People give more importance concerning the overall quality of the modernized ecosystem. The pollution of air is one of the significant problems to be resolved as it restricted the ecological transformation of the modernized ecosystem. Therefore, it is fundamental to evaluate the implication of these ecological issues to enhance the urban ecosystem. This vital purpose of this research is to propose a canonical correlation analysis based hyper basis feedforward neural network classification (CCA-HBFNNC) model for evaluating sustainable urban environmental quality. The CCA-HBFNNC model initially acquires a large size of U.S. air pollution dataset as input. Then, a canonical correlative analysis based feature selection algorithm is applied in the CCA-HBFNNC model to select the key pollutant features, which bear fundamental implications to the modernize air pollution to maintain the level of urban sustainability. After the feature selection process, the CCA-HBFNNC model applies the HYPER BASIS FEEDFORWARD NEURAL NETWORK CLASSIFICATION (HBFNNC) algorithm in order to classify input air data based on chosen pollutants features. During the classification process, the HBFNNC algorithm used three critical layers namely hidden, output and input layers for efficiently categorizing each input data as higher or lower pollution level with higher accuracy. If the level of air pollution on the urban environment is higher, finally CCA-HBFNNC model significantly reduces the pollution level. In this way, the CCA-HBFNNC model attains improved urban sustainability levels when compared to sophisticated operation. An experimental evaluation of the CCA-HBFNNC model is determined in terms of CCA-HBFNNC model, time complexity and false-positive rate in consideration of the diversified number of air data retrieved from the big data sets. An investigational result shows that the proposed CCA-HBFNNC model can increases the sustainability level and minimizes the time complexity of urban development when contrasted with contemporary works.

人们对现代化生态系统的整体质量更加重视。空气污染是要解决的重要问题之一，因为它限制了现代化生态系统的生态转型。因此，评估这些生态问题对改善城市生态系统的意义至关重要。这项研究的重要目的是提出一种基于规范相关分析的超基础前馈神经网络分类（CCA-HBFNNC）模型，用于评估可持续城市环境质量。CCA-HBFNNC模型最初获取了大量的美国空气污染数据集作为输入。然后，在CCA-HBFNNC模型中应用基于规范相关分析的特征选择算法，选择关键污染物特征，这对保持现代化空气污染对维持城市可持续发展水平具有根本意义。在特征选择过程之后，CCA-HBFNNC模型应用超基本前馈神经网络分类（HBFNNC）算法，以便根据选定的污染物特征对输入空气数据进行分类。在分类过程中，HBFNNC算法使用了三个关键层，即隐藏层，输出层和输入层，以将每个输入数据有效地分类为较高或较低的污染等级，并且具有较高的准确性。如果城市环境中的空气污染水平较高，则最终CCA-HBFNNC模型将大大降低污染水平。这样，与复杂的操作相比，CCA-HBFNNC模型可以提高城市的可持续发展水平。考虑到从大数据集中检索到的空气数据的多样性，根据CCA-HBFNNC模型，时间复杂度和假阳性率确定了CCA-HBFNNC模型的实验评估。研究结果表明，与当代作品相比，所提出的CCA-HBFNNC模型可以提高可持续性水平，并最小化城市发展的时间复杂性。