Quantifying the Impact of Climate Change on Pinus hartwegii Lindl Forests: A Novel Approach Using AI-Powered Allometric Models

Abstract

In Mexico, it is essential to generate sufficiently simple biometric models with reliable projections of the current and future condition of the forest. The objective of this study was to build a dynamic stochastic model using AI tools that estimates TTV allometric equations to evaluate carbon capture and describe the temporal evolutionary displacement in forest Pinus hartwegii Lindl in the states of Mexico and Puebla facing climate change scenarios. Numerical bases were used INFyS, NASA Power data, OLS mathematical models, random forest bookstore, Ridge Model with its regression, R algorithms, residual validation graphs, residuals vs. fitted, normal Q-Q, scale location model, residuals vs. Laverage, Newton's volumetric estimation equations, excurrent dendrometrics (theoretical or logs) and traditional allometric equations by Federal Entity. The allometric equation estimated by mathematical models with the best estimate of the TTV cc is theoretical models for excurrent dendrometric types in the states of Mexico with an evaluation of 90.5% and Puebla with 95.0%, respectively. The allometric equations of “commercial volumetric dimension” were estimated for Pinus hartwegii Lindl with significant climatic variables. There is a better volumetric approach to climate change scenarios using Newton's mathematical equations and theoretical models for excurrent dendrometric types.