Abstract
Chlorophyll-a (Chl-a) accurate inversion in inland water is important for water environmental protection. In this study, we tested the Genetic Algorithm optimized Back Propagation (GA-BP) neural network model to precisely simulated the Chl-a in an inland lake using Landsat 8 OLI images. The result show that the R2 of GA-BP neural network model has increased 28.17% compared to traditional BP neural network model. Then this GA-BP model was applied to another two scenes of Landsat 8 OLI image with the R2 of 0.961, 0.954 respectively for March 26 2018, October 26 2018. And the spatial distribution have shown a reasonable result of Chl-a variation in Lake Donghu. This study can provide a new method for Chla concentration inversion in urban lakes and support water environment protection on a large scale.
Highlights
Water environment quality is increasingly a concerning problem in recent years (Hanson et al 2016).Due to the long-term and frequent influence of human activities, many urban lakes are seriously troubled with eutrophication all the year round
The environment of eutrophication is suitable for algae grows, leading to water bloom and causing serious damage to the lake ecosystem
As a complement to traditional monitoring, remote sensing technology was applied for deriving water quality parameters
Summary
Water environment quality is increasingly a concerning problem in recent years (Hanson et al 2016).Due to the long-term and frequent influence of human activities, many urban lakes are seriously troubled with eutrophication all the year round. Data-driven technology is taking the emerging computing intelligence as the main development direction, such as artificial neural network, genetic computing and fuzzy systems They simulate human intelligent activities from different perspectives and provide new insights into the evolution mechanism of ecosystems. Based on the measured water quality data of Lake Donghu, the Genetic Algorithm – Back Propagation (GA-BP) neural network Chl-a prediction model was constructed and the results were compared and analyzed in this study. This can provide a theoretical basis for the further use of water informatics of aquatic environment in inland waters
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