# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 1 Introduction  
As a key element in mitigating climate change and achieving carbon neutrality, forest ecosystems also play an important role in protecting water sources. This can not only prevent soil erosion and land degradation, but also provide a large number of basic resources such as rivers, lakes, and groundwater for agriculture, industry, and cities. However, with the rapid growth of the population, excessive consumption and development, industrial pollution, and deforestation, forest ecology is facing severe challenges. The COD value reflects the pollution degree of the water environment by organic matter, and the concentration is usually expressed in mg/L [1, 2]. The detection of COD in forest water is often used to monitor the pollution around forest lands to assess the impact of human activities on forest ecosystems. High COD values generally indicate the presence of high amounts of organic matter in the water from sources such as human activities or biological activity in natural forest ecosystems. Real-time monitoring of COD values is helpful to provide basic data for forest ecosystem protection [3]. For example, COD detection can evaluate the content and distribution of organic matter in forest land to help optimize soil management and improve soil quality. In addition, it can monitor the pollution of water bodies around forest lands to assess the impact of human activities (such as agriculture, tourism, and urbanization)on forest ecosystems. With the increased awareness of the need for environmental and forest conservation, the rapid, accurate, and non-secondary pollution detection of COD content in the water has attracted significant attention. In recent years, the number of water quality monitoring stations across the country has increased, reflecting the determination of our country to manage water quality in the ecological environment. Existing COD tests are mainly based on chemical methods represented by the dichromatic method and rapid dissipation photometric methods. Although traditional detection methods may be reliable, they are limited by the need for complex sample pre-treatment and are not suitable for online analysis. The chemical method inevitably requires manual on-site sampling, laboratory testing, and result analysis. This not only increases the detection period but also requires more manpower and material resources, especially in geographically complex mountain environments. At the same time, the problem of secondary contamination from chemical reagents during sample measurement cannot be ignored [4]. With the continuous development of modern science and technology, the distributed real-time detection system has developed by favoring intelligence, miniaturization, and networking. To overcome the bottleneck of chemical methods, high-precision, and real-time detection has become a hot spot in water COD detection research. Spectroscopic analysis provides effective information on the physical and chemical properties of samples and has been widely used in qualitative and quantitative research of substances [5]. More importantly, it facilitates real-time detection and system integration. UV-vis spectroscopy uses the characteristic absorption of various organic and inorganic substances in the water to determine the concentration and establishes the relationship between wavelength absorbance and water quality parameters [6]. Based on a deep learning algorithm, [PERSON] et al. chose the 188 to 915 nm band in the UV-vis spectrum to establish a predictive model [7], wherein the \(R^{2}\) and RMSE could reach 0.9991 and 3.8745, respectively. In recent studies, although people have updated the algorithm in the modeling process, the predictive error of UV-vis spectroscopy is still relatively large due to the existence of matter in water. FLU spectroscopy can also calibrate the content of COD through the FLU intensity of organic matter in water. Compared with the UV-vis method, it can achieve higher sensitivity and resolution during measurement. [PERSON] et al.