基于人工神经网络的地下水污染源识别
Contaminant source identification in groundwater by means of artificial neural network
摘要：
在一个理想的环境保护系统中，地下水不能被排除在外。除了过度开采的问题之外，与可持续发展的概念完全不符，另一个不容忽视的问题涉及地下水污染。这主要是由于密集的农业活动或工业化地区。在文献中，有几篇论文讨论了输运问题，特别是其中的释放历史或源位置的识别反问题。本文的创新目标是开发一个数据驱动的模型，该模型能够分析多个场景，甚至是强非线性的场景，以解决正向和反向运输问题，保持结果的可靠性并减少不确定性。此外，这一工具具有提供极快反应的特点，这对于立即确定补救战略至关重要。该模型产生的优势进行了比较，与文献研究。在这方面，一个前馈人工神经网络，它已被训练来处理不同的情况下，代表数据驱动的模型。首先，确定污染物的浓度在特定的观测点在研究区域（正问题）;其次，处理的逆问题，确定在已知的源位置的释放历史;然后，在一个污染源的情况下，确定的释放历史，同时，在一个特定的子域的调查区域的源的位置。最后对观测误差进行了分析和估计。结果令人满意地实现，突出了人工神经网络处理多个场景的能力，通过近似非线性函数，而无需描述该现象的物理观点，提供可靠的结果，具有非常低的计算负担和不确定性。
英文摘要：
In a desired environmental protection system, groundwater may not be excluded. In addition to the problem of over-exploitation, in total disagreement with the concept of sustainable development, another not negligible issue concerns the groundwater contamination. Mainly, this aspect is due to intensive agricultural activities or industrialized areas. In literature, several papers have dealt with transport problem, especially for inverse problems in which the release history or the source location are identified. The innovative aim of the paper is to develop a data-driven model that is able to analyze multiple scenarios, even strongly non-linear, in order to solve forward and inverse transport problems, preserving the reliability of the results and reducing the uncertainty. Furthermore, this tool has the characteristic of providing extremely fast responses, essential to identify remediation strategies immediately. The advantages produced by the model were compared with literature studies. In this regard, a feedforward artificial neural network, which has been trained to handle different cases, represents the data-driven model. Firstly, to identify the concentration of the pollutant at specific observation points in the study area (forward problem); secondly, to deal with inverse problems identifying the release history at known source location; then, in case of one contaminant source, identifying the release history and, at the same time, the location of the source in a specific sub-domain of the investigated area. At last, the observation error is investigated and estimated. The results are satisfactorily achieved, highlighting the capability of the ANN to deal with multiple scenarios by approximating nonlinear functions without the physical point of view that describes the phenomenon, providing reliable results, with very low computational burden and uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
