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 km^2 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.
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 km^2 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.
基金
Project supported by the National Natural Science Foundation of China(Grant No.41405014)
