Estimation of surface shear strength in semi-arid rangelands of Iran
Modeling soil erosion and runoff requires accurate estimates of surface soil shear strength. In this study, we investigated the use of artificial neural networks (ANNs) in estimating soil shear strength from measured particle size distribution, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCO3.
Three neural networks structures: multilayer perceptron (MLP), generalized feed-forward, and modular feed-forward networks were used and compared with conventional multiple linear regression analysis. The performances of the models were evaluated using spearman’s correlation coefficient (r) between the observed and the estimated values and normalized mean square error (NMSE). The performance of MLP as compared to other ANN structures was significantly better (p >0.05). Slope and vegetation attributes were found to be the most sensitive variables to estimate surface soil shear strength in semiarid rangeland of Iran.
| Copyright: | © Aristotle University of Thessaloniki | |
| Source: | 2nd International Conference 2009 (Juni 2009) | |
| Pages: | 6 | |
| Price inc. VAT: | € 5,00 | |
| Autor: | B. Khalilmoghadam  Prof. Majid Afyuni Dr. Karim C. Abbaspour Ahmad Jalalian Amir Ahmad Dehghani | |
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