Article detail · 2023
Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region
- Year
- 2023
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue ARCHIVES OF AGRONOMY AND SOIL SCIENCE
- Catalog match (ISSN) Archives of Agronomy and Soil Science
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
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 (SQIANN). 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 SQIL and SQIL-ANN while the same results were found between SQINL and SQINL-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.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
24 citations
OpenAlex cited_by_count (cache / database)
18 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems 2024
- Assessment of the neutrosophic Fuzzy-AHP and predictive power of some machine learning approaches for maize silage soil quality 2024
- Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems 2024
- Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems 2024
- Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems 2024
- Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition 2024
- Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition 2024
- Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition 2024
- Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition 2024
- Land suitability assessment for wheat-barley cultivation in a semi-arid region of Eastern Anatolia in Turkey 2023