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Information Journal Paper

Title

Investigating the effect of precipitation series decomposition on the simulation of electrical conductivity of river flow (case study: Eskandari sub-basin)

Pages

  33-48

Abstract

 Introduction: By modeling and simulating the qualitative values of river flow according to meteorological parameters as well as climatic changes, changes in the meteorological phase can be considered indirectly. On the other hand, due to the random measurement of qualitative variables, it is not possible to estimate the daily time series of qualitative variables. Also, due to climate changes and the sharp decrease in river flow rate in recent years, some rivers in Iran have changed from permanent to seasonal. This issue has caused the quality variables of river flow not to be measured continuously. Based on this, simulating qualitative values based on other effective and available parameters can help fill this gap in certain conditions. With Wavelet Theory being capable of improving the simulation results of meteorological and hydrological values, the present study has attempted to examine the effectiveness of this model in simulating the electrical conductivity values of surface water in response to changes in rainfall values. Therefore, the aim of this study is to investigate the effect of rainfall and rainfall decomposition series in the simulation of EC values in the Eskandari sub-basin located in the Zayandeh Rood Dam Basin using Random Forest and Random Tree models. In this study, an attempt has been made to investigate the effect of independent series decomposition on the simulation of dependent series using two learning algorithms. Materials and methods: In this study, the effect of rainfall time series decomposition on the simulation of electrical conductivity values of the surface water of the Eskandari sub-basin in the northwest of the Zayandeh Rood Dam Basin in the statistical period of 1990-2020 was investigated by two algorithms based on Random Tree and Random Forest decomposition of observation precipitation series was done using Wavelet Theory, Daubechies 4 and level 2. The simulation of electrical conductivity values in the studied sub-basin was initially done by using two Random Tree and Random Forest algorithms in two phases of training and testing based on the rainfall values that correspond to the electrical conductivity values on a daily basis. Results and discussion: By decomposition the rainfall observation series into approximate and detail signals and increasing the simulation dimension to 4 dimensions, the simulation results of EC values in the study area revealed that in the case of the Random Forest algorithm, the decomposition of rainfall series was able to reduce the simulation error and improve the performance compared to the initial state of the Random Forest algorithm by 77% in the training phase and 10% in the testing phase. The results showed that the efficiency of the model was 0. 67 and 0. 73 in the training phase for Random Forest and Random Tree algorithms, respectively, and the efficiency was 0. 59 and 0. 55 in the testing phase for the mentioned algorithms by the Nash-Sutcliffe efficiency. In comparison to the Random Tree algorithm, the results of the decomposition of rainfall observation series into approximate and detail signals and their involvement in the simulation of electrical conductivity values showed that the Wavelet-RT model was able to reduce the amount of simulation error by about 54% in the training phase and about 22% in the testing phase. In general, the results showed that the decomposition of the rainfall observation series into approximate and detail signals can greatly improve the accuracy, efficiency and error rate of the simulation. By decomposing the series of observations into approximate and detail signals, the dimensions of the simulation are increased and, by its nature, the complexity of modeling and simulation increases. Conclusion: By decomposing the rainfall series into an approximate series and two partial series and increasing the simulation dimension to 4 dimensions, the results showed that the combination of Wavelet Theory with Random Forest and Random Tree algorithms was able to reduce the simulation error (RMSE) of the conductivity values and improve the algorithm’, s error in the training phase compared to the Random Forest and Random Tree algorithms by about 77. 5 and 54%, respectively. These results in the testing phase are about 10 and 22 percent, respectively.

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    APA: Copy

    NAZERI TAHROUDI, MOHAMMAD, & MIRABBASI NAJAFABADI, RASOUL. (2023). Investigating the effect of precipitation series decomposition on the simulation of electrical conductivity of river flow (case study: Eskandari sub-basin). JOURNAL OF DROUGHT AND CLIMATE CHANGE RESEARCH, 1(1 (1) ), 33-48. SID. https://sid.ir/paper/1051149/en

    Vancouver: Copy

    NAZERI TAHROUDI MOHAMMAD, MIRABBASI NAJAFABADI RASOUL. Investigating the effect of precipitation series decomposition on the simulation of electrical conductivity of river flow (case study: Eskandari sub-basin). JOURNAL OF DROUGHT AND CLIMATE CHANGE RESEARCH[Internet]. 2023;1(1 (1) ):33-48. Available from: https://sid.ir/paper/1051149/en

    IEEE: Copy

    MOHAMMAD NAZERI TAHROUDI, and RASOUL MIRABBASI NAJAFABADI, “Investigating the effect of precipitation series decomposition on the simulation of electrical conductivity of river flow (case study: Eskandari sub-basin),” JOURNAL OF DROUGHT AND CLIMATE CHANGE RESEARCH, vol. 1, no. 1 (1) , pp. 33–48, 2023, [Online]. Available: https://sid.ir/paper/1051149/en

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