FAST HYPERSPECTRAL PREDICTION AND INVERSION MODEL OF AQUACULTURE WATER QUALITY BASED ON CSA-PLS ALGORITHM
The quality of aquaculture water directly affects the growth of aquatic products,and it is of great significance to accurately,quickly and comprehensively control the water quality parameters changes of the aquaculture water environment.The traditional monitoring methods of water quality index are all by means of manual sampling,which not only takes a long time,but also can only reflect the local water conditions.To solve these problems,a crow search algorithm(CSA)combined with partial least squares regression(PLSR)was proposed to select hyper spectral characteristic bands,which can quickly predict and invert spectral data.In this paper,we collected water samples and took hyperspectral image data of the same period in a contiguous aquaculture zones.Firstly,various data transformation methods are applied to preprocess the sampling points spectral data extracted;Secondly,using these data,the SVR and AdaBoost regression models are separately constructed for the water quality indicators:Total Nitrogen(TN),Ammonium Nitrogen(NH4+-N),Total Phosphorus(TP),and Chemical Oxygen Demand(COD)across the entire spectrum.At the same time,the model is compared with the model constructed after the proposed CSA-PLS automatic band screening method and the traditional Successive Projections Algorithm(SPA)band screening method;finally,the best models suitable for each water quality indicator are chosen based on the coefficient of determination(R2)and root mean square error(RMSE).From the experimental results,it could be seen that the TN,NH4+-N,TP and COD prediction models that trained by the proposed waveband selection method and the Adaboost model perform better than SVR in predicting TN,NH4+-N,TP,and COD.The optimal prediction model for these parameters outperforms the traditional SPA band selection method in terms of evaluation criteria R2 and RMSE,compared with the optimal model using full spectra,the TN prediction improved by 18.32％and 10.73％;NH4-N improved by 17.42％and 11.19％;COD improved by 2.15％and 2.54％.The results indicate that the prediction and inversion model based on CSA-PLS and AdaBoost is effective and feasible,which provides a new data acquisition model for real-time and accurate early warning and regulation of aquaculture water environment.
hyperspectral datawater quality model predictioncrow search algorithm(CSA)aquaculture water environmentintegrated learning
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