School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
J Environ Manage. 2022 Sep 15;318:115498. doi: 10.1016/j.jenvman.2022.115498. Epub 2022 Jun 18.
PM pollutants are seriously harmful to human health, which is of great significance for the forecasting of PM concentration. To accurately forecast hourly PM concentration, a new combination model based on agreement index variational mode decomposition (AIVMD), radial basis function neural network (RBF), induced ordered weighted averaging (IOWA) operator, long short-term memory neural network (LSTM) and error correction (EC), named AIVMD-RBF-IOWA-LSTM-EC, is proposed, which uses decomposition ensemble framework and error correction technique. Taking the reduction of reconstruction error in the process of VMD as the goal, an adaptive method to determine the mode number of VMD by agreement index (AI), named AIVMD, is proposed. Firstly, PM concentration data are decomposed into simple intrinsic mode function components (IMFs) by AIVMD to reduce the complexity of the data. Secondly, LSTM and RBF models are established for each IMF component, and the prediction results of each model are combined separately. Thirdly, an error correction model based on RBF is established to correct the prediction results. The predicted values of error are not only used to correct the prediction results, but also can be used as the induced value of IOWA operators to solve the weight allocation problem. Finally, the IOWA operator is used to weight the error correction prediction results, and the final result is obtained. To solve the problem that the forecasting accuracy of the combination model based on IOWA operators is low when the complementarity between single models is poor, a combination forecasting method with complementary disadvantage based on IOWA operators is proposed, which effectively improves the robustness of the model. A formula for calculating the proportion of complementary points is given. By solving the formula, the complementarity of the models can be judged, and the method of calculating the weight of the combined model can be selected accordingly. The proposed model is used to forecast PM concentration in Xi'an, and compared with the predicted results of contrast models. The results show that the proposed model has a great advantage in short-term forecasting of PM concentration.
PM 污染物对人类健康危害严重，对 PM 浓度的预测具有重要意义。为了准确预测小时 PM 浓度，提出了一种新的组合模型，该模型基于协议指标变分模态分解（AIVMD）、径向基函数神经网络（RBF）、诱导有序加权平均（IOWA）算子、长短期记忆神经网络（LSTM）和误差修正（EC），称为 AIVMD-RBF-IOWA-LSTM-EC，该模型使用分解集成框架和误差修正技术。以减少 VMD 过程中的重建误差为目标，提出了一种基于协议指标（AI）的自适应 VMD 模态数确定方法，称为 AIVMD。首先，通过 AIVMD 将 PM 浓度数据分解为简单的固有模态函数分量（IMFs），以降低数据的复杂性。其次，为每个 IMF 分量建立 LSTM 和 RBF 模型，并分别对每个模型的预测结果进行组合。第三，建立基于 RBF 的误差修正模型来修正预测结果。误差的预测值不仅用于修正预测结果，还可以用作 IOWA 算子的诱导值来解决权重分配问题。最后，使用 IOWA 算子对误差修正预测结果进行加权，得到最终结果。为了解决基于 IOWA 算子的组合模型在单一模型互补性较差时预测精度低的问题，提出了一种基于 IOWA 算子的具有互补劣势的组合预测方法，有效地提高了模型的稳健性。给出了计算互补点比例的公式。通过求解该公式，可以判断模型的互补性，并相应地选择组合模型的权重计算方法。将所提出的模型应用于预测西安的 PM 浓度，并与对比模型的预测结果进行比较。结果表明，所提出的模型在 PM 浓度的短期预测方面具有很大的优势。
