# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 1 Introduction
proposed a FLU-emission-spectroscopy-based COD detection method with the best model of \(R^{2}=0.9982\) and \(RMSE=0.5342\)[8]. However, the FLU method is easily affected by factors such as scattering, self-absorption, and temperature [9]. At high concentrations, fluorescence quenching and instability will greatly affect the predictive accuracy of the model. Since the deficiencies of the existing UV-vis spectrum and FLU spectrum cannot be well resolved by soft compensation, there still exist significant disadvantages when using a single spectrum to detect water organic pollution. The traditional detection of COD always involves a complex water environment and can be easily interfered with by turbidity and pH value, and thus the use of one technique in isolation may not provide sufficient information to enable accurate prediction. Multisensor data fusion is a process of combining methods and tools to merge data from different sources [10; 11] Spectral information fusion strategies are key techniques to effectively compensate for the shortcomings of different analytical instruments and comprehensively characterize the advantages of the analyte's chemical information, combining the data collection advantages of different instruments to obtain more accurate and superior test results. Distributed water COD detection networks often contain a large number of sensor nodes, which means there exist potential data collisions and redundant data in the process of data transmission. Sending the multispectral data after data fusion not only improves the detection accuracy but also effectively reduces the amount of data sent and saves sensor energy. Information fusion can be classified as dataset fusion, feature-level fusion, and decision-level fusion [12; 13; 14]. In recent years, data fusion has been widely used in various fields, such as wireless sensor networks, wireless cellular networks, robotics, video and image processing, intelligent system design, and fault diagnosis [15; 16; 17; 18; 19; 20]. [PERSON] et al. fused near-infrared spectra and mid-infrared data to quantitatively detect 10-HDA. Compared with the single NIR model results, the accuracy of the feature-level fusion model is improved from 0.8531 to 0.9585 [21]. [PERSON] et al. used spectral fusion technology to quantitatively analyze the impurities in honey. Compared with the \(R^{2}=0.945\) and 0.950 of the single mid-infrared and Raman spectra model, the data fusion accuracy can reach up to 0.998 [22]. [PERSON] et al. fused information from NIR, mid-IR, and Raman spectral data to analyze bread soluble starch, relative crystallinity, and hardness [10]. The RMSE of the model after multispectral fusion was reduced to 0.015, 0.787, and 1.290, respectively. These studies mentioned above have proved that multispectral information fusion technology can effectively improve the accuracy and stability of the model. Existing research on water quality spectral detection mainly focuses on single-spectrum analysis and model optimization. Decision-level data fusion performs decision allocation based on feature-level fusion models to obtain the final prediction result. With this method, the amount of spectral data and prediction accuracy can be significantly optimized. The application of decision-level data fusion has not yet been applied to COD detection in forest water quality and has attracted widespread attention. In this paper, the spectral method and information fusion technology are combined to realize the complementary advantages of multi-spectral detection, reduce the influence of single-spectrum modeling interference, and improve the accuracy of the final COD prediction. The research and construction of the forest water quality detection model also provide the theoretical and technical basis for the subsequent application of a distributed real-time remote monitoring system.