Modeling of dam reservoir volume using generalized regression neural network, support vector machines and m5 decision tree models

Abstract

Dam reservoir capacity estimation is an important issue for operation, design and safety assessments of dam structures. In this study, the reservoir capacity of the Stony Brook dam in the USA was estimated by Generalized Regression Neural Network (GRNN), Support Vector Machines (SVM) and M5 Tree Model (M5T) methods with using 3726 data taken from United States Geological Survey Institute (USGS) for 2012-2015 years. Listed soft computing techniques give opportunities to researchers working on non-linear problems. Based on the non-linear approach, models are generated by using precipitation, flow, temperature hydrological parameters. The models were compared with each other according to the three statistical criteria, namely, mean absolute error (MAE), root mean square error (RMSE), and determination coefficient. As a result of the study, it is seen that Support Vector Machines (SVM) models have better performance in predicting dam reservoir level than the other used soft computing models.