Optimizing structural parameters for accurate prediction of height and diameter relationships in Himalayan pine using artificial intelligence based neural networks

Abstract

Accurate prediction of height and diameter relationships in the context of Himalayan Pine (Pinus wallichiana) holds immense ecological significance. Leveraging the capabilities of Artificial Intelligence (AI) through neural network models provides a promising avenue for achieving such predictions. This study focuses on investigating the impact of structural parameters on the accuracy of AI-based neural network models designed specifically for this purpose. By identifying the optimal combination of parameters such as the number of layers, neurons per layer, and the choice of activation functions, the research aims to enhance the precision of predictions regarding the growth patterns of Himalayan Pine. The results of this study have practical implications for ecological research and conservation efforts in the Himalayan ecosystem. By optimizing the structural parameters of AI-based neural network models, researchers can achieve more accurate predictions of height and diameter relationships for Himalayan Pine. Such predictions are instrumental for informed decision-making regarding forest management, conservation strategies, and environmental sustainability.