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Validation of the predicted map with different techniques (point validation, prediction confidence analysis, and map-to-map comparison) confirmed that the output is reliable and can be used in various soil and environmental studies without major difficulties.
The Multi-Layer Perceptron model was validated before making predictions for further landscape change using the Markov chain method: a predicted map for 2009 was produced and compared with an actual one.
The results showed that the predicted map quite accurately reflected the regional soil variation.
The predicted map has been verified by a series of experimental data.
The predicted MAP fell outside the standard error measurement for the experimental data at only LBNP −30 mmHg while CO was more variable.
The predicted map showed that Podzols and Luvisols were the most frequent soil groups, covering almost two-thirds of the area of Denmark.
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Furthermore, when applying a framework for assessing soil suitability for viticulture, the predicted maps appeared much more coherent with current land use than when using the original polygons.
The spatial detail in predicted maps was considerably improved in relation to the original, while number, proportions and extents of predicted soil types per map unit agreed with the correspondent in the survey report at rates above 70%.
The predicted maps with the kriged dependence terms looked unrealistically smooth, particularly in the classification tree models where they were often selected as the most important variables, and therefore heavily influenced the spatial pattern of the resulting map predictions.
Visual inspection of the predicted maps compared to high-resolution imagery demonstrated that the zero-inflated models also more closely matched the landscape, as traditional models more often incorrectly predicted canopy cover in non-forested areas.
A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8%and2.3%3%, respectively, which is satisfactory in a practical context.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com