Application of Artificial Neural Networks for estimating index floods

Contributions to Geophysics and Geodesy

Viliam Šimor¹ 

关键词: index flood;    catchment predictors;    Artificial Neural Networks (ANNs);    multiple regression models;   

DOI  :  10.2478/v10126-012-0014-7

学科分类：地球科学(综合)

来源: De Gruyter

【 摘 要 】

This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

【 授权许可】

CC BY-NC-ND