Research On Forecasting Method Of Marine Chlorophyll Concentration Based On Argo Data

Algae,as a bait for marine life,have an important impact on marine fisheries.The estimation of algae biomass is generally done by measuring the concentration of chlorophyll in seawater.Therefore,the study of marine chlorophyll concentration has positive significance.On the other hand,with the rapid development of industry and agriculture,a large amount of production and domestic wastewater is discharged into the ocean,and the ocean is facing the threat of severe water pollution.Substances such as nitrate in wastewater can cause eutrophication of water bodies,and in severe cases will produce large-scale "red tide" phenomenon.The decomposition of the red tide will consume a large amount of oxygen in the water and produce toxic substances,which will have a serious impact on the survival of marine fish.Therefore,the detection of marine chlorophyll concentration can also be used as an indicator for judging water pollution.In this paper,marine and chlorophyll are studied in space and time.Study on the spatial distribution of marine chlorophyll concentration.In the past,marine chlorophyll concentration research was performed by satellite spectrum analysis and combined with machine learning related algorithms to retrieve chlorophyll concentration values.In this paper,by downloading the Argo(Array for Real-time Geostrophic Oceanography)data set,we directly obtain data about changes in the concentration ofchlorophyll in the ocean,use the GBDT regression algorithm,and use the original spatial distribution of ocean chlorophyll with latitude and longitude 1 ° × 1 °.The data is refined into a small grid of 0.1 ° × 0.1degrees,and the data visualization operation is implemented with matplab.The specific method is to use multiple linear regression models in the sklearn library to analyze chlorophyll data for regression rows,including SVR models,Bayesian regression models,ordinary linear regression models,elastic network regression models,and gradient enhanced regression models.According to multiple groups of models Compare the results,choose the best fitting enhanced gradient regression(GBDT)regression algorithm as the main algorithm in this experiment,and predict the marine chlorophyll concentration in a small range,showing the horizontal and vertical distribution of marine chlorophyll concentration.Study on the change of marine chlorophyll concentration with time.This paper uses the LSTM network to study the change of chlorophyll concentration in the ocean in time series,and predicts the change trend of chlorophyll concentration in time.The experimental results show that the LSTM model can predict well The change of chlorophyll concentration in the ocean provides an effective method for predicting the change of ocean chlorophyll concentration.