Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
Source abstract
Abstract
Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model’s coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake.
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Research context
OpenAlex topics: Water Quality Monitoring and Analysis · Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China
Funding
National Natural Science Foundation of China · Inner Mongolia Autonomous Region Science and Technology Major Special Project · Science and Technology Programme of Inner Mongolia Autonomous Region · Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region · Basic Research Operation Funds for Universities under Inner Mongolia Autonomous Region · Collaborative Innovation Project of Universities and Institutes in Hohhot
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