Estimation of ground-based atmospheric turbulence strength (C n2) by neural network architecture

• DOI: 10.1364/ao.532723
• Publication Date: 2024-09-09T15:00:20Z
• Authors: Ugurcan Celik; Huseyin Avni Yasar; Merve Yigitoglu K...; Cafer Bayar; Iklim Aslantas; Yakup Midilli
• Keywords: Atmospheric optics

Estimating the atmospheric turbulence strength ( C n 2 ) becomes significant in research field of electromagnetic radiation transmission through atmosphere, particularly optical waves. As increases, quality and these waves may decrease cause much trouble as they pass atmosphere. Throughout years, has been formulated by different groups for various geographical locations seasons using macro-meteorological variables empirically theoretically. However, since models are based on data collected from numerous places conditions, such deserts or coastal areas, do not provide accurate predictions our experimental site, demonstrated three well-known paper. In this study, a novel, to knowledge, artificial neural network (ANN) model named quadratic Fourier (QFNN) is trained estimate experimentally measured ground-based during winter season rural area. The gives reliable estimations, achieving value R =0.92 values.