Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Application of Gene Expression Programming and Support Vector Regression models to Modeling and Prediction Monthly precipitation

Authors
Abazar Solgi
Heidar Zarei
Shahid Chamran University of Ahvaz
10.29252/jgs.18.50.91
Abstract
Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R2= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
Keywords
Modeling monthly precipitation
Gene Expression Programming
Support Vector Regression
Nahavand City
Beriro, D.J., Abrahart, R.J., Paul Nathanail, C. (2013). “Comparison of genetic program-ming with neuro-fuzzy systems for predicting short-term water table depth fluctuations” by Jalal Shiri & Ozgur Kisi [Computers and Geosciences (2011) 1692–1701]. Computers & Geosciences 56: 216-220.
Cortes, C., Vapnik. V. (1995). Support-Vector Networks. Machine Learning 20: 273-295.
Fallah-Mehdipour, E., Bozorg Haddad, O., Mariño, M.A. (2013). Prediction and simula-tion of monthly groundwater levels by genetic programming. Journal of Hydro-environment Research 7(4): 253-260.
Ferreira, C. (2001). Gene expression program-ming a new adaptive algorithm for solving problems. Complex Systems 13(2): 87-129.
Ferreira, C. (2006). Gene expression program-ming: mathematical modeling by an artificial intelligence Springer-Verlag. Germany.
George, Jany., Letha, J., Jairaj, P.G . (2014). Daily Rainfall Prediction using Generalized Linear Bivariate Model- A Case Study. Procedia Technology 24: 31 – 38.
Badr, H.S., Zaitchik, B.F. (2014). Application of Statistical Models to the Prediction of Seasonal Rainfall Anomalies over the Sahel. Journal of Applied Meteorology and Climatology. 53:614-636.
Kashid, S.S., Maity, R. (2012). Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming. Journal of Hydrology 454–455: 26-41.
Nourani, V., Komasi, M. (2013). A geomo-rphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology 490: 41-55.
Shiri, J., Kişi, Ö. (2011). Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers & Geosciences 37(10): 1692-1701.
Shiri, J., Kisi, O., Yoon, H., Lee, K.K., Hossein Nazemi, A. (2013). Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques. Computers & Geosciences 56(0): 32-44.
Shoaib, M., Shamseldin, A.Y.,. Melville, B.W Khan, M.M. (2015). Runoff Forecasting using HybridWavelet Gene Expression Programming(WGEP) Approach. Journal of Hydrology 527 326-344.
Solgi, A., Nourani V., Pourhaghi, A. (2014). Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neuro Fuzzy Inference System (Case Study: Verayneh Station, Nahavand). Advances in Civil Engineering 2014: 1-12.
Solgi, A., Radmanesh, F., Pourhaghi, A.,. Bagherian Marzouni, M. (2014). Evaluation of Artificial Intelligence Systems Performance in Precipitation Forecasting. TI Journals Agriculture Science Developments 3(7): 256-264.
Sujay Raghavendra, N., Paresh Chandra, D. (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing 19: 372-386.
Tripathi, S., Srinivas, V.V., Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology 330(3–4): 621-640.