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

Sungwon Kim,Vijay Singh

doi:10.3390/w7062707

Abstract

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.

Highlights

Rainfall is a necessary input for the design of hydrologic and hydraulic systems
The training performance of artificial neural network (ANN) is iterated until the training error is reached to the training tolerance [38,39,40]
The training performances of multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM) models were stopped after 10,000 iterations

Summary

Introduction

Rainfall is a necessary input for the design of hydrologic and hydraulic systems. Rainfall can be either measured or generated using stochastic simulation [1]. The variability of rainfall has been acknowledged as a reason for the uncertainties in hydrologic applications. To minimize uncertainties calls for methods that improve the reliability of rainfall estimation by combining rainfall information from different sources [2]. Areal rainfall is the average rainfall over the region under consideration and is estimated by one of the popular methods, such as arithmetic mean, Thiessen polygon, isohyetal, spline, kriging, and copula amongst others [3,4,5]. The arithmetic mean method is the simplest one for determining areal rainfall

Objectives

Results

Conclusion