Prediction of the Discharge Rate of the Kangavar Watershed, the Province of Kermanshah, Using Gene Expression Programing and the Decision Tree Regression

Document Type : Research

Authors

1 Ph.D. in Water Resources Engineering, Assistant Professor, Department of Water Science and Engineering, Razi University

2 M.Sc. Student of Water Resources Engineering, Razi University

Abstract

A lack of sufficient water resources, climate change, and especially global warming, is causing apprehension in societies, particularly in watershed managers. Scientists and engineers are forecasting climatic data to prevent the waste of water resources and to optimize their use in watersheds. The Aran Station in the Kangavar Watershed was chosen to predict the trend of temperature, precipitation, and runoff using the CMIP5 climate model. Benefiting from 33 years of data (1983 to 2015), the Gene Expression Programming method (GEPM) and Decision Tree methods were developed to train, test, and predict the river discharge rate. Different climate models were implemented using the historical data of the study area. The Fgoals-g2 was chosen to predict temperature and precipitation data for the 2020-2052 periods. The RCP2.5 climate scenario was used as an optimistic scenario, and the output of the change factor downscaling method was used as an input for the GEP model and the ORANGE Software to find the best prediction of the discharge parameter in the future. The results indicated that the temperature of the next cycle will increase by 13 degrees and the maximum monthly temperature will reach 31.18 degrees centigrade. The maximum monthly precipitation will increase by 4 percent and reach 169.51mm. The longtime yearly mean precipitation will change from 423.39 mm to 427.15 mm. The correlation coefficient of the test data in the GEPM was 0.70. The maximum monthly discharge will decrease 1.84 percent, from 29.31 to 28.77 cubic meters per second (m3s-1). The mean discharge will decrease 5.71 percent, from 3.33 to 3.14 cubic meters per second. The correlation coefficient of the test data in the decision tree regression method, using the ORANGE software was 0.995. The mean discharge will increase by 10 percent and reach 3.69 m3s-1. The maximum yearly discharge will decrease by 6 percent, from 7.62 to 7.12 m3s-1.

Keywords

References

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History

  • Receive Date: 21 April 2021
  • Revise Date: 16 June 2021
  • Accept Date: 20 June 2021

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