基于机器学习的空间数据开发,用于优化印度尼西亚的天文观测站点.
Anjar Dimara Sakti1, Muhammad Rizky Zakiar2, Cokro Santoso2
1Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia.
PloS one
|October 20, 2023
概括
这项研究通过整合物理,大气和污染数据来确定印尼天文台的最佳位置. 在整个群岛推18个地点,以推进天文学研究和活动.
科学领域:
- 天文学 天文学
- 地理空间科学 地理空间科学
- 环境科学 环境科学
背景情况:
- 天文天文台的建设对于研究,教育和旅游业至关重要.
- 印度尼西亚多样化的地理形态为观测站的定位带来了独特的挑战和机会.
- 之前的研究往往缺乏对物理,大气和污染因素的综合分析.
研究的目的:
- 开发印尼天文台的位置分布场景.
- 将物理和大气适应性指数与机器学习和气候模型相结合.
- 在最佳的观测站建设场景中考虑低空气污染风险的公平分配.
主要方法:
- 整合物理和大气观测站索引的适用性分析.
- 机器学习模型和长期气候模型的应用.
- 基于经度/度划分和空气污染风险评估的位置分布场景.
主要成果:
- 印度尼西亚总体上表现出高适宜性和低空气污染风险,但某些地区除外.
- 爪,东苏门答腊和加里曼坦部分地区的天文适用性低,空气污染风险高.
- 在苏门答腊,爪,加利曼坦,努萨丁加拉,苏拉威西和巴布亚地区确定了18个潜在的天文台地点.
结论:
- 该研究提供了一种全面,综合的方法,以选择最佳的观测站点.
- 考虑天文适用性和空气污染风险对于有效的定位至关重要.
- 推的地点可以优化印度尼西亚的天文研究和活动的潜力.
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