Retraction Note: Short-term rainfall monitoring in mountainous area based on Bayesian model and mental health intervention of college students

Abstract

Lifelong learning algorithm based on Bayesian model can receive data continuously and update the model dynamically in real time. It is suitable for the processing of scalability and data flow pattern. It is one of the hot spots in the field of machine learning. Aiming at the problem of continuous learning of data stream, this paper studies Bayesian model design, parameter reasoning, and multi-data processing. Then, according to the product operation of wavelet transform and short-term monitoring support vector machine, the precipitation dynamic estimation method of rainfall radar multiplier in mountainous area is proposed. Based on the precipitation data of East China in 2019 and the rainfall data observed by radar in mountainous areas, and taking the estimated time as the reference, the radar reflection and rainfall data in mountainous areas in the first 30 min are selected and converted into wavelet field as the training data, and then the dynamic precipitation estimation model is established by using vector medium, and the precipitation in mountainous areas is estimated at 6-min intervals. Finally, this paper makes an intervention study on college students’ mental health. The research object is a university student. The subjects are divided into two groups by using random clustering samples. There were 779 people in the experimental group; the other group was the control group, with a total of 355 people. In order to improve their mental health literacy, we should change the implementation of mental health education curriculum. The changes of dependent variables include self-reported mental health literacy, self-efficacy of coping with psychological problems, and behavior attitude of seeking psychological help. Based on the above Bayesian model and intervention research, this paper intervenes the mental health education of college students, so as to improve the mental health quality of college students.