# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 5 Conclusions  
In this paper, the XGBoost model was optimized to invert the Chl-a concentration in Wuliangsu Lake using the Sentinel-2 remote-sensing image data, while the month information was used as one of the input features, using OGolden-DBO. The results show that, compared with other optimizations, the XGBoost model of OGolden-DBO has the best inversion effect, and can accurately invert the Chl-a concentration in Wuliangsu Lake.  
Comprehensive analysis of this paper reveals that under the existing system of agricultural development, farmers are decentralizing their farming and gradually replacing farmyard manure with chemical fertilizers. This leads to a variety of agricultural surface pollution, and irregular and random emission points. This type of surface pollution from agriculture causes the concentration of Chl-a in Wuliangsu Lake to rise dramatically in August and September, intensifying the degree of eutrophication. Water quality is also impacted by climate change at the same time. The OGolden-DBO-XGBoost model can help address the water quality issues facing Wuliangsu Lake by offering timely and accurate monitoring and early warning of changes in water quality, aiding in the management of water quality, making data from water quality testing publicly available, raising public awareness of the need to protect Wuliangsu Lake, and providing scientific data support for environmental agencies and the government to develop effective policies for agriculture and water resource management. In summary, the OGolden-DBO-XGBoost model can accurately invert the concentration of Chl-a. It is also a useful tool for maintaining the biological balance of the lake and encouraging its sustainable expansion.  
However, the data used to train the model were limited due to weather, frequency of satellite transit, etc. In future work, more data need to be taken to further improve the accuracy and reliability of the model. Meanwhile, the possible correlation between the bands increases the complexity of the model, so future studies will select specific bands and reduce the complexity of the model to predict the Chl-a concentration in Wuliangsu Lake more accurately.  
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z.; formal analysis, H.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, X.F. and H.L.; visualization, H.Z.; supervision, X.F. and H.L.; project administration, X.F. funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (62041211, 61962047), Inner Mongolia Autonomous Region Science and Technology Major Special Project (2021 ZD0004), Science and Technology Programme of Inner Mongolia Autonomous Region (2022 YFHH0070), Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (NMGIRT2313), Basic Research Operation Funds for Universities under Inner Mongolia Autonomous Region (BR22-14-05), Collaborative Innovation Project of Universities and Institutes in Hohhot (XTCX2023-20, XTCX2023-24). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflict of interest.