（JCP 2019 Forecast）Novel analysiseforecast system based on multi-objective optimization for air quality index

可参考的表达方式：

Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results.

以往的研究主要集中在提高预测的准确性或稳定性上，不能同时提高这两方面，结果不尽人意。

 

Therefore, developing an efficient and reliable analysiseforecast system is still desirable.

因此，开发一套高效、可靠的分析预报系统仍然是值得的。

 

In this study, a novel analysise-forecast system was proposed that overcomes the shortcomings mentioned above and is intended to be a powerful and efficient technique for air quality analysis and monitoring.

本研究提出了一套全新的分析预报系统，克服了上述不足之处，为空气质量分析与监测提供了一种强大而有效的技术。

 

In the developed system, complexity analysis based on sample entropy was proposed as the first step in system analysis to mine the information in AQI. 

在已开发的系统中，提出了基于样本熵的复杂性分析作为挖掘AQI中的信息的系统分析的第一步。

 

The developed analysiseforecast system was evaluated on hourly AQI series from eight cities, and several performance metrics (i.e., MAPE, MAE, MSE, PMAPE, PMAE, PMSE) were calculated.

开发的分析预报系统在八个城市的每小时AQI序列上进行了评估，并计算了几个性能指标(即MAPE, MAE, MSE, PMAPE, PMAE, PMSE)。

 

The experimental results indicated that the proposed hybrid forecasting model is superior to comparison models with the smallest MAPE of 3.72%, 2.70%, 4.89%, 3.09%, 5.86%, 3.58%, 2.24%, and 5.42%, respectively, in the eight datasets.

实验结果表明，提出的混合预测模型优于对比模型，在8个数据集中，最小的MAPE分别为3.72%、2.70%、4.89%、3.09%、5.86%、3.58%、2.24%和5.42%。

 

Thus, the developed system can be a powerful tool for decision-makers in monitoring and forecasting air quality.

因此，所开发的系统可作为决策者监测和预测空气质量的有力工具。