4.3. ANNs for soil drainage

An ANN was developed and trained to predict high-resolution soil drainage class maps following the flowchart in Figure 9. The research indicated that the best ANN for mapping soil drainage had five input nodes (two from coarse resolution soil maps: average soil drainage class, sand content; three from DEM-generated topo-hydrological variables: slope, SDR, and VSP) and 20 hidden nodes in Ref. [34]. After training, the calibration correlation coefficient of the ANN was 0.69, which was slightly higher than the prediction correlation coefficient (0.65), with MSE of 0.758.

The trained ANN was used to produce a high-resolution soil drainage map for a little watershed (Figure 10). An error matrix was constructed using soil drainage records (measured soil

Figure 9. Schematic diagram showing structure and flow of the artificial neural network for predicting soil drainage in ref. [34].

resolution 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 addi-

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

An ANN was developed and trained to predict high-resolution soil drainage class maps following the flowchart in Figure 9. The research indicated that the best ANN for mapping soil drainage had five input nodes (two from coarse resolution soil maps: average soil drainage class, sand content; three from DEM-generated topo-hydrological variables: slope, SDR, and VSP) and 20 hidden nodes in Ref. [34]. After training, the calibration correlation coefficient of the ANN was 0.69, which was slightly higher than the prediction correlation coefficient (0.65),

The trained ANN was used to produce a high-resolution soil drainage map for a little watershed (Figure 10). An error matrix was constructed using soil drainage records (measured soil

Figure 9. Schematic diagram showing structure and flow of the artificial neural network for predicting soil drainage in

tional parameter for modeling SOC.

64 Advanced Applications for Artificial Neural Networks

98 to 97% (Level 5).

with MSE of 0.758.

ref. [34].

4.3. ANNs for soil drainage

Figure 10. Low-resolution soil drainage map (A), high-resolution soil drainage map (B) and predicted soil drainage map based on artificial neuron network model (C) in ref. [34].

drainage classes) from 1:10,000 soil maps as reference data (Figure 10B) and predicted soil drainage classes using the ANN (Figure 10C). Results indicated that 52% of model-predicted drainage classes were exactly the same as the field assessment. About 94% of model-predicted drainage classes were within 1 class compared to the field assessment.

The comparing of coarse resolution soil drainage map (Figure 10A) and predicted soil drainage map using ANN model (Figure 10C) showed that the predicted soil drainage maps have more detailed soil drainage condition information than the coarse resolution soil drainage map. As shown in Figure 10C, the original soil polygon boundaries of coarse resolution soil map are still visible in the high-resolution soil map, which indicated that coarse resolution soil data had a significant influence on the distribution of soil drainage in high-resolution soil drainage map produced. This implied that the accuracy of the coarse resolution soil sand content data, especially around the boundary, will affect the accuracy of predicted highresolution soil drainage maps.
