GroMoPo Metadata for Kish Island seawater intrusion model

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert elicitation methodology is developed and applied to the real-world test case in order to provide a road map for the use of fuzzy Bayesian inference in groundwater modeling applications. (C) 2016 Elsevier B.V. All rights reserved.传统上，贝叶斯推断（Bayesian inference）被视为在地下水模型参数估计中正式整合专家知识的适宜框架。然而，传统贝叶斯推断无法考量专家所提供信息中本质蕴含的不精确性。为解决这一问题，近年来学界提出了多种针对传统贝叶斯推断的扩展方法。其中一类扩展方法为模糊贝叶斯推断（fuzzy Bayesian inference），其核心是将模糊技术集成至贝叶斯统计学体系中。模糊贝叶斯推断具备多项优良特性，使其成为在地下水模型参数估计流程中整合专家知识的极具吸引力的方案：其一，它能够良好适配专家提供信息的固有属性；其二，它可以对不确定性与不精确性进行差异化建模；其三，它提供了一个可融合贝叶斯推断算法各类输入相关专家信息的框架。但在地下水数值模拟应用中采用模糊贝叶斯推断的一项重要阻碍是计算负担过重，因为所需的数值模型模拟次数往往会变得极为庞大，时常在计算上不可行。本文提出了一种加速模糊贝叶斯推断算法的新颖方法，该方法基于使用源自代理模型（surrogate modeling）的近似后验分布作为计算中的筛选工具。所提方法首先被应用于滨海含水层（coastal aquifer）中的海水入侵（seawater intrusion, SWI）合成测试案例。研究表明，针对该合成测试案例，所提方法将所需的数值模拟次数降低了一个数量级。随后，该方法被应用于一个真实世界测试案例，该案例涉及波斯湾基什岛的三维海水入侵数值模拟。本文还开发并应用了一套专家征询（expert elicitation）方法，用于该真实世界测试案例，旨在为地下水模拟应用中模糊贝叶斯推断的使用提供一套实施路线图。© 2016 爱思唯尔 B.V.（Elsevier B.V.）保留所有权利。