Prediction of Chlorophyll-<i>a</i> Concentrations in the Nakdong River Using Machine Learning Methods

Prediction of Chlorophyll-<i>a</i> Concentrations in the Nakdong River Using Machine Learning Methods

Many studies have attempted to predict chlorophyll-<i>a</i> concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boos...

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Bibliographic Details
Main Authors: Yuna Shin, Taekgeun Kim, Seoksu Hong, Seulbi Lee, EunJi Lee, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Jungsu Park, Tae-Young Heo
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Water
Subjects:
machine learning
Recurrent Neural Network
Long–Short-Term Memory
1–step ahead recursive prediction
variable selection
water quality
Online Access:https://www.mdpi.com/2073-4441/12/6/1822

Internet

https://www.mdpi.com/2073-4441/12/6/1822

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