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\begin{document}

\title{A deep adaptive cycle generative adversarial neural network for inverse
estimation of groundwater contaminated source and model parameter}



\author[1]{Zidong Pan}%
\author[1]{Wenxi Lu}%
\author[1]{Yaning Xu}%
\author[1]{Chengming Luo}%
\author[1]{Yukun Bai}%
\affil[1]{Jilin University}%


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  \date{June 11, 2024}


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\begin{abstract}
In light of the challenges posed by groundwater contamination and the
urgent need for accurate and efficient groundwater contaminated source
estimation (GCSE), the present study proposes a novel approach for GCSE
using a deep adaptive cycle generative adversarial neural network
(DA-CGAN). Given the equifinality from different parameters (EFDP) often
associated with GCSE, we leveraged a bidirectional adversarial training
pattern involving a forward process and a recovery process to supervise
the inverse mapping relationship. Once trained, the forward process can
be utilized to provide estimation for GSCE. This bidirectional-training
strategy mitigates EFDP, thereby effectively enhancing the reliability
of GCSE. Moreover, the performance of DA-CGAN is closely related to the
quality of the training samples. To address this, we introduced a
significant enhancement through an adaptive sampling strategy. This
substantially improves the quality of training samples and consequently
increases the accuracy of the GCSE. Furthermore, the inherent
data-driven attribute of the deep cycle GAN considerably reduces
computational costs when conducting GCSE. The research unfolds in the
contexts of both hypothetical and real-world scenarios, with the goal of
providing an efficient, precise, and cost-effective solution for GCSE.
The results demonstrate that the DA-CGAN, an innovative model in the
hydrogeological domain, exhibits superior performance in both estimation
accuracy (Average Relative Error (ARE) of 4.91\% and R of 0.998) and
computational efficiency (0.17 seconds per run). This is particularly
notable when compared with typical inverse methods such as the genetic
algorithm (GA) and the ensemble kalman filter (ENKF).%
\end{abstract}%



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\textbf{Hosted file}

\verb`968993_0_art_file_11196065_ry2s73.docx`  available at \url{https://authorea.com/users/641765/articles/655882-a-deep-adaptive-cycle-generative-adversarial-neural-network-for-inverse-estimation-of-groundwater-contaminated-source-and-model-parameter}

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