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首页> 外文期刊>Journal of Hydrology >Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data
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Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data

机译：基于损坏数据的经常性神经网络的鲁棒训练估算地下水位

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In the present study, a cost function is developed for the robust training of recurrent neural-network models using groundwater-level data that are corrupted by outliers and noise. The optimal cost function in this study utilizes least trimmed squares (LTS) with asymmetric weighting (AW) and the Whittaker smoother (WS), which have different outlier- or noise-rejecting mechanisms. The developed cost function is benchmarked with other cost functions in the training of a long short-term memory (ISTM) model using data from the Gangjin-Seongjeon and Pohang-Gibuk monitoring wells in South Korea, the results of which are then compared to the validation data. Based on comparisons of the validation results, it is confirmed that the optimal cost function is the most successful in rejecting the influence of outliers during the training process when applied to data from the Ganglin-Seongjeon monitoring well. It is also demonstrated that the estimation results based on this optimal cost function can effectively identify outliers in groundwater-level data. For the Pohang-Gibuk monitoring well data, the optimal cost function without AW exhibits superior regularizing performance by generating the lowest mean estimation error. Using this cost function, the influence of persistent noise is mostly canceled out, and the estimation results reflect the regular changes in the water table level of a shallow aquifer over time. The developed robust cost function can potentially be employed in many hydrogeological applications, such as the monitoring of groundwater resources, the prediction and analysis of water table levels, and the identification of changes in aquifer processes. The cost function is also expected to be useful for many other field applications in which the data are susceptible to external influences.
机译：在本研究中，使用由异常值和噪声损坏的地下水位数据进行经常性神经网络模型的强大培训开发了成本函数。本研究中的最佳成本函数利用具有不对称加权（AW）和Whittaker更顺畅（WS）的最小修整的平方（LTS），其具有不同的异常或噪声抑制机制。发达的成本函数在训练中以其他成本函数为基准测试，这些功能在韩国的刚刚九川和浦岗监测井中的刚刚的短期内存（ISTM）模型中的训练中的培训，然后将其结果与验证数据。基于验证结果的比较，确认最佳成本函数是最成功的，最为成功地拒绝在培训过程中的培训过程中的影响，当应用于来自龙珠 - Seongjeon监测的数据时。还证明了基于该最佳成本函数的估计结果可以有效地识别地下水位数据中的异常值。对于Pohang-Gibuk监测井数据，无AW的最佳成本函数通过产生最低平均估计误差来表现出优越的正常性能。使用这种成本函数，持久噪声的影响大部分被取消，并且估计结果随着时间的推移反映了浅含水层的水位水平的正常变化。发达的稳健成本函数可以在许多水文层中使用，例如对地下水资源的监测，水表水平的预测和分析，以及含水层过程的变化。预计成本函数也有助于许多其他现场应用，其中数据易受外部影响。

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