Estimating dam reservoir level fluctuations using data-driven techniques

• Autorzy: Unes F. , Demirci M. , Tasar B. , Kaya Y.Z. , Varcin H.
• Języki publikacji: EN

Abstrakty

EN

Estimating dam reservoir level is very important in terms of the operation of a dam, the safety of transport in the river, the design of hydraulic structures, and determining pollution, the salinity of the river flow fluctuations and the change of water quality in the dam reservoir. In this study, an adaptive network-based fuzzy inference system (ANFIS ), support vector machines (SVM), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) approaches were used for the prediction and estimation of daily reservoir levels of Millers Ferry Dam on the Alabama River in the USA. Particularly, the feasibility of ANFIS as a prediction model for the reservoir level has been investigated. The Millers Ferry Dam on the Alabama River in the USA was selected as a case study area to demonstrate the feasibility and capacity of ANFIS, SVM, RBNN, and GRNN. The model results are compared with conventional auto-regressive models (AR), auto-regressive moving average (ARMA), multi-linear regression (MLR) models, and artificial intelligence models for the best-input combinations. The comparison results show that ANFIS models give better results than classical and other artificial intelligence models in estimating reservoir level.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2019
• Tom: 28
• Numer: 5
• Opis fizyczny: p.3451-3462,fig.,ref.

Twórcy

autor

Unes F.

• Civil Engineering Department, Hydraulics Division, Iskenderun Technical University, İskenderun, Hatay, Turkey

autor

Demirci M.

• Civil Engineering Department, Hydraulics Division, Iskenderun Technical University, İskenderun, Hatay, Turkey

autor

Tasar B.

• Civil Engineering Department, Hydraulics Division, Iskenderun Technical University, İskenderun, Hatay, Turkey

autor

Kaya Y.Z.

• Civil Engineering Department, Hydraulics Division, Osmaniye Korkut Ata University, Osmaniye, Turkey

autor

Varcin H.

• Civil Engineering Department, Hydraulics Division, Iskenderun Technical University, İskenderun, Hatay, Turkey

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Identyfikatory

DOI

10.15244/pjoes/93923