#AITradingAffectsForex Using Predictive Analytics for Sustainable Forest Management focuses on applying data-driven models to optimize forest management practices, ensuring long-term ecological balance, economic profitability, and social benefits. Here's a summary of the key points: 1. Importance of Sustainable Forest Management (SFM): SFM aims to balance the ecological, economic, and social roles of forests. Predictive analytics helps forecast forest trends, optimize resource use, and mitigate risks, contributing to healthier, more resilient forest ecosystems. 2. Role of Predictive Analytics: Forest Growth and Yield Prediction: Predictive models estimate how forests will grow and what timber yields can be expected under various management strategies. These forecasts guide harvesting decisions, ensuring sustainable resource extraction. Biodiversity and Habitat Modeling: Predictive analytics models the impact of forest management practices on biodiversity, helping to conserve wildlife habitats and promote ecological stability. Climate Change Adaptation: By predicting the effects of climate change (e.g., temperature rise, drought, pests), predictive models assist in adjusting forest management practices to mitigate adverse impacts, such as species shift or increased fire risk. 3. Data Sources: Remote Sensing: Satellite imagery