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.

Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.

Engineering Journal and Science Journal to publish paper with DOI and journal has global recognized indexing Open Access International Journal