A Physics‐Informed Reinforcement Learning Framework for Predictive Evaluation of Cloud Seeding Effectiveness

ABSTRACT Cloud seeding has emerged as a promising weather modification technique for enhancing precipitation in arid and semi‐arid regions; however, accurately evaluating its effectiveness under dynamic atmospheric conditions remains challenging due to the nonlinear behaviour of precipitation processes and the complexity of aerosol–cloud interactions. Existing statistical and machine learning approaches often fail to incorporate atmospheric physical constraints, resulting in unstable prediction performance and limited generalization capability. To address these limitations, this paper proposes a hybrid framework termed Regression‐based Physics‐Informed Bellman Reinforcement Learning (R‐PIBRL) for predictive evaluation of cloud seeding effectiveness. The proposed framework integrates atmospheric feature engineering, change‐point detection, physics‐informed learning, and reinforcement learning within a unified predictive architecture. Initially, Target‐Control Regression and Mann–Kendall Change Point Detection are employed to extract significant atmospheric transition patterns from the United Arab Emirates (UAE) weather dataset. The engineered features are subsequently incorporated into a two‐stage Physics‐Informed Bellman Reinforcement Learning model consisting of physics‐guided pre‐training and Bellman‐based fine‐tuning. The physics‐informed component embeds aerosol–cloud–precipitation constraints, while the reinforcement learning stage enhances adaptive prediction under dynamic weather variations. Experimental results conducted on the UAE weather dataset demonstrate that the proposed R‐PIBRL framework achieves improved prediction accuracy, lower false positive rate, and reduced prediction error compared with conventional baseline approaches, including Gradient Boosted Decision Trees (GBDT) and existing cloud seeding prediction models, with prediction accuracy improvements of approximately 5%–12% under dynamic atmospheric conditions. The proposed method provides a computationally efficient and physically consistent framework for predictive cloud seeding assessment and weather modification analysis in dynamic meteorological environments.