloom predictions in a lake using graph attention networks

Machine learning-based prediction of harmful algal blooms in water supply reservoirs

Willingness to Pay for Improved Water Supply Services Based on Asset Management: A Contingent Valuation Study in South Korea

Prediction of cyanobacteria blooms in the lower Han River (South Korea) using ensemble learning algorithms

Assessing Land-Cover Effects on Stream Water Quality in Metropolitan Areas Using the Water Quality Index

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

A Bayesian network incorporating observation error to predict phosphorus and chlorophyll a in Saginaw Bay

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model

Evaluation of temperature effects on brake wear particles using clustered heatmaps

The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Multisite algal bloom predictions in a lake using graph attention networks

Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie

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

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates

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

Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

Are Chlorophyll a–Total Phosphorus Correlations Useful for Inference and Prediction?

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