Modeling planted forests biomass in the north of Iran using Machine learning approaches

• Machine learning models were evaluated for predicting biomass in planted forests. • Biomass prediction improved by integrating diverse biotic and abiotic variables. • Key biotic and abiotic drivers of forest biomass were identified. • The support vector machine model achieved very high predictive accuracy (R² = 0.98), outperforming the random forest model. Forest biomass is a critical parameter for assessing the condition of forest ecosystems. It plays a vital role in determining carbon sequestration and serves as an important indicator for evaluating forest health. In this study, we predicted and estimated forest biomass in the Guilan forests of northern Iran using multiple linear regression models, as well as two artificial intelligence models: Random Forest (RF) and Support Vector Machine (SVM). We collected research data through field observations, measurements, and standardized sampling protocols for forest inventory. To ensure the experimental nature of the research was upheld, we followed technical guidelines and applied various statistical techniques. Data collection and sampling occurred in the field, while soil tests were conducted in the laboratory to guarantee data accuracy. We systematically established 32 square fixed-area plots, each covering 0.04 hectares, arranged on a rectangular grid of 50 × 50 meters across the Radar Poshteh section in Siahkal County. In our modeling, we incorporated a variety of biotic and abiotic variables, including tree volume, basal area (BA), and the physical and chemical properties of the soil. The results indicated that the artificial intelligence models predicted forest biomass with greater accuracy and precision than the multiple linear regression model. Among these models, the SVM demonstrated a significantly higher coefficient of determination (R²) value of 0.98, compared to the RF model (R² = 0.49) and the multiple linear regression model (R² = 0.35). Additionally, a sensitivity analysis revealed that the most influential variables affecting forest biomass in the study area were tree volume and basal area. Among the abiotic factors, the percentage of soil organic matter and clay content were also significant. This study highlights that artificial intelligence techniques can greatly enhance the accuracy of biomass estimation.