Uncertainty quantification (UQ) is an important benchmark to assess the performance of artificial intelligence (AI) and particularly deep learning ensembled-based models. However, the ability for UQ using current AI-based methods is not only limited in terms of computational resources but it also requires changes to topology and optimization processes, as well as multiple performances to monitor model instabilities. From both geo-engineering and societal perspectives, a predictive groundwater table (GWT) model presents an important challenge, where a lack of UQ limits the validity of findings and may undermine science-based decisions. To overcome and address these limitations, a novel ensemble, an automated random deactivating connective weights approach (ARDCW), is presented and applied to retrieved geographical locations of GWT data from a geo-engineering project in Stockholm, Sweden. In this approach, the UQ was achieved via a combination of several derived ensembles from a fixed optimum topology subjected to randomly switched off weights, which allow predictability with one forward pass. The process was developed and programmed to provide trackable performance in a specific task and access to a wide variety of different internal characteristics and libraries. A comparison of performance with Monte Carlo dropout and quantile regression using computer vision and control task metrics showed significant progress in the ARDCW. This approach does not require changes in the optimization process and can be applied to already trained topologies in a way that outperforms other models.

不确定性量化 ( UQ ) 是评估人工智能 ( AI ) 性能的重要基准，尤其是基于深度学习集成的模型。然而，UQ使用当前基于AI的方法的能力不仅在计算资源方面受到限制，而且还需要更改拓扑和优化过程，以及监测模型不稳定性的多种性能。从地球工程和社会的角度来看，预测地下水位 ( GWT ) 模型提出了一个重要挑战，其中缺乏UQ限制了研究结果的有效性，并可能破坏基于科学的决策。为了克服和解决这些限制，提出了一种新的集成，一种自动随机去激活连接权重方法 ( ARDCW )，并将其应用于从瑞典斯德哥尔摩的一个地球工程项目中检索GWT数据的地理位置。在这种方法中，UQ是通过组合来自固定最优拓扑的几个派生集合来实现的，这些集合受到随机关闭的权重的影响，这允许通过一次前向传递进行预测。该过程的开发和编程是为了在特定任务中提供可跟踪的性能，并访问各种不同的内部特征和库。使用计算机视觉和控制任务指标与 Monte Carlo dropout 和分位数回归进行的性能比较表明，ARDCW取得了重大进展。这种方法不需要更改优化过程，并且可以以优于其他模型的方式应用于已经训练好的拓扑。