Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Water

Sungwon Kim²  Vijay P. Singh¹ 

[1] Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843-2117, USA; E-Mail:;Department of Railroad and Civil Engineering, Dongyang University, Yeongju 750-711, Korea

关键词: areal rainfall;    conjugate gradient;    Kohonen self-organizing feature map;    Levenberg-Marquardt;    multilayer perceptron;    quickprop;    rainfall disaggregation;   

DOI  :  10.3390/w7062707

来源: mdpi

【 摘 要 】

The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg–Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.