Hydrological Neural Modeling Aided by Support Vector Machines
نویسندگان
چکیده
This paper aims in the construction of an Artificial Neural Network model that performs successful estimation of the Maximum Water Supply (m /sec) and of the Special Water Flow (m/sec*Km) using several topographic, meteorological and morphometric parameters as independent variables. Support Vector Machines applying specific Kernel algorithms [9] are used to determine the Error or Loss of the Neural Network model and to enhance its ability to generalize. Data come from the Greek island of Thasos, which is located in the North-Eastern part of the Aegean sea. As a matter of fact, this manuscript can be considered as a specific case study, but its modeling design principles and its Error minimization methods can be applied in a wide range of research studies and applications
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