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Mean relative sea level (MRSL) rise has caused more frequent flooding in many parts of the world. The MRSL rise varies substantially from place to place. Here we use tide-gauge data and satellite measurements to examine past MRSL trends for the coasts of the China Seas. We then combine climate model output and satellite observations to provide MRSL projections in the 21st century. The MRSL trend based on tide-gauge data shows substantial regional variations, from 1 to 5 mm/yr. The vertical land motion (VLM) based on altimetry and tide-gauge (ATG) data indicates large land subsidence at some tide-gauge locations, consistent with the Global Positioning Systems (GPS)-based VLM but different significantly from small uplift estimated by a Glacial Isostatic Adjustment (GIA) model, which suggests other important factors causing the VLM instead of the GIA process. When GPS- or ATG-based VLM estimates are used, the projected MRSL rise between 1986–2005 and 2081–2100 at tide-gauge sites varies from 60 to 130 cm under the Representative Concentration Pathway 8.5 (RCP8.5) scenario of the Intergovernmental Panel on Climate Change (IPCC). Our projections are significantly larger than those of IPCC and other literature, as a result of accounting for the land subsidence derived from observations. Steric and dynamic ocean effects and land-ice melt effects are comparable (about 30 cm each) and do not vary much over the tide-gauge locations. The VLM effect varies from −10 to 60 cm. The projections between 1986–2005 and 2081–2100 under RCP4.5 show a similar spatial distribution to that under RCP8.5, with a smaller amount of rise by 18 cm on average for this region. [ABSTRACT FROM AUTHOR]

Providing real-time bus arrival information can help to improve the service quality of a transit system and enhance its competitiveness among other transportation modes. Taking the city of Jinan, China, as an example, this study proposes two artificial neural network (ANN) models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data. Also, to contend with the difficulty in capturing the traffic fluctuations over different time periods and account for the impact of signalized intersections, this study also subdivides the collected dataset into a bunch of clusters. Sub-ANN models are then developed for each cluster and further integrated into a hierarchical ANN model. To validate the proposed models, six scenarios with respect to different time periods and route lengths are tested. The results reveal that both proposed ANN models can outperform the Kalman filter model. Particularly, with several selected performance indices, it has been found that the hierarchical ANN model clearly outperforms the other two models in most scenarios. [ABSTRACT FROM AUTHOR]