结合遥感和地理信息系统的2030年随机森林机器学习在印度尼西亚西爪哇发展卫生设施位置适宜性预测

Q2 Environmental Science

Environmental Advances Pub Date : 2024-12-01 DOI:10.1016/j.envadv.2024.100604

Riantini Virtriana , Kalingga Titon Nur Ihsan , Tania Septi Anggraini , Albertus Deliar , Agung Budi Harto , Akhmad Riqqi , Anjar Dimara Sakti

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引用次数: 0

摘要

在今天，获得医疗保健设施是至关重要的。保健设施必须与特定地区的人口成比例。因此，重要的是在存在不平衡的地区增加医疗保健设施的数量。在确定医疗设施的位置时，适当的规划和可持续性审查是必要的。环境变化会影响一个地点未来的适宜性。因此，需要能够预测未来适宜性条件的规划，以确保建成地点具有高可持续性。本研究利用遥感和地理信息系统（GIS）预测2030年西爪哇省医疗设施选址的适宜性。将使用西爪哇以30×30米间隔处理的静态和动态数据。研究中使用了地理空间和遥感数据。动态参数外推采用2000年至2018年的数据。利用西爪哇现有的卫生设施培训数据，采用随机森林机器学习方法获得卫生设施位置的适宜性值。结果表明，2018 - 2030年，各地区卫生保健设施适宜性等级发生变化，部分地区适宜性等级有所增加或减少。这项研究始终强调合适的地点，确保其作为医疗设施站点的可持续性。

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites.

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