This study investigates the impact of reinforcement learning (RL)-optimized afforestation planning on watershed-scale soil erosion reduction in comparison with classical silvicultural planning methods. Escalating climate change pressures and anthropogenic land-use transformations have rendered soil erosion a critical environmental concern at the global scale. Traditional silvicultural planning approaches operate on expert knowledge and static rules, yet may prove insufficient in generating optimal solutions under multidimensional and dynamic environmental conditions. In this context, an RL-based afforestation planning framework was developed using Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms. The simulation environment was integrated with the Revised Universal Soil Loss Equation (RUSLE) and tested across three distinct watershed scenarios representing Mediterranean, Black Sea coastal, and Central Anatolian steppe basins. Results demonstrate that the RL-based planning approach reduced soil loss by an average of 23.7% ± 4.2% more than classical silvicultural methods (p < 0.001), with particularly pronounced superiority in watersheds characterized by heterogeneous topography and variable climatic conditions. Furthermore, the capacity of RL models to simultaneously optimize species spatial distribution and planting timing yielded significantly higher erosion control performance compared to classical approaches. The study provides important implications for the integration of AI-assisted decision support systems into sustainable forest management and watershed conservation strategies.
Kaan Alper (Sat,) studied this question.