Abstract
This paper aims to present a desirable prediction method for oceanographic trends. Therefore, an online monitoring scheme was prepared to collect the accurate oceanographic hydrological data based on wireless sensor network (WSN) and computer technology. Then, the data collected by the WSN were processed by support vector regression algorithm. To obtain the most important parameters of the algorithm, the particle swarm optimization was introduced to search for the global optimal solution through the coopetition between the particles. After that, an oceanographic hydrological data collection and observation system was created based on the hydrological situation of New York harbour. Then, the traditional support vector regression and the proposed method were applied to predict the oceanographic trends based on water temperature, salinity and other indices. The results show that the proposed algorithm enhanced the data utilization rate of the WSN, and achieved good prediction accuracy. The research provides important insights into the application of advanced technology in oceanographic forecast.
Highlights
Since its nascence in the late 1990s, the wireless sensor network (WSN) has developed into a collaborative network capable of collecting and processing object information and transmitting the processed information to users
We present an online monitoring scheme by using WSN technology and computer technology, so as to collect hydrological data effectively
This paper uses the method of support vector regression to process the data collected by the wireless sensor
Summary
Since its nascence in the late 1990s, the wireless sensor network (WSN) has developed into a collaborative network capable of collecting and processing object information and transmitting the processed information to users. In China, scholars have explored the WSN-based real-time monitoring systems for water resources [7]. It is not surprising that the WSN has been introduced to the monitoring of oceanographic hydrological data. These data have long been the focal point in theoretical and empirical research. This paper prepares an online monitoring scheme that can effectively collect oceanographic hydrological data based on the WSN and computer technology. The data collected by the WSN were processed by support vector regression algorithm. The traditional support vector regression and the proposed method were applied to predict the oceanographic trends based on water temperature, salinity and other indices. The results show that the proposed algorithm enhanced the data utilization rate of the WSN, and achieved good prediction accuracy
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have