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首页> 外文期刊>Journal of Hydroinformatics >Hydraulic head uncertainty estimations of a complex artificial intelligence model using multiple methodologies
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Hydraulic head uncertainty estimations of a complex artificial intelligence model using multiple methodologies

机译：使用多种方法对复杂的人工智能模型进行液压头不确定性估计

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The purpose of this study is to examine the uncertainty of various aspects of a combined artificial neural network (ANN), kriging and fuzzy logic methodology, which can be used for the spatial and temporal simulation of hydraulic head in an aquifer. This simulation algorithm was applied in a study area in Miami - Dade County, USA. The percentile methodology was applied as a first approach in order to define the ANN uncertainty, resulting in wide prediction intervals (PIs) due to the coarse nature of the methodology. As a second approach, the uncertainty of the ANN training is tested through a Monte Carlo procedure. The model was executed 300 times using different training set and initial random values, and the training results constituted a sensitivity analysis of the ANN training to the kriging part of the algorithm. The training and testing error intervals for the ANNs and the kriging PIs calculated through this procedure can be considered narrow, taking into consideration the complexity of the study area. For the third and final approach used in this work, the uncertainty of kriging parameter was calculated through the Bayesian kriging methodology. The derived results prove that the simulation algorithm provides consistent and accurate results.
机译：这项研究的目的是研究组合人工神经网络（ANN），克里金法和模糊逻辑方法学各个方面的不确定性，这些方法可用于含水层中水头的时空模拟。该模拟算法已应用于美国迈阿密-戴德县的研究区域。为了定义ANN的不确定性，将百分位数方法用作第一种方法，由于该方法的粗略性质，导致宽的预测间隔（PI）。第二种方法是，通过蒙特卡洛程序测试ANN训练的不确定性。该模型使用不同的训练集和初始随机值执行了300次，训练结果构成了ANN训练对算法的克里金部分的敏感性分析。考虑到研究区域的复杂性，可以认为通过该程序计算出的ANN和kriging PI的训练和测试错误间隔较窄。对于这项工作中使用的第三个也是最后一个方法，克里格参数的不确定性是通过贝叶斯克里格方法来计算的。所得结果证明，该仿真算法提供了一致且准确的结果。

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