Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.
Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.
复制标题
评估切萨皮克湾物理屏障的贝叶斯地质统计预测的性能。
DOI：
10.1007/s10661-024-12401-y
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发表时间：
2024
影响因子：
3
通讯作者：
Curriero,FC
中科院分区：
文献类型：
--
作者：
Desjardins,MR;Davis,BJK;Curriero,FC
切萨皮克湾是美国和世界各地研究最广泛的水域之一。水质指标（例如盐度）的日常监测依赖于整个海湾的固定采样站。利用这些丰富的监测数据，各种方法可以对水质指标进行表面预测，以进一步表征海湾的健康状况并支持野生动物和人类健康研究。用于地质统计建模的贝叶斯方法变得越来越流行，并且比频率论方法更受青睐，因为可以计算完整且精确的推理，以及更准确地表征不确定性。传统的地质统计预测方法在将空间依赖性描述为距离的函数时假设两点之间的欧几里德距离。然而，当建模过程中跨越水陆边界时，欧几里得方法可能不适用于河口环境。在本研究中，我们将静止和障碍 INLA 地统计模型与经典克里金地统计模型进行比较，以预测 2019 年 4 个月切萨皮克湾的盐度。对每种方法进行交叉验证，以根据预测准确性和精度评估模型性能。结果证明，这两种基于贝叶斯的模型优于普通克里金法，特别是在检查预测准确性时（尤其是在支流中）。我们还建议非欧几里得模型考虑了采样地点之间适当的水基距离，并且可能更好地描述不确定性。然而，更复杂的水体可能会更好地展示物理屏障 INLA 模型的能力和功效。
The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.