A Study on Estimating Theme Park Attendance Using the AdaBoost Algorithm Based on Weather Information from the Korea Meteorological Administration Web

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Jinkook Kim;Soohyun Kim
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引用次数: 0

Abstract

The purpose of this study is to propose an efficient machine learning model based on five years of data for Seoul Grand Park in Republic of Korea, depending on the weather and day characteristics, and to increase its effectiveness as a strategic foundation for national theme park management and marketing. To this end, the AdaBoost model, which reflects the characteristics of the weather and the day of the week, was recently compared with the actual number of visitors and the predicted number of visitors to analyze the accuracy. The analysis showed 30 days of abnormal cases, and the overall annual distribution was found to show similar patterns. Abnormal cases required details of wind speed, average relative humidity, and fine dust concentration for weather information, and it was derived that more accurate predictions would be possible considering variables such as group visitors, new events, and unofficial holidays.
基于韩国气象局网络天气信息,使用 AdaBoost 算法估算主题公园入园人数的研究
本研究的目的是根据大韩民国首尔大公园五年的数据,根据天气和日期特征,提出一个高效的机器学习模型,并提高其作为国家主题公园管理和营销战略基础的有效性。为此,最近将反映天气和星期特征的 AdaBoost 模型与实际游客人数和预测游客人数进行了比较,以分析其准确性。分析结果显示,有 30 天出现异常情况,整体年度分布也呈现出类似的规律。异常情况需要详细的风速、平均相对湿度和微尘浓度等天气信息,由此得出,考虑到团队游客、新活动和非官方假日等变量,可以进行更准确的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Web Engineering
Journal of Web Engineering 工程技术-计算机:理论方法
CiteScore
1.80
自引率
12.50%
发文量
62
审稿时长
9 months
期刊介绍: The World Wide Web and its associated technologies have become a major implementation and delivery platform for a large variety of applications, ranging from simple institutional information Web sites to sophisticated supply-chain management systems, financial applications, e-government, distance learning, and entertainment, among others. Such applications, in addition to their intrinsic functionality, also exhibit the more complex behavior of distributed applications.
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