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Table 3

Results of the MLP neural network in the land change modeler

Land-use transitionsVariables*Hidden layer neuronsAccuracy rate (%)Skill measure
Cultivated land to cities and towns 1, 4, 6 98.65 0.9730 
Cultivated land to rural residential land 2, 3, 4 90.89 0.8178 
Cultivated land to shrubbery lands 2, 3, 6 91.32 0.8263 
Cultivated land to middle coverage grassland 1, 2, 3, 4, 5, 6 86.26 0.7251 
Other forest land including garden to cities and towns 2, 4, 6 88.03 0.7606 
Land-use transitionsVariables*Hidden layer neuronsAccuracy rate (%)Skill measure
Cultivated land to cities and towns 1, 4, 6 98.65 0.9730 
Cultivated land to rural residential land 2, 3, 4 90.89 0.8178 
Cultivated land to shrubbery lands 2, 3, 6 91.32 0.8263 
Cultivated land to middle coverage grassland 1, 2, 3, 4, 5, 6 86.26 0.7251 
Other forest land including garden to cities and towns 2, 4, 6 88.03 0.7606 

*This item indicated the variables selected and used for driving or explaining the land-use transition, which were numbered for short in the table:

1: Digital elevation model map.

2: Slope map.

3: Distance to rural residential land, which is dynamic to be updated during calculation.

4: Distance to cities and towns, which is dynamic to be updated during calculation.

5: Distance to river, which is dynamic to be updated during calculation.

6: Distance to road.

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