Evaluation of Random Forest and Hybrid Time Series Models in Bivariate Simulation of Electrical Conductivity

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Online ISSN: 2382-9931

Journal of Water and Irrigation Management

Homepage: https://jwim.ut.ac.ir/

University of Tehran Press

Evaluation of Random Forest and Hybrid Time Series Models in Bivariate Simulation of Electrical Conductivity

Emad Mahjoobi^

Corresponding Author, Department of Water and Environmantal Engineering, Faculty of Civil Engineering, Shahrood Univerisity of Technology, Shahrood, Iran. E-mail: [email protected]

Article Info ABSTRACT

Article type:

Research Article

Article history:

Received: 22 June 2022 Received in revised form:

17 August 2022

Accepted: 04 September 2022 Published online:

25 December 2022

Keywords:

Autocorrelation, Conditional variance, Nonlinear model, Urmia Lake.

The quality parameters of the river, including electrical conductivity, are highly dependent on changes in flow rate. Adding the flow rate parameter to the simulation of this parameter can increase the certainty of the simulation results.For this reason, in this study, random forest, CARMA and CARMA- GARCH models were used to model the electrical conductivity values in Gerdyaghoub, Kutar and Bitas stations in Mahabadchai basin, taking into account the flow rates. In this regard, the monthly values of electrical conductivity and flow discharge in the statistical period 1986-2018 were used.

The results were analyzed using Nash-Sutcliffe statistics, root mean square error and violin plot. The results of evaluation the root mean square error and Nash-Sutcliffe statistics showed that the simulation results of CARMA- GARCH model compared to CARMA model in Bitas and Kuter stations as well as the training step in Gerdyaghoub station were improved. The results showed that the combination of nonlinear and linear models could improve the modeling error in three stations, Gerdyaghoub, Kotar and Bitas in the training step of 9.56, 9.70 and 21.68 percent. By examining the violin plots, the results showed acceptable accuracy and performance of CARMA and CARMA-GARCH models compared to the random forest model. In general, the results showed that time series models have higher accuracy in bivariate simulating of electrical conductivity values in the study area.

Cite this article: Mahjoobi, E. (2022). Evaluation of Random Forest and Hybrid Time Series Models in Bivariate Simulation of Electrical Conductivity. Journal of Water and Irrigation Management, 12 (4), 729-745.

DOI: http://doi.org/10.22059/jwim.2022.344836.1003

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Table 1- Details of the studied hydrometric stations in the study area Q (m³/s) EC (μS/cm)

Station

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Min CV STD Mean

Max Min

1.33 5.4

3.94 45.79

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Table 2. Results of RMSE and NSE in simulation of EC values in studied stations using CARMA model RMSE (μS/cm)

Stations NSE

Test Train

43.56 50.76

0.97 0.96

Gerdyaghoub

54.73 56.64

0.76 0.62

Kutar

43.49 55.09

0.79 0.64

Bitas

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Figure 2. Results of simulation of EC values in Gerdyaghoub station using CARMA model

Figure 3. Results of simulation of EC values in Kutar station using CARMA model

Figure 4. Results of simulation of EC values in Bitas station using CARMA model

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Table 3. Results of RMSE and NSE in simulation of EC values in studied stations using CARMA-GARCH model RMSE (μS/cm)

Stations NSE

Test Train

48.98 45.92

0.96 0.97

Gerdyaghoub

46.88 51.15

0.72 0.80

Kutar

44.98 43.15

0.85 0.72

Bitas

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Figure 5. Results of simulation of EC values in Gerdyaghoub station using CARMA-GARCH model

Figure 6. Results of simulation of EC values in Kutar station using CARMA-GARCH model

Figure 7. Results of simulation of EC values in Bitas station using CARMA-GARCH model

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Stations NSE

Test Train

50.00 53.66

0.69 0.95

Gerdyaghoub

53.48 66.68

0.63 0.67

Kutar

53.52 62.88

0.79 0.53

Bitas

Figure 8. Results of simulation of EC values in Gerdyaghoub station using Random Forest model

Figure 9. Results of simulation of EC values in Kutar station using Random Forest model

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Figure 10. Results of simulation of EC values in Bitas station using Random Forest model

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Figure 11. Violin plot of simulation of EC values given by corresponding discharge values at Gerdyaghoub station using the studied models

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Figure 12. Violin plot of simulation of EC values given by corresponding discharge values at Kutar station using the studied models

Figure 13. Violin plot of simulation of EC values given by corresponding discharge values at Bitas station using the studied models

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5 . C ) 1. Artificial Neural Network

2. Wavelet Artificial Neural Network

3. Generalized Autoregressive Conditional Heteroskidastisity 3. Autoregressive Conditional Heteroskidastisity

4. Contemporaneous Autoregressive Moving Average

6 .

E F +

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7 . E

! @ )

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