EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Ann. Limnol. - Int. J. Lim., 52 (2016) 137-150 Article references

Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

A multiple process univariate model for the prediction of chlorophyll-a concentration in river systems

Sanghyun Kim

A multiple-process univariate model is developed to predict chlorophyll-a concentrations in inland waters. The results of application of the proposed method and other existing approaches to 16 points in four major rivers of South Korea demonstrated the accurate predictability and parsimony of the developed model for chlorophyll-a prediction.

Ann. Limnol. - Int. J. Lim., 52 (2016) 137-150
Published online: 2016-03-21
DOI: https://doi.org/10.1051/limn/2016003

Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method

Nur Aqilah Paskhal Rostam, Nurul Hashimah Ahamed Hassain Malim, Nur Afzalina Azmee, Renato J. Figueiredo, Mohd Azam Osman and Rosni Abdullah
Computing and Artificial Intelligence 1 (1) 100 (2023)
https://doi.org/10.59400/cai.v1i1.100

Predicting Daily River Chlorophyll Concentrations at a Continental Scale

Philip Savoy and Judson W. Harvey
Water Resources Research 59 (11) (2023)
https://doi.org/10.1029/2022WR034215

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

Moein Izadi, Mohamed Sultan, Racha El Kadiri, Amin Ghannadi and Karem Abdelmohsen
Remote Sensing 13 (19) 3863 (2021)
https://doi.org/10.3390/rs13193863

Merged-LSTM and multistep prediction of daily chlorophyll-a concentration for algal bloom forecast

H Cho and H Park
IOP Conference Series: Earth and Environmental Science 351 (1) 012020 (2019)
https://doi.org/10.1088/1755-1315/351/1/012020

Spatiotemporal and Longitudinal Variability of Hydro-meteorology, Basic Water Quality and Dominant Algal Assemblages in the Eight Weir Pools of Regulated River (Nakdong)

Jae-Ki Shin and Yongeun Park
Korean Journal of Ecology and Environment 51 (4) 268 (2018)
https://doi.org/10.11614/KSL.2018.51.4.268

Forecasting population dynamics of the black Amur bream (Megalobrama terminalis) in a large subtropical river using a univariate approach

Fangmin Shuai, Sovan Lek, Xinhui Li, et al.
Annales de Limnologie - International Journal of Limnology 53 35 (2017)
https://doi.org/10.1051/limn/2016034