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

Alaboz P., ODABAŞ M. S., Dengiz O.

Archives of Agronomy and Soil Science, cilt.69, sa.15, ss.3514-3532, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 69 Sayı: 15
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/03650340.2023.2248002
  • Dergi Adı: Archives of Agronomy and Soil Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, BIOSIS, CAB Abstracts, Environment Index, Food Science & Technology Abstracts, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.3514-3532
  • Anahtar Kelimeler: ANN, machine learning, soil management, soil quality, sustainable agriculture
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

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.