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Akademický článek

Long short-term memory models of water quality in inland water environments

Autor: JongCheol Pyo, Yakov Pachepsky, Soobin Kim, Ather Abbas, Minjeong Kim, Yong Sung Kwon, Mayzonee Ligaray, Kyung Hwa Cho

Publikováno v: Water Research X, Vol 21, Iss , Pp 100207- (2023)

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior a

Externí odkaz: https://doaj.org/article/c57d1792701f4337b2ac592906a46a05

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Akademický článek

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

Autor: Do Hyuck Kwon, Seok Min Hong, Ather Abbas, Sanghyun Park, Gibeom Nam, Jae-Hyun Yoo, Kyunghyun Kim, Hong Tae Kim, JongCheol Pyo, Kyung Hwa Cho

Publikováno v: GIScience & Remote Sensing, Vol 60, Iss 1 (2023)

Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behavior

Externí odkaz: https://doaj.org/article/3f12053e68594d26bfbef3c5634fd85b

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Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions

Autor: Zeeshan Haider Jaffari, Ather Abbas, Muhammed Umer, Eun-Sik Kim, Kyung Hwa Cho

Publikováno v: Journal of Materials Chemistry A. 11:9009-9018

Precisely measuring the adsorption capability of materials towards toxic heavy metal ions in aqueous solution is essential for the synthesis of effective novel adsorbents.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::658345c924660f42962b6fda47c436aa
https://doi.org/10.1039/d3ta00019b

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Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts

Autor: Muhammad Umer, Sohaib Umer, Mohammad Zafari, Miran Ha, Rohit Anand, Amir Hajibabaei, Ather Abbas, Geunsik Lee, Kwang S. Kim

Publikováno v: Journal of Materials Chemistry A. 10:6679-6689

Carbon-based transition metal (TM) single-atom catalysts (SACs) have shown great potential toward electrochemical water splitting and H2 production.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d710a117182c6154f92cf359e91ac09c
https://doi.org/10.1039/d1ta09878k

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In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

Autor: Ather Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, Kyung Hwa Cho

Publikováno v: Hydrology and Earth System Sciences. 25:6185-6202

Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such moni

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2024dedcb2d2bd9b16ea8422d7e7f3c7
https://doi.org/10.5194/hess-25-6185-2021

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An open-source deep learning model for predicting effluent concentration in capacitive deionization

Autor: Moon Son, Nakyung Yoon, Sanghun Park, Ather Abbas, Kyung Hwa Cho

Publikováno v: The Science of the total environment. 856(Pt 2)

To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f793cc786c4c8e3ab8259d432da086f2
https://pubmed.ncbi.nlm.nih.gov/36191701

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Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

Autor: Sangsoo Baek, Ather Abbas, Minji Park, Kyung Hwa Cho

An accurate estimation of harmful algal blooms is imperative for surface water protection. Site-dependent models have been developed using deep-learning (DL) methods for chlorophyll-a (Chl-a) simulations. These DL models have been developed and evalu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8878041ff300df27583095e91e6a167d
https://doi.org/10.21203/rs.3.rs-1643745/v1

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AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

Autor: Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, Kyung Hwa Cho

Publikováno v: Geoscientific Model Development
Geoscientific Model Development, 2022, 15, pp.3021-3039. ⟨10.5194/gmd-15-3021-2022⟩

Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a06d906664ddf02b1c528eac14fe61ce
https://hal-insu.archives-ouvertes.fr/insu-03661482

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