Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Event-driven beach sandline change

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (i.e., Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the event-driven beach sandline change model.本数据集为障壁岛与河口湿地物理变化评估（Barrier Island and Estuarine Wetland Physical Change Assessment），旨在为弗吉尼亚州、马里兰州及新泽西州的研究区域校准并测试障壁岛河口岸线后滨（backshore）与海滩沙线变化的概率模型。该类模型针对与长期及风暴引发的越流、障壁后岸线（back-barrier shoreline）变化相关的水文与物理变量的影响展开分析。研究人员借助NORSYS Software Corporation开发的Netica软件，将输入变量构建为贝叶斯网络（Bayesian Network, BN）——该软件支持用户开展置信网络与影响图的相关操作。每一款模型均基于全域输入变量间习得的相关性，对其预测长期及事件驱动型（即飓风桑迪（Hurricane Sandy）引发的）后滨与海滩沙线变化的能力进行测试。结合输入的水动力与地貌数据，该贝叶斯网络将受约束以生成各位置后滨或海滩沙线变化的更新条件概率预测结果。为评估该贝叶斯网络复现模型训练所用观测数据的能力，本研究采用了模型技能、对数似然比与概率预测三类指标。本数据集即为事件驱动型海滩沙线变化模型的概率与模型技能指标数据。