Estimation of Evaporation using a Self-Organizing Map Network
Date Issued
2007
Date
2007
Author(s)
Kao, Huey-Shan
DOI
zh-TW
Abstract
The phenomenon of evaporation is an important factor that affects the distribution of water in hydrological cycle and plays a key role in agriculture and water resource management. The tranditional evaporation formulas usally neglect the non-linear characteristics in the nature. In this study we propose the self-organizing map(SOM) network to estimate daily evaporation. First, the daily meteorological data from climate gauges were collected as inputs of the SOM and then classified into topology map based on their similarities to investigate their potential property. To effectively and accurately estimate the daily evaporation, the connected weights between the cluster in topology layer with output layer were trained by using the linear regression method. In addition, we bulit enforced Self-Organizing Map (ESOM) to strength mapping spaces for these extremely data and compared with Modified Penman (FAO,1984) and Penman-Monteith (ICID,1994). The results demonstrated that the topology structures of SOM and ESOM could give a meaningful map to present the clusters of meteorological variables and the networks could well estimate the daily evaporation based on the input meteorological variables used in this study. In comparing the performances of these four models, the ESOM provides the best performance (RMSE=1.15mm/day，MAE=0.87 mm/day). The ESOM performance is also well in estimating long term evaporation. We have the suitability of using these models in other areas where their evaporations are different widely from the original station, the estimation, however, are not well as the one we use in the built station. This result suggests that the network must be adequately trained before it is used to estimate the local evaporation.
Subjects
類神經網路
蒸發量
氣象變數
自組特徵映射網路
artificial neural network
evaporation
meteorological variables
self-organizing map
SDGs
Type
thesis