Abstract
Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.
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Acknowledgments
This study was supported by the grants from the Everglades Research Fellowship (ERF) Program at University of Florida, which was funded by the Everglades National Park, USA. However, the views expressed in this article do not necessarily represent the views of the agencies. The authors would like to thank Dr. Upmanu Lall at Columbia University and anonymous reviewers for their thoughtful reviewing of the manuscript and constructive comments.
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Kim, TW., Ahn, H. Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data. Stoch Environ Res Risk Assess 23, 367–376 (2009). https://doi.org/10.1007/s00477-008-0223-9
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DOI: https://doi.org/10.1007/s00477-008-0223-9