ORIGINAL RESEARCH
Estimating Dam Reservoir Level Fluctuations
Using Data-Driven Techniques
1 1 | Iskenderun Technical University, Civil Engineering Department, Hydraulics Division, İskenderun, Hatay, Turkey |
2 | Osmaniye Korkut Ata University, Civil Engineering Department, Hydraulics Division, Osmaniye-Turkey |
CORRESPONDING AUTHOR
Fatih Üneş
Iskenderun Technical University, Civil Engineering Faculty / Hydraulics Division. 31200, İskenderun Campus, 31200 HATAY, Turkey
Iskenderun Technical University, Civil Engineering Faculty / Hydraulics Division. 31200, İskenderun Campus, 31200 HATAY, Turkey
Submission date: 2018-02-26
Final revision date: 2018-07-09
Acceptance date: 2018-08-02
Online publication date: 2019-04-29
Publication date: 2019-05-28
Pol. J. Environ. Stud. 2019;28(5):3451–3462
KEYWORDS
reservoir levelPredictionadaptive network-based fuzzy inference systemsupport vector machinesradial basis neural networksgeneralized regression neural networks
TOPICS
ABSTRACT
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.
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