RAINFALL ESTIMATION USING ARTIFICIAL NEURAL NETWORK METHOD

Yıl 2012, Cilt: 4 Sayı: 1, 10 - 19, 01.03.2012

Özlem Terzi Eda Çevik

Öz

Bu çalışmada, Isparta’nın aylık toplam yağış değerlerini (RMT) tahmin temek için yapay sinir ağı (YSA) modelleri geliştirilmiştir. RMT’yi tahmin etmek için Devlet Meteoroloji İşleri Genel Müdürlüğü’nden alınan Senirkent, Uluborlu, Eğirdir, Yalvaç ve Isparta istasyonlarına ait yağış verileri kullanılmıştır. Aynı zamanda, aynı girdi parametreleri kullanılarak çoklu lineer regresyon (ÇLR) modelleri geliştirilmiştir. Geliştirilen modellerin performanslarını değerlendirmek için ölçülmüş yağış değerleri ile YSA ve ÇLR modelleri karşılaştırılmıştır. Karşılaştırmalar YSA tahminleri ile ölçülmüş yağış değerleri arasında iyi bir uyuşma olduğunu göstermiştir.

Anahtar Kelimeler

Yağış, Yapay Sinir Ağları, Çoklu Lineer Regresyon, Isparta

YAPAY SİNİR AĞLARI METODU İLE YAĞIŞ TAHMİNİ

Öz

In this study, artificial neural network (ANN) models were developed as a new approach to estimate monthly total rainfall (RMT) for Isparta. Rainfall data from Senirkent, Uluborlu, Eğirdir, Yalvaç and Isparta stations in Isparta, operated by the Turkish State Meteorological Service were used to estimate RMT. The various models were developed to estimate RMT using ANN method. Also, multiple linear regression models were developed using the same input parameters for rainfall estimation. The results of ANN and multiple linear regression models were compared with measured rainfall values to evaluate performance of the developed models. The comparison showed that there was a good agreement between the ANN estimations and measured rainfall values

Anahtar Kelimeler

Yağış, Yapay Sinir Ağları, Çoklu Lineer Regresyon, Isparta

Kaynakça

• Andersen ME, Jobson HE (1982). Comparison of techniques for estimating annual lake evaporation using climatological data. Water Resour. Res. 18:630-636.
• Bodri L, Cermak V (1999). Prediction of Extreme Precipitation using a Neural Network: Application to Summer Flood Occurence in Moravia. Adv. Eng. Soft. 31:311-321.
• Braddock RD, Kremmer ML, Sanzogni L (1998). Feed-forward artificial neural network model for forecasting rainfall-runoff. Environmetrics. 9:419–432.
• Chiang Y-M, Chang F-J, Jou B J-D, Lin P-F (2007). Dynamic ANN for precipitation estimation and forecasting from radar observations. J. Hydrol. 334:250–261.
• Çevik E (2009). Rainfall forecasting with artificial neural networks method. M.Sc. Thesis, Suleyman Demirel University Graduate School of Natural and Applied Sciences, Isparta Turkey, p.51, (In Turkish).
• Demuth H, Beale M (2001). Neural network toolbox user’ guide—version 4.
• Dibike YB, Solomatine DP (2001). River flow forecasting using artificial neural networks. Phys. Chem. Earth (B) 26:1-7.
• Dorvlo ASS, Jervase, JA, Al-Lawati A (2002). Solar radiation estimation using artificial neural networks. Appl. Energy. 71:307-319.
• French MN, Krajewski W-F, Cuykendall RR (1992). Rainfall forecasting in space and time using a neural network. J. Hydrol. 137(1-4):1-31.
• Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009). An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13:1413–1425.
• Imrie CE, Durucan S, Korre A (2000). River flow prediction using artificial neural networks: generalization beyond the calibration range. J. Hydrol. 233:138-153.
• Keskin ME, Terzi Ö (2006). Artificial neural network models of daily pan evaporation. J. Hydrol. Eng. 11(1):65-70.
• Kumar DN, Raju KS, Sathish T (2004). River flow forecasting using recurrent neural networks. Water Res. Manage. 18:143-161.
• Lin G-F, Chen L-H (2004). A non-linear rainfall-runoff model using radial basis function network. J. Hydrol. 289:1–8.
• Lin CT, Lee CSG (1995). Neural fuzzy systems, Prentice Hall P T R 797, New Jersey.
• Lin G-F, Wu M-C (2009). A hybrid neural network model for typhoon-rainfall forecasting. J. Hydrol. 375:450–458.
• Luk KC, Ball JE, Sharma A (2000). A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 227:56-65.
• Mohandes M, Rehman S, Halawani TO (1998). A neural networks approach for wind speed prediction. Renew. Energy. 13:345-354.
• Mohandes M, Balghonaim A, Kassas M, Rehman S, Halawani TO (2000). Use of radial basis functions for estimating monthly mean daily solar radiation. Solar Energy. 68:161-168.
• Ramirez MCV, Velhob HFC, Ferreira NJ (2005). Artificial neural network technique for rainfall forecasting applied to the Sa˜o Paulo region. J. Hydrol. 301:146–162.
• Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM (2002). Modelling evaporation using an artificial neural network algorithm. Hydrolog. Proces. 16:3189-3202.
• Tapiador FJ, Kidd C, Hsu K-L, Marzano F (2004). Neural networks in satellite rainfall estimation. Meteorol. Appl. 11:83–91.
• Tokar AS, Johnson PA (1999). Rainfall-runoff modeling using artificial neural networks. J. Hydraul. Eng. 4:232-239.
• Zealand CM, Burn DH, Simonovic SP (1999). Short term streamflow forecasting using artificial neural networks. J. Hydrol. 214:32-48.