Feasibility analysis for acquiring visibility based on lidar signal using genetic algorithm-optimized back propagation algorithm

• 来源期刊: 中国物理B（英文版）
• 关键词: visibility neural network lidar signals extinction coefficient
• 年，卷（期）: 2019,（2）
• 页码范围: 279-283
• 页数: 5页
• 语种: 英文
• DOI: 10.1088/1674-1056/28/2/024213

摘要

Visibility is an important atmospheric parameter that is gaining increasing global attention. This study introduces a back-propagation neural network method based on genetic algorithm optimization to obtain visibility directly using light detection and ranging (lidar) signals instead of acquiring extinction coefficient. We have validated the performance of the novel method by comparing it with the traditional inversion method, the back-propagation (BP) neural network method, and the Belfort, which is used as a standard value. The mean square error (MSE) and mean absolute percentage error (MAPE) values of the genetic algorithm-optimized back propagation (GABP) method are located in the range of 0.002 km2– 0.005 km2 and 1％–3％, respectively. However, the MSE and MAPE values of the traditional inversion method and the BP method are significantly higher than those of the GABP method. Our results indicate that the proposed algorithm achieves better performance and can be used as a valuable new approach for visibility estimation.