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To manage arable areas according to land resources for future generations, it is crucial to determine the quality of the soils. The main purpose of this study is to identify soil quality for cultivated lands in the semi-humid terrestrial ecosystem in the Black Sea region. Multi-criteria decision-analysis was performed in weighted linear combination approach and standard scoring function (linear-L and nonlinear-NL) integrated with GIS techniques and interpolation models It was tested to predict soil quality index (SQI) values using artificial neural network (SQI ANN ). The soil quality index values obtained using the linear method ranged from 0.444 to 0.751, while those obtained using the non-linear method ranged from 0.315 to 0.683. As a result, we determined the soil quality indices of cultivation areas. According to our statistical analysis, there were no statistically significant differences between the soil quality index values obtained from SQI L  and SQI L-ANN  while the same results were found between SQI NL  and SQI NL-ANN . According to the cluster analysis, 98.2% similarity between SQIL and SQIL-ANN, and 99.2% between SQINL and SQINL-ANN was determined. In addition, the spatial distribution maps obtained by both the clustering analysis and the geostatistical analysis showed quite a lot of similarity between SQI values.

Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region