Abstract
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.
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Kajornrit, J., Wong, K.W., Fung, C.C. (2012). Estimation of Missing Precipitation Records Using Modular Artificial Neural Networks. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_7
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DOI: https://doi.org/10.1007/978-3-642-34478-7_7
Publisher Name: Springer, Berlin, Heidelberg
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