Abstract
Numerical simulation is an important method used in studying the evolution mechanisms of lake water quality. At the same time, lake water quality inversion technology using the characteristics of spatial optical continuity data from remote sensing satellites is constantly improving. It is, however, a research hotspot to combine the spatial and temporal advantages of both methods, in order to develop accurate simulation and prediction technology for lake water quality. This paper takes Donghu Lake in Wuhan as its research area. The spatial data from remote sensing and water quality monitoring information was used to construct a multi-source nonlinear regression fitting model (genetic algorithm (GA)-back propagation (BP) model) to invert the water quality of the lake. Based on the meteorological and hydrological data, as well as basic water quality data, a hydrodynamic model was established by using the MIKE21 model to simulate the evolution rules of water quality in Donghu Lake. Combining the advantages of the two, the best inversion results were used to provide a data supplement for optimization of the water quality simulation process, improving the accuracy and quality of the simulation. The statistical results were compared with water quality simulation results based on the data measured. The results show that the water quality simulation of chlorophyll a and nitrate nitrogen mean square errors fell to 17% and 24%, from 19% and 31% respectively, after optimization using remote sensing spatial information. The model precision was thus improved, and this is consistent with the actual pollution situation of Donghu Lake.
Highlights
The water quality issue has become serious, due to increasing eutrophication in shallow lakes [1]
The results show that the overall spatial distribution of total nitrogen (TN) inversion concentration is reasonable, and each sub-lake region is consistent with the actual situation, which verifies the reliability of the model
In this study of Donghu Lake, a heuristic intelligent algorithm has been applied in the field of empirical remote sensing inversion, and an improved back propagation (BP) neural network algorithm model based on the genetic algorithm (GA) is proposed
Summary
The water quality issue has become serious, due to increasing eutrophication in shallow lakes [1]. Yunfang Z et al [13] established a traditional BP-ANN model for Lake Taihu in Jiangsu province based on multi-spectral remote sensing from a multi-source satellite and real-time water quality data measured on the ground This was used to simulate the nonlinear regression relationship between the spectrum and the concentrations of water quality index parameters. Cao et al [15], working at Weishan Lake, built a variety of empirical and collection models to apply water quality inversion using hyperspectral multiphase data, the measured chlorophyll a, total suspended solids and turbidity data This proved that the particle swarm optimization algorithm combined with the support vector machine collection model produces the most accurate simulations. The mature numerical model of the hydrological environment is combined with the still-developing remote sensing inversion technology It can follow evolution mechanisms, but can improve the numerical simulations, producing accurate water quality results. The results of the optimization and traditional simulation are compared and analyzed
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