An intelligent modeling framework to optimize the spatial layout of ocean moored buoy observing networks

Author:

Liu Shixuan,Song Miaomiao,Chen Shizhe,Fu Xiao,Zheng Shanshan,Hu Wei,Gao Saiyu,Cheng Kaiyu

Abstract

This research is motivated by the practical requirements in the sustainable deployment of ocean moored buoy observing networks. Ocean moored buoys play an important role in the global marine environment monitoring. Ocean buoy station layout planning is a typical multiple-objective spatial optimization problem that aims to reduce the spatial correlation of buoy stations and improve their spatial monitoring efficiency. In this paper, we develop a multi-objective mathematical model for allocating ocean buoy stations (MOLMofOBS) based on Tobler’s first law of geography. A spatial neighborhood model based on a Voronoi diagram is built to represent the spatial proximity of distributed buoy stations and delimit the effective monitoring region of every station. Then, a heuristic method based on a multiple-objective particles swarm optimization (MOPSO) algorithm is developed to calculate the MOLMofOBS via a dynamic inertia weight strategy. Meanwhile, a series of experiments is conducted to verify the efficiency of the proposed model and algorithms in solving single- and multiple-buoy station location problems. Finally, an interactive portal is developed in the Cyberinfrastructure environment to provide decision-making services for online real-time planning of the ocean buoy station locations. The work reported in this paper will provide spatial decision-making support for the sustainable development of ocean buoy observing networks.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Frontiers Media SA

Subject

Ocean Engineering,Water Science and Technology,Aquatic Science,Global and Planetary Change,Oceanography

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Developing an Artificial Intelligence-Based Method for Predicting the Trajectory of Surface Drifting Buoys Using a Hybrid Multi-Layer Neural Network Model;Journal of Marine Science and Engineering;2024-06-07

2. Comparative Analysis of Hydrodynamic Performance of Small Wave Buoys;Journal of Physics: Conference Series;2023-12-01

3. Research on Anomaly Detection of Offshore Buoy Water Temperature Data Based on LSTM-DAE Model;2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI);2023-12-01

4. YOLO-FE: A Lightweight Ship Detection Algorithm Based on Improved YOLOv8;2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI);2023-12-01

5. A Trajectory Prediction Method for Drifting Buoy Based on GA-DNN Model;2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI);2023-12-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3