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Information Journal Paper

Title

FORCASTING PEEK FLOOD IN THE MADARSOO BASIN USING NEURAL NETWORK AND VARIABLE SEVERAL REGRESSIONS METHOD (CASE STUDY: MADAR SOO BASIN)

Pages

  113-132

Abstract

 In this study, Madrsoo basin, with 2364/96 km2 of area, located in Golestan was chosen as a case study due to it is flash floods stricken nature. These Tow models are ARTIFICIAL NEURAL NETWORK (ANN) and multiple regressions. The QNET2000 software used to evaluate ANN model for flood forecasting and estimation of maximum discharge. In this research used the triple layer MLP ARTIFICIAL NEURAL NETWORK with error propagation training algorithm. The number of input node (first layer) determined (5 nodes) and the number of present node in hidden layer obtained by trial and error. The result shows the ANN model do not sensitive to number of median layer in train phase, but it has a better result with 5 median layers in calibration phase. Accordingly this method is better work than multiple regressions.

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APA: Copy

PANAHI, ALI, & ALIJANI, BOHLOUL. (2013). FORCASTING PEEK FLOOD IN THE MADARSOO BASIN USING NEURAL NETWORK AND VARIABLE SEVERAL REGRESSIONS METHOD (CASE STUDY: MADAR SOO BASIN). GEOGRAPHY, 11(38), 113-132. SID. https://sid.ir/paper/150446/en

Vancouver: Copy

PANAHI ALI, ALIJANI BOHLOUL. FORCASTING PEEK FLOOD IN THE MADARSOO BASIN USING NEURAL NETWORK AND VARIABLE SEVERAL REGRESSIONS METHOD (CASE STUDY: MADAR SOO BASIN). GEOGRAPHY[Internet]. 2013;11(38):113-132. Available from: https://sid.ir/paper/150446/en

IEEE: Copy

ALI PANAHI, and BOHLOUL ALIJANI, “FORCASTING PEEK FLOOD IN THE MADARSOO BASIN USING NEURAL NETWORK AND VARIABLE SEVERAL REGRESSIONS METHOD (CASE STUDY: MADAR SOO BASIN),” GEOGRAPHY, vol. 11, no. 38, pp. 113–132, 2013, [Online]. Available: https://sid.ir/paper/150446/en

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