气候变化领域集成服务门户(CCPortal): Bayesian probabilistic forecasting for ship emissions

CCPortal

DOI	10.1016/j.atmosenv.2020.117540
	Bayesian probabilistic forecasting for ship emissions
	Liu J.; Duru O.
发表日期	2020
ISSN	1352-2310
卷号	231
英文摘要	This paper proposes a Bayesian forecasting algorithm to extrapolate ship movements and accordingly ship emissions based on probabilities extracted from current ship movements, sailing configurations and ship particulars. Total amount of ship emission on a given sea field may be estimated by using the Automatic Identification System (AIS) records. However, emission predictions cannot be generated since future ship movements are not available. Therefore, predictive exercises usually lie on extrapolations of mass amount of emissions independent from ship movements and any transformations of ship fuel and engine characteristics. In this circumstance, a Bayesian ship traffic generator is developed to simulate long-term predictions of ship movements based on cumulative ship traffic. The empirical study reflects the case of the Port of Singapore and the forecasting horizon for years of 2020 and 2025, with 2018 being the baseline year for comparison analysis. By utilizing the proposed algorithm, policy makers can visualize and simulate the impact of various regulations and implementation on emission control, fuel standards or technical changes in ship design or engine simultaneously. In other words, the proposed simulation testbed also enables prescriptive analytics of emission factors by adjusting technical features of ships. © 2020 Elsevier Ltd
关键词	Bayesian MCMC Emission projection Fuel-mix simulation Prescriptive analytics
语种	英语
scopus关键词	Automatic identification; Emission control; Engines; Extrapolation; Forecasting; Waterway transportation; Automatic identification system; Bayesian forecasting; Comparison analysis; Empirical studies; Long-term prediction; Probabilistic forecasting; Simulation test beds; Technical features; Ships; algorithm; automation; Bayesian analysis; carbon emission; empirical analysis; engine; forecasting method; identification method; maritime transportation; probability; air pollution control; algorithm; article; empiricism; exercise; forecasting; prediction; probability; ship; simulation; Singapore; Singapore [Southeast Asia]
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249159
作者单位	School of Civil and Environmental Engineering, Nanyang Technological University Singapore, 50 Nanyang Avenue, N1-01c-95639798, Singapore
推荐引用方式 GB/T 7714	Liu J.,Duru O.. Bayesian probabilistic forecasting for ship emissions[J],2020,231.
APA	Liu J.,&Duru O..(2020).Bayesian probabilistic forecasting for ship emissions.ATMOSPHERIC ENVIRONMENT,231.
MLA	Liu J.,et al."Bayesian probabilistic forecasting for ship emissions".ATMOSPHERIC ENVIRONMENT 231(2020).