Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Estimation of missing precipitation records using modular artificial neural networks

Kajornrit, J., Wong, K.W. and Fung, C.C.ORCID: 0000-0001-5182-3558 (2012) Estimation of missing precipitation records using modular artificial neural networks. In: 19th International Conference on Neural Information Processing (ICONIP2012), 12 - 15 November, Doha, Qatar

PDF - Authors' Version
Download (416kB)


Estimation of missing precipitation records is one of the important tasks in hydrological study. The completeness of precipitation data leads to more accurate results from the hydrological models. This study proposes the use of modular artificial neural networks to estimate missing monthly rainfall data in the northeast region of Thailand. The simultaneous rainfall data from neighboring control stations are used to estimate missing rainfall data at the target station. The proposed method uses two artificial neural networks to learn the generalized relationship of rainfall recorded in dry and wet periods. Inverse distance weighting method and optimized weight of subspace reconstruction method are used to aggregate the final estimation value from both networks. The experimental results showed that modular artificial neural networks provided a higher accuracy than single artificial neural network and other conventional methods in terms of mean absolute error.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Information Technology
Publisher: Springer
Copyright: © Springer-Verlag GmbH Berlin Heidelberg
Notes: The original publication is available at
Item Control Page Item Control Page


Downloads per month over past year