# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 1 Introduction  
Lake wetlands are ecosystems with multiple ecological functions, which are crucial to the sustainable development of human beings and the environment. In recent years, global warming, population activities, and irrational exploitation of natural resources [1] have triggered a series of problems such as shrinking area, fragile aquatic ecosystems, and deterioration of water quality in domestic lakes and wetlands. Chl-a is an important carrier of photosynthesis in algal organisms, and its concentration is an important indicator for assessing the degree of algae and eutrophication in water bodies [2, 3]; therefore, the inversion of Chl-a concentration is of great significance for detecting the water quality of water bodies as well as the protection of lake wetlands. With the rapid development of satellite remote-sensing technology, satellite remote-sensing data are widely used in water quality detection for their high accuracy, multi-band, and map integration [4]. This greatly improves the estimation accuracy of numerical parameters and contributes to the ecological environmental protection and sustainable development of water bodies [5]. For example, [PERSON] et al. [6] used the red-peak envelop area method (PEA) to invert the Chl-a concentration in the Pearl River based on the Sentinel-3 satellite data of the Pearl River, and compared with algorithms such as reflectance peak algorithm and fluorescence baseline height method, the results show that the PEA method is the best with a R\({}^{2}\) of 0.74 and a RMSE of 0.12 mg/m\({}^{3}\). [PERSON] et al. [7] used the Pearl River One hyperspectral satellite to build a three-band model to invert the Chl-a concentration of Chaohu Lake, and it was found that the three-band model [Rrs(700 nm)-1--Rrs(670 nm)-1] \(\times\) Rrs(746 nm) constructed from the 14, 16, and 19 bands of the Zhuhai One OHS-2A star image achieved a high degree of accuracy, with the relative error and the RMSE were 19.97% and 10.85 mg/m\({}^{3}\), respectively. However, the traditional inversion algorithm for Chl-a concentration has a strong dependence on the quality of spectraldata, is easily affected by the concentration of other substances in the water, and has a poor inversion effect in the face of a more complex water body. With the development of artificial intelligence, such as neural networks, support vector machines, random forests, and other artificial intelligence-based algorithms are widely used in solving nonlinear problems in waters [8]. Numerous existing scholars have studied the inversion of Chl-a concentration in marine or inland waters through remote-sensing images combined with AI-based algorithms. For example, [PERSON] et al. [9] constructed a Chl-a concentration inversion model for the clean water of Qiandao Lake based on Gaofen-1 satellite remote-sensing images and a neural network model, and the neural network model has a better effect on the inversion of the inland clean water with a low Chl-a content compared to other conventional methods. [PERSON] et al. [10] used extreme gradients and inversions to estimate the Chl-a concentration of eight lakes in eastern China based on Landsat-8 remote-sensing data. Remote-sensing data use the machine-learning method of extreme gradient augmented tree to propose an algorithm for inversion of Chl-a concentration in turbid lakes by broadband instruments such as OLI. [PERSON] et al. [11] selected multiple regression, BP neural network, and random forest methods based on GF-1 WFV data to establish the inversion concentration of Chl-a concentration in the downstream of Minjiang River, respectively, and the results showed that the inversion accuracy of the random forest model was optimal. [PERSON] et al. [12] proposed a genetic algorithm combined with an artificial neural network as a machine-learning method to invert the Chl-a concentration, and also compared with the three-band model; the results show that the machine-learning model performance is overall better than the three-band model under three different remote-sensing datasets.