Leveraging Machine Learning for Optimized Water Conservation and Tree Canopy Expansion in Telangana: A Data-Driven Approach
Author : B Kavitha
Abstract :
This research presents a data-driven approach to optimize water conservation and expand tree canopy coverage in Telangana, leveraging machine learning techniques. The study utilizes a comprehensive dataset comprising 45 observations of forest health indicators, including tree metrics (DBH, height, crown dimensions), environmental factors (temperature, humidity, slope, elevation), soil nutrient levels (Total Nitrogen, Phosphorus, Available Phosphorus, Ammonium Nitrogen), biodiversity indices (Menhinick and Gleason), disturbance levels, fire risk indices, and overall health status. By employing advanced machine learning models, the analysis identifies key drivers of forest health and water conservation potential. Results highlight the significant influence of soil nutrients, biodiversity indices, and climatic conditions on tree canopy health and water retention efficiency. The findings provide actionable insights for policymakers and forest managers to implement sustainable practices aimed at improving ecosystem resilience while addressing water scarcity challenges in semi-arid regions like Telangana.
Keywords :
Water conservation, tree canopy expansion, forest health, machine learning, soil nutrients, biodiversity indices, sustainable forestry.
