Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India 462003.
Indian Institute of Technology Guwahati, Guwahati, Assam, India 781039.
J Contam Hydrol. 2024 Nov;267:104447. doi: 10.1016/j.jconhyd.2024.104447. Epub 2024 Oct 22.
Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.
污染源的溯源一直是准确模拟地下水运移的难题。污染源的溯源问题可以分为几个部分，例如识别污染的位置、污染的强度、污染物进入地下水的时间以及其活动的持续时间。将所有部分作为变量来识别污染源会增加计算的复杂性。通过优化方法快速解决问题，需要减少污染源识别问题中的变量数量。在污染源识别建模中，最具挑战性的变量是污染的位置，因为对于地下水模型的几乎所有数值解，它都是一个离散变量。在本研究中，我们创建了一种方法，将污染源的位置从整个含水层中的随机分布缩小到合理数量的可能位置。尽管以前已经设计出了识别污染源位置的方法，但我们尝试了一种将粒子追踪方法与贝叶斯方法相结合的方法，以更新概率作为一种新颖的方法，其中观测数据是有限的。我们考虑了含水层参数和观测井数据，并设计了一种方法，通过拉格朗日方法对粒子运动进行追踪，以识别潜在的源位置。我们使用贝叶斯方法通过新的细化网格空间信息来更新概率，将源位置细化到更窄的概率分布。我们使用不同的假设问题测试了模型以识别潜在的源，并在平均概率为 0.53 的情况下识别了平流主导传输中的源，在平均概率为 0.62 的情况下识别了扩散主导传输中的源，在平均概率为 0.99 的情况下识别了异质土壤中的源，在平均概率为 0.91 的情况下识别了各向异性含水层中的源，以及在平均概率为 0.96 的情况下识别了具有不规则边界的含水层中的源，以识别与实际污染源最近的位置。结果令人满意，以 88%的准确率（概率大于 0.4 的情况下识别出 15 个潜在源中的 17 个）识别出潜在源的数量，并以 0.223 的概率识别出含水层中的位置。找到最接近实际位置的源的概率为 0.745，平均距离实际源位置 11.6 米。
