In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.

中文翻译：

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基于主成分分析的基于机器学习的预测模型的降维

在这项研究中，人们试图将小波-ANFIS / ANN（人工神经网络/自适应神经模糊推理系统）模型的尺寸朝着可靠的预测方向发展，并降低了计算成本。在这方面，对由离散小波变换分解的输入时间序列进行主成分分析，以馈入ANN / ANFIS模型。该模型用于河流中的溶解氧（DO）预报，这是影响水生生物和水质的重要变量。DO的当前值，水表面温度，盐度和浊度已被视为输入变量，可以进一步在三步内预测DO。研究结果表明，PCA可以用作减少输入变量的维数以及检测输入变量的相互关系的有力工具。将PCA小波ANN模型的结果与从小波ANN模型获得的结果进行比较，而较早的模型则具有比后一种模型更少的计算时间的优势。在处理ANFIS模型时，PCA对于避免小波ANFIS模型创建过多规则会降低ANFIS模型的效率更为有利。此外，利用PCA处理小波ANFIS模型会导致计算时间显着减少。最后，发现PCA小波ANN / ANFIS模型可以提供可靠的溶解氧预报，作为河流中重要的水质指标。将PCA小波ANN模型的结果与从小波ANN模型获得的结果进行比较，而较早的模型则具有比后一种模型更少的计算时间的优势。在处理ANFIS模型时，PCA对于避免小波ANFIS模型创建过多规则会降低ANFIS模型的效率更为有利。此外，利用PCA处理小波ANFIS模型会导致计算时间显着减少。最后，发现PCA小波ANN / ANFIS模型可以提供可靠的溶解氧预报，作为河流中重要的水质指标。将PCA小波ANN模型的结果与从小波ANN模型获得的结果进行比较，而较早的模型则具有比后一种模型更少的计算时间的优势。在处理ANFIS模型时，PCA对于避免小波ANFIS模型创建过多规则会降低ANFIS模型的效率更为有利。此外，利用PCA处理小波ANFIS模型会导致计算时间显着减少。最后，发现PCA小波ANN / ANFIS模型可以提供可靠的溶解氧预报，作为河流中重要的水质指标。PCA更有利于避免小波ANFIS模型创建过多的规则，从而降低ANFIS模型的效率。此外，利用PCA处理小波ANFIS模型会导致计算时间显着减少。最后，发现PCA小波ANN / ANFIS模型可以提供可靠的溶解氧预报，作为河流中重要的水质指标。PCA更有利于避免小波ANFIS模型创建过多的规则，从而降低ANFIS模型的效率。此外，利用PCA操纵小波ANFIS模型可显着减少计算时间。最后，发现PCA小波ANN / ANFIS模型可以提供可靠的溶解氧预报，作为河流中重要的水质指标。