Journal of Dalian Maritime University >
2020 , Vol. 46 >Issue 3: 31 - 37
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2020.03.004
Research on intelligent water level prediction service system
Received date: 2020-01-31
Revised date: 2020-03-25
Online published: 2020-03-25
In order to improve the accuracy of water level prediction in inland waterway, the intelligent prediction of water level in inland waterway was studied by using depth neural network. A multi station linkage water level prediction model based on GRU cycle neural network was proposed. The experimental results on the water level observation data sets of several water level stations in the lower reaches of the Yangtze River at 8:00 in 30 years show that the model can make full use of the correlation of water level values between the upstream and downstream water level stations, reduce the impact of the randomness of single water level station data, and with a high prediction accuracy. The maximum average relative error value MRE of 5-day prediction is better than the classic ARIMA model, and also better than the GRU model of single water level station type. The prediction model is deployed by using TensorFlow Serving, and the intelligent water level prediction service system is developed by the front-end and back-end separation framework based on Spring Boot, Vue.js and other technologies. Each part of the system is deployed independently and connected through the RESTful API interface, which has good loose coupling and flexibility. The prediction results can be displayed in various forms, such as Web pages, APP and WeChat official account, which provides convenient intelligent services for inland river users.
PAN Ming-yang , ZHOU Hai-nan , LI Zeng-hui , LIU Yi-sai , LI Chao , LI Yu . Research on intelligent water level prediction service system[J]. Journal of Dalian Maritime University, 2020 , 46(3) : 31 -37 . DOI: 10.16411/j.cnki.issn1006-7736.2020.03.004
/
| 〈 |
|
〉 |