Integration of Fuzzy Inference Systems and Linear Regression for Enhanced Height Prediction of Deodar Cedar Trees in Kumrat Valley

Accurate estimation of tree height is fundamental to sustainable forest management, particularly in regions such as Kumrat Valley, Pakistan, where Deodar Cedar (Cedrus deodara) serves as a vital ecological and economic resource.Conventional height estimation models often exhibit limitations in capturing the inherent complexity of forest ecosystems, where multiple environmental factors interact non-linearly.To address this challenge, a hybrid predictive framework integrating fuzzy inference systems (FIS) and multiple linear regression (MLR) has been developed to enhance the accuracy of height estimation.The FIS model incorporates key environmental and physiological parameters, including trunk diameter, soil quality, temperature, and rainfall, which are classified into fuzzy sets-low, medium, and high-corresponding to distinct growth rates (slow, normal, fast) and developmental stages (early, average, late).This classification enables a nuanced representation of environmental variability and tree growth dynamics.Complementarily, the MLR model quantifies the statistical relationships between these variables and tree height, yielding an R value of 0.85, an adjusted R of 0.64, and a statistically significant p-value of 0.04.The integration of fuzzy logic with regression analysis offers a robust, data-driven approach to height prediction, effectively addressing the uncertainties associated with environmental fluctuations.By leveraging both rule-based inference and quantitative modeling, this method provides valuable insights for precision forestry, contributing to the sustainable management and conservation of Deodar Cedar in Kumrat Valley.