High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R² values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

在马来西亚，经常报告对流层臭氧浓度超过允许水平。这项研究提出了一种基于机器学习算法的准确模型，以预测位于马来西亚吉隆坡和雪兰莪州的主要城市的对流层臭氧浓度。拟议的模型是使用三年历史数据针对不同参数开发的，以预测对流层臭氧浓度的24小时和12小时为输入。已经研究了不同的机器学习算法，即。线性回归，神经网络和增强决策树。结果表明，风速，湿度，一氧化氮，一氧化碳和二氧化氮对臭氧的形成有显着影响。增强决策树的性能优于所有站的线性回归和神经网络算法。对于三个调查站，R ²值分别等于0.91、0.88和0.87。为了评估Boosted Decision Tree模型的不确定性，引入了95％的预测不确定性（95PPU）d因子.95PPU显示约94.4、93.4、96.7％，与S1，S2相关的d因子分别为0.001015、0.001016和0.001124。和S3分别。获得的结果为模拟马来西亚不同地区的实际臭氧浓度提供了可靠的预测模型。