Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of...

Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering the potential to uncover patterns and trends that may not be readily apparent through traditional methods. This study is aimed at evaluating the efficiency of various MLAs (such as SVM, KNN, CART, Naïve Bayes, and Random Forest) in analyzing LULC changes in Northeast Iran. The analysis utilized the Google Earth Engine (GEE) to process satellite imagery spanning the years 1994 to 2021, covering a period of 27 years. Landsat 5 and TM sensor data from 1994 and 2001, as well as Landsat 8 and OLI sensor data from 2014 and 2021, were employed in this research. Additionally, post-processing tasks on the classified images were carried out using ArcGIS 10.8 software. Based on the validation results, it is evident that the Random Forest machine learning algorithm outperformed other algorithms. In contrast, for the years 2014 and 2021, the support vector machine algorithm had the highest accuracy of 85%, making it the most optimal choice during those years. The results indicated a decrease in rangeland, with a significant difference (34.04%) observed in 1994-2021. This decline could be attributed to factors such as rangeland degradation and a shift in LULC towards agriculture and orchards. Conversely, agricultural land had significant increases of 275.83%, 223.77%, and 61.97% in 2021 compared to 1994, 2001, and 2014, respectively. However, the area of forest lands decreased notably over the studied periods, with reductions of 81.66%, 64.21%, and 30.56% in 2021 compared to 1994, 2001, and 2014, respectively. The study results reveal distinction shifts in LULC patterns, indicating declines in rangelands and significant expansions in agricultural areas, which need to be considered in land use planning and environmental conservation programs.