Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

Many studies have attempted to predict chlorophyll-a 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 Boosting (XGBoost), Recurrent Neural Network (RNN), and Long–Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1–step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold–out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1–step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance. View Full-Text

Keywords: machine learning; recurrent neural network; long–short-term memory; 1–step ahead recursive prediction; variable selection; water quality; chlorophyll-a

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

MDPI and ACS Style

Shin, Y.; Kim, T.; Hong, S.; Lee, S.; Lee, E.; Hong, S.; Lee, C.; Kim, T.; Park, M.S.; Park, J.; Heo, T.-Y. Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods. Water 2020, 12, 1822. https://doi.org/10.3390/w12061822

AMA Style

Shin Y, Kim T, Hong S, Lee S, Lee E, Hong S, Lee C, Kim T, Park MS, Park J, Heo T-Y. Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods. Water. 2020; 12(6):1822. https://doi.org/10.3390/w12061822

Chicago/Turabian Style

Shin, Yuna, Taekgeun Kim, Seoksu Hong, Seulbi Lee, EunJi Lee, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Jungsu Park, and Tae-Young Heo. 2020. "Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods" Water 12, no. 6: 1822. https://doi.org/10.3390/w12061822

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI