Prediction Of Dissolved Oxygen Content In Shrimp Pond Based On Improved Adaboost Regression Algorithm

Chicken lobster is rich in high protein and has become a delicacy for tens of thousands of families.The market demand of chicken lobsters is also increasing year by year.In the process of chicken lobster breeding,the content of dissolved oxygen in water affects the growth,development and metabolism of chicken lobster,thus affecting the yield and specification of chicken lobster.This paper takes the water dissolved oxygen content data of chicken lobster breeding in some place of Anhui Province as the research object,uses the improved Adaboost regression algorithm to predict the water dissolved oxygen content,constructs the water dissolved oxygen prediction model,develops the chicken lobster water dissolved oxygen prediction system,and helps chicken lobster farmers better control the dissolved oxygen content in the breeding pond by comparing the water dissolved oxygen prediction value with the historical data,which provide scientific guidance for chicken lobster breeding.The main work and research results are as follows:(1)Aiming at the problem that dissolved oxygen can not be accurately evaluated due to the influence of many factors,the prediction method of random forest improved Adaboost regression algorithm is adopted.In the process of chicken lobster breeding,the change of environmental factors will cause the change of dissolved oxygen content in water quality.The environmental factors of dissolved oxygen in water quality include:ambient temperature,ambient air pressure,water depth,water temperature,wind speed,date,etc.the prediction of dissolved oxygen in water quality is a continuous prediction problem,and the regression prediction method needs to be used to predict the content of dissolved oxygen.By studying the regression prediction model and according to the influencing factors in the prediction process of dissolved oxygen in water quality,the random forest algorithm is used to improve the prediction method of dissolved oxygen in chicken lobster breeding water quality based on Adaboost regression algorithm.(2)The dissolved oxygen prediction model of water quality based on Adaboost regression algorithm is constructed.The random forest algorithm and Adaboost regression algorithm are combined to predict the dissolved oxygen content of water quality.The improved dissolved oxygen content prediction model was trained and tested by collecting the data of time,ambient temperature,ambient air pressure,water depth,water temperature,wind speed,date and dissolved oxygen from July to November in a chicken lobster breeding base in Anhui Province.The experimental results show that the prediction ability of the water quality dissolved oxygen prediction model constructed in this paper is better than random forest regression,supporting vector machine regression,k nearest neighbor regression,gradient lifting decision tree regression,extreme random tree regression and so on.By using the prediction and evaluation index of dissolved oxygen content,the effectiveness of the water quality dissolved oxygen prediction model is verified.(3)Develop a water quality dissolved oxygen prediction system for chicken lobster.By installing water quality sensors and meteorological observation points in a chicken lobster breeding base in Anhui Province,the real-time data collected by the sensor equipment are obtained,and the collected data are stored and displayed;At the same time,combined with the constructed water quality dissolved oxygen prediction model and technologies such as Spring Boot,Spring,security,Bootstrap and ECharts,an interactive water quality dissolved oxygen prediction system for chicken lobster based on Web page is developed to predict the content of dissolved oxygen in water quality;The predicted data of dissolved oxygen in water quality are displayed on the web page and compared with the historical data to help farmers better manage the water quality of lobster pond.