Modeling Ghotour-Chai River’s Rainfall-Runoff process by Genetic Programming

Journal of Advances in Computer Research

مقاله 6، دوره 9، شماره 1 - شماره پیاپی 31، اردیبهشت 2018، صفحه 71-84   

نوع مقاله: Original Manuscript

نویسندگان

Mina Ruhnavaz؛ Abdolreza Hatamlou^*

Department of Computer Science, Khoy Branch, Islamic Azad University, Khoy, Iran

چکیده

Considering the importance of water and computing the amount of rainfall runoff resulted from precipitation in recent decades, using appropriate methods for predicting the amount of runoff from rainfall date has been really essential. Rainfall-runoff models are used to estimate runoff generated from precipitation in the catchment area. Rainfall-runoff process is totally a non-linear phenomenon. In the present study, it has been tried to Model Ghatoor-Chai River rainfall-runoff, one of the studying sub-basins of Aras River with an area of 8544 square kilometers, by genetic programming and to analyze the results. In this study, the statistical data from Ghatoor-Chai’s daily rainfall-runoff, Marakan hydrometric station during the period 1386-1390 has been used. Data from events during the period 1386-1389 is used for training and data from 1390 for testing. In this modeling, 8 input models have been defined for the system. After applying input models in system, the results based on statistical measures of root-mean-square error and correlation coefficient were analyzed and evaluated. The findings show the success of genetic programming for rainfall-runoff process and this procedure can be suggested as a way for modeling this process.

کلیدواژه‌ها

Rainfall-runoff؛ Genetic programming؛ Forecasting؛ Ghatoor-Chai River