This is the authors' abstract. We don't add key points for this paper.
Abstract Environmental impact assessments of Korea's railway projects are currently presented qualitatively and follow guidance in the Eco-friendly Railway Construction Guidelines (2015). Quantitative prediction and evaluation are limited due to limitations conducting literature research or making uniform measurements. Recent developments in the quantification of assessment data emphasize the collection of environmental with Fourth Industrial Revolution technologies such as drones, lidar, big data, and artificial intelligence (AI). In this study, we reviewed the limitations of existing evaluation methods for six key evaluation items for the assessment of railroad projects (air quality, water quality, ecology, topography, noise and vibration, and scenic value). For the first time in Korea, we developed a digitalization plan that will secure quantitative data through drones and lidar for environmental impact evaluations of railroad projects. Based on our results, a preemptive response system can be established for the digitalization of environmental impact assessment for the railway business. In the future, it will be necessary to apply the technologies used in this study to collect continuous time-series data based on the methods established in this study, and to implement specific prediction models.
No takes yet. Share an insight, caveat, or question.
Kim et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: