An investigation of artificial neural networks on regional classification and estimation of evaporation in Taiwan
Date Issued
2009
Date
2009
Author(s)
Li, Pin-Hui
Abstract
Evaporation is an important component for watershed management and water resources development and therefore plays a key role in hydrological cycle. In recent years, applications of artificial neural networks on the estimation of evaporation have been proposed. However, previous works merely focused on estimating the evaporation at a specific site. The accuracy may decrease if the constructed model was applied to other sites due to the difference in hydro-geo-meteorology conditions. In this study, daily data are collected from 2002 to 2007 at sixteen meteorological gauges. First of all, these gauges are classified into four clusters according to their similarities by using K-Means, Fuzzy C-Means and SOM. The results indicate that the SOM is more suitable for classification as compared with other methods, and it clustered results cshow a distribution of north region, middle region, mountain region, and south region. Second, the Gamma test is used for finding the meteorological factors that may dominate the evaporation in each cluster. Finally, the selected meteorological factors are separately taken as the inputs of four self-organizing map networks (SOMNs) and the model performance are further compared with those of Modified Penman and Penman-Monteith. The results show that the SOMNs outperform two empirical formulas. Generally speaking, it is time-consuming to build a specific evaporation estimating model for each site in a region even though better performance may be obtained; whereas the four regional SOMN models constructed in this study not only provide a meaningful distribution of each cluster but effectively decrease the number of models. Furthermore, results obtained from this study strongly demonstrate that the regional SOM is an accurate and efficient method for evaporation estimation.
Subjects
Evaporation
Cluster
Artificial Neural Network
Self- Organizing Map
Type
thesis
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