The ultraviolet-visible (UV-Vis) spectroscopy measurement method of Chemical Oxygen Demand (COD) in water is a simple physical method that can measure water without secondary pollution from chemical reagents. To solve the problems of low accuracy and insufficient generalization capability of the COD prediction model, an improved Bagging algorithm is proposed and evaluated in this study. The Improved-Bagging algorithm can reduce model variance and bias concurrently, and improves the accuracy and stability of the traditional Bagging algorithm. Results show that the Improved-Bagging algorithm achieves a better prediction ability on different preprocessed data than the traditional Bagging algorithm. After ensemble empirical mode decomposition based (EEMD-Based) algorithm denoising and stability competitive adaptive reweighted sampling (SCARS) algorithm dimension reduction, Improved-Bagging model achieves the best prediction performance. Its coefficient of determination (R ² ) on the prediction set reached 0.9317, its root mean square error of prediction (RMSEP) reached 5.39 mg/L, and its variance reached 5.53 mg ² . Results also show that the Improved-Bagging algorithm can accurately measure the COD concentration in water, which lays the foundation for the wide application of spectroscopy to measure water quality parameters.

Chemical oxygen demand (COD) is a critical parameter employed to assess the level of organic pollution in water. Accurate COD detection is essential for effective environmental monitoring and water quality assessment. Ultraviolet–visible (UV-Vis) spectroscopy has become a widely applied method for COD detection due to its convenience and the absence of the need for chemical reagents. This non-destructive and reagent-free approach offers a rapid and reliable means of analyzing water. Recently, deep learning has emerged as a powerful tool for automating the process of spectral feature extraction and improving COD prediction accuracy. In this paper, we propose a novel multi-scale one-dimensional convolutional neural network (MS-1D-CNN) fusion model designed specifically for spectral feature extraction and COD prediction. The architecture of the proposed model involves inputting raw UV-Vis spectra into three parallel sub-1D-CNNs, which independently process the data. The outputs from the final convolution and pooling layers of each sub-CNN are then fused into a single layer, capturing a rich set of spectral features. This fused output is subsequently passed through a Flatten layer followed by fully connected layers to predict the COD value. Experimental results demonstrate the effectiveness of the proposed method, as it was compared with three traditional methods and three deep learning methods on the same dataset. The MS-1D-CNN model showed a significant improvement in the accuracy of COD prediction, highlighting its potential for more reliable and efficient water quality monitoring.

In recent years, ultraviolet-visible (UV-Vis) spectroscopy has become one of the important methods used to measure water chemical oxygen demand (COD). However, environmental factors (pH, temperature, conductivity, etc.) can interfere with spectral information, thereby influencing the stability and accuracy of COD detection. The three environmental factors that influence UV-Vis spectroscopy were researched in this study. Considering the complexity of environmental factors, a data fusion method is proposed to compensate for the influence of three environmental factors simultaneously. This data fusion method is based on the weighted superposition of the spectrum and three environmental factors. A COD prediction model was established by fusing spectral feature wavelengths and environmental factors to reduce the influence of environmental factors on COD detection. Through the proposed data fusion method, the accuracy of COD detection based on UV-Vis spectroscopy has been improved. The determination coefficient of prediction ($R_{Pred}^2$) reaches 0.9602, and the root mean square error of prediction (RMSEP) reaches 3.52.

A one-dimensional multi-scale feature fusion CNN model was proposed to improve the detection accuracy of water COD based on UV-vis spectroscopy.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.