Prediction of missing flow records using multilayer perceptron and coactive neurofuzzy inference system.
Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R (2) of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM.
Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung 91201, Taiwan.
Samkele S
SS
Tfwala
Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung 91201, Taiwan.
Yu-Min
YM
Wang
Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung 91201, Taiwan.
Yu-Chieh
YC
Lin
D004190:Disasters; D062070:Hydrology / Q000379:methods*; D008962:Models, Theoretical; D045483:Rivers