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Prediction of the Discharge Rate of the Kangavar Watershed, the Province of Kermanshah, Using Gene Expression Programing and the Decision Tree Regression | ||
| پژوهش های آبخیزداری | ||
| Article 2, Volume 35, Issue 1 - Serial Number 134, March 2022, Pages 2-15 PDF (2.5 M) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmrj.2021.354279.1403 | ||
| Authors | ||
| Maryam Hafezparast Mavadat* 1; Foroozan Payfeshorde2 | ||
| 1Ph.D. in Water Resources Engineering, Assistant Professor, Department of Water Science and Engineering, Razi University | ||
| 2M.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 | ||
| Gene expression programing; orange; CMIP5 models; rainfall- runoff | ||
| References | ||
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Aytek A, Asce M, Alp M. 2008. An application of artificial intelligence for rainfall–runoff modeling. Journal of Earth System Science 117: 145–155 Binaman J, Shoemaker CA. 2005. An analysis of high-flow sediment event data for evaluating model performance. Journal of Hydrological Processes. 19: 605–620. Chattamvelli R. 2011. Data mining algorithms. (1th ed). Oxford: Alpha Science: 274–290 Dastoorati MT, Habibipoor A, Ekhtesasi MR, Talebi A, Mahjoobi J. 2012. Evaluation of the decision Tree Model in precipitation prediction (Case study: Yazd Synoptic Station). Iran-Water Resources Research. 8(3): 14–27. (In Persian). Farboudfam N, Ghorbani MA, Alami MT. 2009. Forecasting of river flow using genetic programing (Case study: Liqwan River Basin). Agriculture Science. 19(4): 107–123 (In Persian). Fallahi MR, Varvani H, Glian S. 2011. Flood prediction with decision tree regression model due to flood control. Fifth Conference of watershed management and soil and water resources management. 9th and 10th march, Iran. Kerman (In Persian). Ferreira C. 2001. Gene expression programming a new adaptive algorithm for solving problems. Complex Systems. 13(2):87–129 Ghorbani M, Salehi A. 2011. Using genetic programming for investigating ground water quality changes with surface water fluctuations in Bourkhar plain in Esfahan, 6th National Conference in Civil Engineering. 16th Esfand. Mazandaran. (In Persian). Habibipoor A, Dastoorati MT, Ekhtesasi MR, Afkhami H. 2011. Investigating the effect of decreasing of data range on the efficiency of artificial neural network and decision tree regression models in drought prediction. Journal of Watershed Management. 2(3): 63–79. (In Persian). Habibipour A. (2010). Prediction of drought in Yazd Synoptic Station using decision tree, M.Sc. Thesis, Yazd University. 130 p. (In Persian). IPCC (Intergovernmental Panel on Climate Change). 2013. The physical science basis. In: Stocker TF, Qin Plattner D, Tignor GK, Allen M, Boschung SK, Nauels J, Xia A, Bex Y, Midgley V. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. United Kingdom and New York, NY, USA, 1535 p. Jayawardena AW, Muttil N, Fernando TMKG. 2005. Rainfall-Runoff Modelling using genetic programming. International Congress on Modelling and Simulation Society of Australia and New Zealand. New Zealand: 1841–1847 ISBN: 0-9758400-2-9. Khu ST, Liong SY, Babovic V, Madsen H, Muttil N. 2001. Genetic programming and its application in real- time runoff forecasting. Journal of the American Water Resources Association. 37(2): 439–451. Koza JR, Koza JR. 1992. Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT Press. Liong SY, Gautam TR, Khu ST, Babovic V, Keijzer M, Muttil N. 2002. Genetic programming: A new paradigm in rainfall runoff modeling. Journal of the American Water Resources Association. 38(3): 705–718. Mahjoubi E, Mahjoubi J, Kouchaksaraei A. 2018. Comparison of Decision Trees regression (CART Algorithm) and Artificial Neural Network to Predict Kashkan River Flow, 7th National Conference on Water Resources Management. 5–6 Ordibehesht. Yazd University. (In Persian). Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL. 2007. American Society of Agricultural and Biological Engineers . 50(3): 885−900. Mozafari G, Shafiei SH, Taghizadeh Z. 2015. Evaluating the efficiency of the regression decision tree model in drought forecasting (Case study: Sanandaj Synoptic Station). Dangers of the Natural Environment. 4(6): 1–19. (In Persian). Omidvar K, Shafiei S, Taghizadeh M. 2014. Evaluating the efficiency of the decision tree model in predicting precipitation of Kermanshah synoptic station. Journal of Practical Research in Geographical Sciences. 14(34): 89–110. (In Persian). Rahimi B, HafezParast Mavadat M. 2020. Comparison of support vector regression models, programming of gene expression and IHACRES in predicting runoff changes due to climate change (Case study: Jamishan Dam). Iranian Soil and Water Research. 15(10): 2483–2499. (In Persian). doi: 10.22059 / ijswr.2020.303779.668640. Santhi C, Arnold JG, Williams J, Dugas WA, Hauck L. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. The American Water Resources Association, 37 (5): 1169–1188. Shoaib M, Shamseldin AY, Melville BW, Khan MM. 2015. Runoff forecasting using hybrid wavelet gene expression programming (WGEP) Approach. Journal of Hydrology, 527: 326–344. Soltani A, Ghorbani M, Fakherifard A, Darbandi S, Farsadizadeh D. 2010. Genetic programing and use of it in modeling of precipitation process-runoff. Water and Soil Science. 20(4): 62–71. (In Persian). Ian H, Witten, Eibe Frank, Mark A, Hall, Christopher J, Pal. 2016. Data mining, fourth edition: practical machine learning tools and techniques (4th. ed.). Morgan Kaufmann Publishers Inc. San Francisco. CA. USA. Zarezade Mehrizi SH, Khoorani A, Bazrafshan J, Bazrafshan O. 2018. Assessment of future runoff trends under multiple climate change scenarios in the Gamasiab River Basin, Iranian Journal of Ecohydrology, 5(3): 777–789. | ||
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