STREAMFLOW FORECASTING USING DIFFERENT NEURAL NETWORK MODELS WITH SATELLITE DATA FOR A SNOW DOMINATED REGION IN TURKEY

UYSAL G., ŞORMAN A. A., ŞENSOY ŞORMAN A.

12th International Conference on Hydroinformatics (HIC) - Smart Water for the Future, Güney Kore, 21 - 26 Ağustos 2016, cilt.154, ss.1185-1192 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 154
  • Doi Numarası: 10.1016/j.proeng.2016.07.526
  • Basıldığı Ülke: Güney Kore
  • Sayfa Sayıları: ss.1185-1192
  • Anahtar Kelimeler: Upper Euphrates River, streamflow forecasting, neural networks, snowmelt modeling, FEEDFORWARD, COVER
  • Anadolu Üniversitesi Adresli: Evet

Özet

Data driven models such as Artificial Neural Networks (ANNs) became a very popular tool in hydrology for a long time, especially in rainfall-runoff modelling. However, it does not have common usage in mountainous catchments, where snowmelt plays an important role, due to lack of continuous snow observations. In order to improve the accuracy of snowmelt modeling, recently available satellite snow products are considered as an alternative input to these models. In this study, two different ANN models are employed and compared with each other using novel MODIS satellite snow covered area products as an alternative input into climatic data based models. Firstly, flows are modelled with Multi-Layer Perceptron (MLP) network using gradient-based Levenberg-Marquardt algorithm. Secondly, Radial Basis Function (RBF) network is developed. Both models are performed to estimate the daily flows of Karasu River in the Upper Euphrates Basin, Turkey using 2002 - 2011 data. The main difference between the RBF network and MLP network is in the nature of the nonlinearities associated with hidden nodes. The nonlinearity in MLP is implemented by a fixed function such as a sigmoid. On the other hand, the RBF method bases its nonlinearities on the training set data. In the study the determination of model architectures, optimization algorithms and methods to avoid overfitting are elaborately investigated. (C) 2016 The Authors. Published by Elsevier Ltd.