The Investigation of the Applicability of Data-Driven Techniques in Hydrological Modeling: The Case of Seyhan Basin

• Autorzy: Turhan Evren , Keleş Mümine Kaya , Tantekin Atakan , Keleş Abdullah Emre
• Języki publikacji: EN

Abstrakty

EN

Proper water resources planning and management is based on reliable hydrological data. Missing rainfall and runoff observation data, in particular, can cause serious risks in the planning of hydraulics structures. Hydrological modeling process is quitely complex. Therefore, using alternative estimation techniques to forecast missing data is reasonable. In this study, two data-driven techniques such as Artificial Neural Networks (ANN) and Data Mining were investigated in terms of availability in hydrology works. Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN) methods were performed on rainfall-runoff modeling for ANN. Besides, Hydrological drought analysis were examined using data mining technique. The Seyhan Basin was preferred to carry out these techniques. It is thought that the application of different techniques in the same basin could make a great contribute to the present work. Consequently, it is seen that FFBPNN is the best model for ANN in terms of giving the highest R2 and lowest MSE values. Multilayer Perceptron (MLP) algorithm was used to predict the drought type according to limit values. This system has been applied to show the relationship between hydrological data and measure the prediction accuracy of the drought analysis. According to the obtained data mining results, MLP algorithm gives the best accuracy results as flow observation stations using SRI-3 month data.

Słowa kluczowe

EN

artificial neural networks; drought analysis; data mining; Multilayer Perceptron; Seyhan Basin

• Wydawca: Politechnika Koszalińska
• Czasopismo: Rocznik Ochrona Środowiska
• Rocznik: 2019
• Tom: Tom 21, cz. 1
• Strony: 29--51
• Opis fizyczny: Bibliogr. 70 poz., tab., rys.

Twórcy

autor

Turhan Evren

• Adana Alparslan Türkeş Science and Technology University, Turkey

autor

Keleş Mümine Kaya

• Adana Alparslan Türkeş Science and Technology University, Turkey

autor

Tantekin Atakan

• Adana Alparslan Türkeş Science and Technology University, Turkey

autor

Keleş Abdullah Emre

• Adana Alparslan Türkeş Science and Technology University, Turkey

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).