Back to Search Start Over

The Marine Economy and Marine Industry in Qingdao Based on Lasso Regression Model

Authors :
He ZHANG
Mengxuan FAN
Source :
Haiyang Kaifa yu guanli, Vol 39, Iss 8, Pp 22-28 (2022)
Publication Year :
2022
Publisher :
Editorial Office of Ocean Development and Management, 2022.

Abstract

In order to promote the optimization of marine industry structure and the high-quality development of marine economy in Qingdao, this paper established the evaluation index system, analyzed the marine industry which had an important impact on the development of marine economy in Qingdao by using Lasso regression model, and put forward development suggestions. The results showed that the evaluation index system of Qingdao′s marine economic development level included 6 categories and 20 characteristic variables, which were marine fishery, marine chemical industry, total marine import and export, marine environment, marine transportation and coastal tourism. The optimal value of the adjustment coefficient was determined by 10 fold cross validation, and 10 characteristic variables were selected into the regression model by solving the Lasso regression coefficient. According to the estimated value of regression coefficient, the number of domestic passengers was the most important, the value of marine fishery output was important, and the value of cargo throughput was the least important. Lasso regression model had high prediction accuracy, model generalization ability and reliability, and belonged to robust sparse model. In the future, Qingdao should give full play to the industrial advantages of coastal tourism and marine fishery, focus on the development of marine transportation industry, protect the marine ecological environment and increase the investment in marine science and technology research and development.

Details

Language :
Chinese
ISSN :
10059857
Volume :
39
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Haiyang Kaifa yu guanli
Publication Type :
Academic Journal
Accession number :
edsdoj.fb2d71a42fef49529df6d802fb5efe9f
Document Type :
article