Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Prediction of pollution loads in the Geum River upstream using the recurrent neural network algorithm

  • Lim, Heesung (Agricultural and Rural Engineering, Chungnam National University) ;
  • An, Hyunuk (Agricultural and Rural Engineering, Chungnam National University) ;
  • Kim, Haedo (Rural research institute, Korea Rural Community Corporation) ;
  • Lee, Jeaju (Rural research institute, Korea Rural Community Corporation)
  • Received : 2018.10.25
  • Accepted : 2018.11.13
  • Published : 2019.03.01
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Abstract

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

Keywords

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Fig. 1. Structure of recurrent neural networks. A, weight; ht, hidden layer; h0, hidden layer 0; h1, hidden layer 1; h2, hidden layer 2; Xt, input layer; X0, input layer 0; X1, input layer 1; X2, input layer 2.

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Fig. 2. Structure of LSTM (long short-term memory) network. A, weight; ht-1, hidden layer (t-1); ht, hidden layer; ht+1, hidden layer (t+1); Xt-1, input layer (t-1); Xt, input layer; Xt+1, input layer (t+1); σ, sigmoid layer; tanh, hyperbolic tangent; X, multiplication; +, plus.

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Fig. 3. Study area (Okcheon-gun, Chungcheongbuk-do, Korea).

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Fig. 4. RNN (recurrent neural network) scatter plot of water quality. BOD, biochemical oxygen demand; COD, chemical oxygen demand; SS, suspended solids.

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Fig. 5. LSTM (long short-term memory) scatter plot of water quality. BOD, biochemical oxygen demand; COD, chemical oxygen demand; SS, suspended solids.

Table 1. Meaning of R2 and RMSE (root mean square error) index.

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Table 2. Model performance results of the BOD (biochemical oxygen demand) RNN (recurrent neural network) model.

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Table 3. Model performance results of the COD (chemical oxygen demand) RNN (recurrent neural network) model.

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Table 4. Model performance results of the SS (suspended solids) RNN (recurrent neural network) model.

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Table 5. Model performance results of the BOD (biochemical oxygen demand) LSTM (long short-term memory) model.

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Table 6. Model performance results of the COD (chemical oxygen demand) LSTM (long short-term memory) model.

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Table 7. Model performance results of the SS (suspended solids) LSTM (long short-term memory) model.

CNNSA3_2019_v46n1_67_t0007.png 이미지

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