Department of Informatics and Telecommunication, University of Ioannina, 45110 Ioannina, Greece.
Department of Computer Science, University of Pisa, 56126 Pisa, Italy.
Sensors (Basel). 2020 Jun 3;20(11):3181. doi: 10.3390/s20113181.
Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics.
航海旅程在很大程度上依赖于天气条件，因此气象学在航海业务中一直起着关键作用。如今，创新的机器学习方法的新时代，以及各种传感器和微控制器的可用性，为提供可靠的船舶近程主要气象变量实时预测提供了越来越多的前景。本研究的主要目标是提出一种用于实时天气预报的轻量级车载解决方案。该系统由一个商业气象站和一个工业物联网边缘数据处理模块组成，该模块在无需互联网连接的情况下计算风向和风速预测。选择回归机器学习算法是为了需要最少的资源（内存、CPU），并且能够在微控制器中运行。该算法是根据特定的条件和规范进行设计和编码的。该系统已经在静态气象站和测试航行期间的船舶上收集的真实天气数据上进行了测试。该系统的效率已经通过各种误差指标得到了证明。
