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Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea

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  • Hye-Suk Yi

    (Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Korea
    K-Water Convergence Institute, Korea Water Resources Corporation, Daejeon 34350, Korea)

  • Sangyoung Park

    (K-Water Convergence Institute, Korea Water Resources Corporation, Daejeon 34350, Korea)

  • Kwang-Guk An

    (Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Korea)

  • Keun-Chang Kwak

    (Department of Control and Instrumentation Engineering, Chosun University, Gwangju 61452, Korea)

Abstract

In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and ecological risk assessment. For this purpose, the performance of the designed models was evaluated for four artificial weirs in the Nakdong River, Korea. The Nakdong River has harmful annual algal blooms that can affect health due to exposure to toxins. In contrast to conventional neural network (NN) that use backpropagation (BP) learning methods, ELMs are fast learning, feedforward neural networks that use least square estimates (LSE) for regression. The weights connecting the input layer to the hidden nodes are randomly assigned and are never updated. The dataset used in this study includes air temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a concentration, which were collected on a weekly basis from January 2013 to December 2016. Here, upstream chlorophyll-a concentration data was used in our ELM2 model to improve algal bloom prediction performance. In contrast, the ELM1 model only uses downstream chlorophyll-a concentration data. The experimental results revealed that the ELM2 model showed better performance in comparison to the ELM1 model. Furthermore, the ELM2 model showed good prediction and generalization performance compared to multiple linear regression (LR), conventional neural network with backpropagation (NN-BP), and adaptive neuro-fuzzy inference system (ANFIS).

Suggested Citation

  • Hye-Suk Yi & Sangyoung Park & Kwang-Guk An & Keun-Chang Kwak, 2018. "Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea," IJERPH, MDPI, vol. 15(10), pages 1-20, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2078-:d:171351
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    References listed on IDEAS

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    Cited by:

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    2. Haleh Sadeghi & Saeed Reza Mohandes & M. Reza Hosseini & Saeed Banihashemi & Amir Mahdiyar & Arham Abdullah, 2020. "Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects," IJERPH, MDPI, vol. 17(22), pages 1-25, November.

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