BORF: A Bayesian optimized random forest for prediction of aerosol extinction coefficient from Mie Lidar signal

In continuous observation signals of lidar, the identification and selection of effective signals are crucial, especially for the aerosol extinction coefficient retrieval. In this study, the Bayesian Optimized Random Forest (BORF) model, a machine learning approach combining Random Forest regression with Bayesian optimization, was developed for predicting aerosol extinction coefficients. Built upon the foundation of the Random Forest (RF) regression method, this model leverages Bayesian optimization to adjust model parameters precisely, significantly enhancing the accuracy of aerosol extinction coefficient predictions. This approach offers a valuable means to identify and screen anomalous Lidar signals. We constructed a training dataset comprising continuously observed Mie Lidar signals and aerosol extinction coefficients retrieved using the Klett method. The dataset contains dimensions, including Mie Lidar signals, detection time, detection distance, pressure, and temperature. This paper provides a detailed description of the BORF model’s establishment process and the optimization of model parameters using Bayesian optimization. Through model assessments, significance tests, and comparative experiments, we demonstrate the effectiveness of the BORF model. Experimental results indicate that, compared to other relevant models, the BORF model excels in predicting aerosol extinction coefficients, closely aligning with the accuracy of the Klett method. Specifically, in datasets with better data quality, the BORF model exhibits an approximately 4% increase in $R^2$ compared to the RF and BP neural network optimized by genetic algorithm (BPGA), accompanied by a 41% to 47% reduction in MSE and MAE. The Mean Squared Error (MSE) and Mean Absolute Error (MAE) decrease by approximately 40% to 90% in datasets with lower data quality and less apparent data variations. This study provides a robust technical solution to ensure the reliability of Lidar data, thereby contributing to an enhanced understanding of atmospheric aerosols and environmental monitoring.