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Prediction of Soil Organic Carbon across Different Land-use Patterns

A Neural Network Approach

^a Dep. of Botany, The Open Univ. of Sri Lanka, Nawala, Nugegoda, Sri Lanka
^b Institute of Fundamental Studies, Hantana Road, Kandy, Sri Lanka
^c Vice Chancellor's Office, The Open Univ. of Sri Lanka, Nawala, Nugegoda, Sri Lanka

View larger version (35K): [in a new window]   Fig. 1. The structure of a typical neural network. Legend: wji = Initial weights, bk = Weight of hidden layer.

View larger version (21K): [in a new window]   Fig. 2. Relationships between the observed and MLR model predicted soil organic carbon (SOC). The SOC measured by (A) internal heat of dilution (Ci) and (B) application of external heat (Ce) across different land use patterns of Sri Lanka.

View larger version (23K): [in a new window]   Fig. 3. Plot of residuals of multi-linear regression (MLR) model predicted soil organic carbon (SOC) as measured by (A) internal heat of dilution (Ci) and (B) application of external heat (Ce) across different land use patterns of Sri Lanka.

View larger version (19K): [in a new window]   Fig. 4. Plot of training, validation and test errors during the training process of the network constructed for the prediction of soil organic C measured by internal heat of dilution (Ci) across different land-use patterns of Sri Lanka.

View larger version (22K): [in a new window]   Fig. 5. Relationships between the observed and ANN model predicted soil organic carbon (SOC). The SOC measured by (A) internal heat of dilution (Ci) and (B) application of external heat (Ce) across different land use patterns of Sri Lanka.