QUANTUM-ENHANCED FEATURE EXTRACTION (QUFEX): A CONCEPTUAL FRAMEWORK FOR PRECISION ATMOSPHERIC RIVER ANALYSIS

Atmospheric Rivers (ARs) are major contributors to global hydrological variability and flood hazards, particularly in regions influenced by monsoonal systems. Accurate detection of ARs remains a challenge due to their dynamic, non-linear spatial patterns. This study presents a comparative evaluation of three advanced deep learning models—U-Net, CGNet, and DeepLabV3+—and proposes Q-ARNet, a quantum-enhanced atmospheric river detection framework based on a Quantum Neural Network (QNN) for image segmentation. The experiments were conducted on a dataset comprising 574 AR events from 1951 to 2020, incorporating variables such as integrated water vapor (IWV), zonal and meridional wind components (U850, V850), and sea-level pressure (PSL). Q-ARNet outperforms current classical benchmarks by approximately 3.5% in accuracy while utilizing a substantially reduced input feature set, thereby addressing the computational bottleneck associated with high-dimensional climate data. The proposed framework achieves an average accuracy of 97.5 ± 0.3%, an Intersection over Union (IoU) of 94.5 ± 0.4%, and an F1-score of 95.7 ± 0.2%, consistently surpassing classical deep learning models. These findings highlight the potential of quantum neural networks to enhance both the representational capacity and computational efficiency of atmospheric modeling through quantum parallelism and entanglement. The study contributes to the growing intersection of quantum computing and climate informatics, offering a promising framework for improved flood forecasting and climate risk management.