ارزیابی تناسب ارضی با استفاده از رویکردهای سنتی و مدلهای یادگیری ماشینی مطالعه موردی دشت آبیک استان قزوین.

Land suitability is a crucial factor in land use planning and sustainable agricultural production. Evaluating land suitability helps optimize land use, promote sustainable land use, protect the environment, and ensure optimal use of natural resources. This study was conducted in the Abiek region of Qazvin province in northwest Iran, covering an area of 60,000 hectares. After collecting data from 300 soil profiles and determining land suitability classes for wheat cultivation with surface irrigation using the FAO classification system, digital elevation models, Landsat-8 and Sentinel-2 satellite images, and environmental variables extracted from the digital elevation model were used to create digital maps using both traditional and machine learning methods. The results showed that the machine learning method had a higher accuracy rate of 74% and a Kappa index of 68 compared to the traditional method with an accuracy rate of 62% and a Kappa index of 53. The most important environmental variables used in the machine learning model were those extracted from the digital elevation model and Landsat-8 satellite images. The largest area for wheat cultivation with surface irrigation was found in the relatively suitable class (S2), with 30,753 hectares in the random forest method and 21,028 hectares in the traditional method. In contrast, the smallest area belongs to the unsuitable class (N), with 3,052 hectares in the forest method. Additionally, random fields and 7185 hectares were identified in the traditional method. Also, 15,000 hectares of the study area are suitable for wheat cultivation without restrictions. [ABSTRACT FROM AUTHOR]