Monitoring of Regional Low-Flow Frequency Using Artificial Neural Networks

Subject Areas : Journal of Water Sciences Research

M Akbari ¹ , K Solaimani ² , M Mahdavi ³ , M Habibnejhad ⁴

1 - M. Sc. Watershed, Agriculture Bank of Iran
2 - Associated Professor of Remote Sensing, Agricultural Sciences and Natural Resources University of Sari
3 - Professor of Hydrology, University of Tehran
4 - Associated Professor of Remote Sensing, Agricultural Sciences and Natural Resources University of Sari

Received: 2015-05-17 Accepted : 2015-05-17 Published : 2011-12-01

Keywords: Nu Monitoring Regional, Low-flow, Neural Networks, Lorestan province,

Abstract :

Ecosystem of arid and semiarid regions of the world, much of the country lies in the sensitive and fragile environment Canvases are that factors in the extinction and destruction are easily destroyed in this paper, artificial neural networks (ANNs) are introduced to obtain improved regional low-flow estimates at ungauged sites. A multilayer perceptron (MLP) network is used to identify the functional relationship between low-flow quantiles and the physiographic variables. Each ANN is trained using the Levenberg-Marquardt algorithm. To improve the generalization ability of a single ANN, several ANNs trained for the same task are used as an ensemble. The bootstrap aggregation (or bagging) approach is used to generate individual networks in the ensemble. The stacked generalization (or stacking) technique is adopted to combine the member networks of an ANN ensemble. The proposed approaches are applied to selected catchments in the Lorestan province, Iran, to obtain estimates for several representative low-flow quantiles of summer and winter time. The jackknife validation procedure is used to evaluate the performance of the proposed models. The ANN-based approaches are compared with the traditional parametric regression models. The results indicate that both the single and ensemble ANN models provide superior estimates than these of the traditional regression models. The ANN ensemble approaches provide better generalization ability than the single ANN models.