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http://dx.doi.org/10.12652/Ksce.2011.31.2B.155

Development of a Soil Moisture Estimation Model Using Artificial Neural Networks and Classification and Regression Tree(CART)  

Kim, Gwangseob (경북대학교 건축토목공학부)
Park, Jung-A (경북대학교 대학원 공간정보학과)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.31, no.2B, 2011 , pp. 155-163 More about this Journal
Abstract
In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.
Keywords
Soil moisture; CART; ANNs; Remote Sensing;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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