4.2. ANNs for soil organic carbon

A set of ANNs were developed to predict SOC distribution across the landscape in Ref. [42]. The ANNs used widely available coarse resolution soil map data, high-resolution DEMgenerated topo-hydrologic variables, and detailed land use data as inputs. In order to select the best combination of inputs, the various schemes of combining inputs were designed and showed in Table 3.

Results from the two-input-node ANN (Level 1) are shown in Figure 8. The STF was the poorest predictor of SOC with a MSE of 84 and ROA 1% (a parameter of assessing model predictions, calculated by counting all predictions within a 1% range of the referenced SOC value) of 66%. The VSP stood out as the best predictor of SOC, with MSE of 29 and ROA 1% of 70.6%. These results indicated that VSP was the best predictor of SOC distribution across the landscapes.

For Level 2, VSP combined with SDR was the best three-input-node ANN SOC prediction model with MSE of 22. The model of VSP combined with PSR also exhibited a slightly higher


\*Relative overall accuracy (ROA) 5%, a parameter of assessing the relative accuracy of model predictions, was calculated by counting all predictions within a 5% range of the referenced clay and sand content.

Table 2. Predicted soil clay and sand content based on 6-25-2 ANN model using the LM method when the epoch was 25, 50, 100, 150, 200 and 250 times in ref. [41].


\*CSOC: coarse resolution SOC data; sand, silt, clay, drainage: high-resolution sand, silt, clay, and drainage data; land use: detailed land use map with 13 classes.

Table 3. Schemes of combining inputs with different levels.

increased but the prediction accuracy decreased. In another word, over-fitted ANN models would have poor "generalization" and could lead to inaccurate prediction when using to other input data than the original training set. The same results were presented for the nets trained by the RP method, but the RP method had the highest value of prediction accuracy with 30

Prediction accuracies of the 6-25-2 network using the LM training method with training cycles of 25–250 are showed in Table 2. As presented, the values of ROA 5% had the maximum value after 100 epochs. The results indicated that when the epochs of training was more than

It can be concluded that net structure, training algorithms, and training cycles would have

A set of ANNs were developed to predict SOC distribution across the landscape in Ref. [42]. The ANNs used widely available coarse resolution soil map data, high-resolution DEMgenerated topo-hydrologic variables, and detailed land use data as inputs. In order to select the best combination of inputs, the various schemes of combining inputs were designed and

Results from the two-input-node ANN (Level 1) are shown in Figure 8. The STF was the poorest predictor of SOC with a MSE of 84 and ROA 1% (a parameter of assessing model predictions, calculated by counting all predictions within a 1% range of the referenced SOC value) of 66%. The VSP stood out as the best predictor of SOC, with MSE of 29 and ROA 1% of 70.6%. These results indicated that VSP was the best predictor of SOC distribution across the landscapes.

For Level 2, VSP combined with SDR was the best three-input-node ANN SOC prediction model with MSE of 22. The model of VSP combined with PSR also exhibited a slightly higher

25 27 86 76 50 25 83 72 100 24 88 81 150 24 87 80 200 23 83 80 250 23 84 81

\*Relative overall accuracy (ROA) 5%, a parameter of assessing the relative accuracy of model predictions, was calculated

Table 2. Predicted soil clay and sand content based on 6-25-2 ANN model using the LM method when the epoch was

Clay Sand

Training cycles MSE (%) ROA 5% (%)\*

by counting all predictions within a 5% range of the referenced clay and sand content.

25, 50, 100, 150, 200 and 250 times in ref. [41].

100, the ANNs could be over-trained, which is another form of over-fitting.

hidden layer nodes and the best net structure was 6-30-2.

significant impacts on performance of an ANN.

4.2. ANNs for soil organic carbon

62 Advanced Applications for Artificial Neural Networks

showed in Table 3.

Figure 8. Mean squared error of ANNs (A) and prediction accuracy referring to relative overall accuracy 1% (B) under different schemes of combining inputs.

MSE (23). However, in terms of MSE, the difference between the two models was considered to be insignificant. Furthermore, the CSOC-VSP-PSR ANN had better performance when measured with ROA 1% (77 vs. 74%) than the CSOC-VSP-SDR ANN. The model of VSP combined with slope showed the highest values of ROA 1% (79%).

Within the four input node ANN models (Level 3), the CSOC-VSP-slope-PSR ANN had the best performance, while the CSOC-VSP-SDR-slope ANN had the poorest accuracy of prediction. A further increase of input nodes by adding other DEM-generated topo-hydrological variables could not improve the accuracy of model prediction. As shown in Figure 8, the method of adding SDR as a new input node into the CSOC-VSP-slope-PSR ANN could cause a decrease in the accuracy of model prediction.

Input data extracted from high-resolution soil maps significantly improved model prediction accuracy (Level 4). For example, the addition of one soil parameter reduced MSE from a range of 8–20 (level II) to 2–9. Based on the results, soil parameters that were extracted from highresolution soil maps could significantly improve the accuracy of model prediction. Both of the content of silt and clay and soil drainage classes were better predictors than the sand content. With MSE decreased to 2 and ROA 1% increased to 98%, soil drainage was the best additional parameter for modeling SOC.

When land use was introduced as an input layer node in addition to the best four-input-node ANN, CSOC-VSP-slope-PSR, the MSE increased from 2 to 3 but the ROA 1% decreased from 98 to 97% (Level 5).
