anomaly-detection-in-groundwater-monitoring-data-using-lstm-autoencoder-neural-networks-–-springer

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks – Springer

References

  • Aggarwal, C. C., & Aggarwal, C. C. (2017). An introduction to outlier analysis (pp. 1–34). Springer International Publishing.

  • Alla, S., & Adari, S. K. (2019). Beginning anomaly detection using python-based deep learning. Apress.

    Book  Google Scholar 

  • Amiri, V., Nakhaei, M., Lak, R., & Li, P. (2021). An integrated statistical-graphical approach for the appraisal of the natural background levels of some major ions and potentially toxic elements in the groundwater of Urmia aquifer, Iran. Environmental Earth Sciences, 80(12), 432.

    Article  CAS  Google Scholar 

  • Anh, D. T., Pandey, M., Mishra, V. N., Singh, K. K., Ahmadi, K., Janizadeh, S., … & Dang, N. M. (2023). Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm. Applied Soft Computing, 132, 109848.

  • Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2022). Do deep neural networks contribute to multivariate time series anomaly detection? Pattern Recognition, 132, 108945.

    Article  Google Scholar 

  • Azimi, S., Azhdary Moghaddam, M., & Hashemi Monfared, S. A. (2018). Anomaly detection and reliability analysis of groundwater by crude Monte Carlo and importance sampling approaches. Water Resources Management, 32, 4447–4467.

    Article  Google Scholar 

  • Bakx, W., Doornenbal, P. J., Van Weesep, R. J., Bense, V. F., Oude Essink, G. H., & Bierkens, M. F. (2019). Determining the relation between groundwater flow velocities and measured temperature differences using active heating-distributed temperature sensing. Water, 11(8), 1619.

    Article  Google Scholar 

  • Balasubaramanian, S., Cyriac, R., Roshan, S., Paramasivam, K. M., & Jose, B. C. (2023). An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection. Array, 19, 100294.

  • Bengio, Y. (2012). Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning (vol. 27, pp. 17–36).

  • Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR), 54(3), 1–33.

    Article  Google Scholar 

  • Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv:1901.03407.

  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: a survey. ACM Computing Surveys (CSUR), 41(3), 1–58.

    Article  Google Scholar 

  • Cook, A. A., Mısırlı, G., & Fan, Z. (2019). Anomaly detection for IoT time-series data: a survey. IEEE Internet of Things Journal, 7(7), 6481–6494.

    Article  Google Scholar 

  • DeCastro-García, N., Castañeda, Á. L. M., & Fernández-Rodríguez, M. (2020, November). RADSSo: An automated tool for the multi-CASH machine learning problem. In International Conference on Hybrid Artificial Intelligence Systems (pp. 183–194). Cham: Springer International Publishing.

  • Duarte, D. P., Nogueira, R. N., & Bilro, L. B. (2019). Semi-supervised Gaussian and t-distribution hybrid mixture model for water leak detection. Measurement Science and Technology, 30(12), 125109.

    Article  CAS  Google Scholar 

  • Farahani, M. (2021). Anomaly detection on gas turbine time-series’ data using deep LSTM-autoencoder. Master’s thesis, Umeå University.

  • Feng, X., Zhong, J., Yan, R., Zhou, Z., Tian, L., Zhao, J., & Yuan, Z. (2022). Groundwater radon precursor anomalies identification by EMD-LSTM model. Water, 14(1), 69.

    Article  CAS  Google Scholar 

  • Finke, T., Krämer, M., Morandini, A., Mück, A., & Oleksiyuk, I. (2021). Autoencoders for unsupervised anomaly detection in high energy physics. Journal of High Energy Physics, 2021(6), 1–32.

    Article  Google Scholar 

  • Ghasemlounia, R., Gharehbaghi, A., Ahmadi, F., & Saadatnejadgharahassanlou, H. (2021). Developing a novel framework for forecasting groundwater level fluctuations using bi-directional long short-term memory (BiLSTM) deep neural network. Computers and Electronics in Agriculture, 191, 106568.

    Article  Google Scholar 

  • Goularas, D., & Kamis, S. (2019). Evaluation of deep learning techniques in sentiment analysis from Twitter data. In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) (pp. 12–17). IEEE.

  • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232.

    Article  Google Scholar 

  • Gu, J. (2016). Mathematical modeling of groundwater anomaly detection. Master’s thesis, Colorado State University. 

  • Hill, D. J., Minsker, B. S., & Amir, E. (2009). Real‐time Bayesian anomaly detection in streaming environmental data. Water Resources Research, 45, W00D28.

  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  CAS  Google Scholar 

  • Jeong, J., Park, E., Han, W. S., Kim, K., Choung, S., & Chung, I. M. (2017). Identifying outliers of non-Gaussian groundwater state data based on ensemble estimation for long-term trends. Journal of Hydrology, 548, 135–144.

    Article  Google Scholar 

  • Jeong, J., Park, E., Chen, H., Kim, K. Y., Han, W. S., & Suk, H. (2020). Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data. Journal of Hydrology, 582, 124512.

    Article  Google Scholar 

  • Kang, J., Kim, C. S., Kang, J. W., & Gwak, J. (2021). Anomaly detection of the brake operating unit on metro vehicles using a one-class LSTM autoencoder. Applied Sciences, 11(19), 9290.

    Article  CAS  Google Scholar 

  • Keesari, T., Ramakumar, K. L., Chidambaram, S., Pethperumal, S., & Thilagavathi, R. (2016). Understanding the hydrochemical behavior of groundwater and its suitability for drinking and agricultural purposes in Pondicherry area, South India–A step towards sustainable development. Groundwater for Sustainable Development, 2, 143–153.

    Article  Google Scholar 

  • Kim, Y., Jeong, J., Park, H., Kwon, M., Cho, C., & Jeong, J. (2022). Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data. Journal of Hydrology, 612, 128127.

  • Kim, D., Lindquist, W. B., & Peters, C. A. (2011). Upscaling geochemical reaction rates accompanying acidic CO2‐saturated brine flow in sandstone aquifers. Water Resources Research, 47, W01505. 

  • Langevin, C. D., Thorne Jr, D. T., Dausman, A. M., Sukop, M. C., & Guo, W. (2008). SEAWAT version 4: a computer program for simulation of multi-species solute and heat transport. US Geological Survey Techniques and Methods Book 6, Ch A22.

  • Li, H., Son, J. H., Hanif, A., Gu, J., Dhanasekar, A., & Carlson, K. (2017). Colorado Water Watch: Real-time groundwater monitoring for possible contamination from oil and gas activities. Journal of Water Resource and Protection, 9(13), 1660.

    Article  Google Scholar 

  • Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 131, 103498.

    Article  Google Scholar 

  • Liu, X., Wang, Z., & Zhang, X. (2016). A review of the green tides in the Yellow Sea, China. Marine Environmental Research, 119, 189–196.

    Article  CAS  Google Scholar 

  • Liu, J., Gu, J., Li, H., & Carlson, K. H. (2020a). Machine learning and transport simulations for groundwater anomaly detection. Journal of Computational and Applied Mathematics, 380, 112982.

    Article  Google Scholar 

  • Liu, J., Wang, P., Jiang, D., Nan, J., & Zhu, W. (2020b). An integrated data-driven framework for surface water quality anomaly detection and early warning. Journal of Cleaner Production, 251, 119145.

    Article  CAS  Google Scholar 

  • Maleki, S., Maleki, S., & Jennings, N. R. (2021). Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering. Applied Soft Computing, 108, 107443.

    Article  Google Scholar 

  • Maniyath, S. R., Pooja, G., Chandana, R., Namitha, K. S., & Lakshminarasamma, N. (2021, June). Groundwater anomaly detection using machine learning. In 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C) (pp. 8–14). IEEE.

  • Mao, J., Wang, H., & Spencer, B. F., Jr. (2021). Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders. Structural Health Monitoring, 20(4), 1609–1626.

    Article  Google Scholar 

  • Mikuni, V., & Nachman, B. (2023). High-dimensional and permutation invariant anomaly detection. Physics Review, D 106, 092009.

  • Mitiche, I., McGrail, T., Boreham, P., Nesbitt, A., & Morison, G. (2021). Data-driven anomaly detection in high-voltage transformer bushings with LSTM auto-encoder. Sensors, 21(21), 7426.

    Article  Google Scholar 

  • Moradi Vartouni, A., Teshnehlab, M., & Sedighian Kashi, S. (2019). Leveraging deep neural networks for anomaly-based web application firewall. IET Information Security, 13(4), 352–361.

    Article  Google Scholar 

  • Mulligan, A. E., Langevin, C., & Post, V. E. (2011). Tidal Boundary Conditions in SEAWAT. Groundwater, 49(6), 866–879.

    Article  CAS  Google Scholar 

  • Naddaf-Sh, S., Naddaf-Sh, M. M., Kashani, A. R., & Zargarzadeh, H. (2020, December). An efficient and scalable deep learning approach for road damage detection. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5602–5608). IEEE.

  • Nasiri, M., Moghaddam, H. K., & Hamidi, M. (2021). Development of multi-criteria decision making methods for reduction of seawater intrusion in coastal aquifers using SEAWAT code. Journal of Contaminant Hydrology, 242, 103848.

    Article  CAS  Google Scholar 

  • Nayyar, A., & Singh, R. (2015). A comprehensive review of simulation tools for wireless sensor networks (WSNs). Journal of Wireless Networking and Communications, 5(1), 19–47.

    Google Scholar 

  • Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and anomaly detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management, 57, 102282.

    Article  Google Scholar 

  • Nicholaus, I. T., Park, J. R., Jung, K., Lee, J. S., & Kang, D. K. (2021). Anomaly detection of water level using deep autoencoder. Sensors, 21(19), 6679.

    Article  Google Scholar 

  • Oppus, C., Guico, M. L., Monje, J. C., Domingo, M. A. L. G. A., Ngo, G., Retirado, M. G., & Kwong, J. C. (2020, October). Remote and real-time sensor system for groundwater level and quality. In 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) (pp. 152–155). IEEE.

  • Panjehfouladgaran, A., & Rajabi, M. M. (2022). Contaminant source characterization in a coastal aquifer influenced by tidal forces and density-driven flow. Journal of Hydrology, 610, 127807.

    Article  CAS  Google Scholar 

  • Papastergios, G., Filippidis, A., Fernandez-Turiel, J. L., Gimeno, D., & Sikalidis, C. (2011). Surface soil geochemistry for environmental assessment in Kavala area, northern Greece. Water, Air, & Soil Pollution, 216, 141–152.

    Article  CAS  Google Scholar 

  • Rajabi, M. M., Komeilian, P., Wan, X., & Farmani, R. (2023). Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks. Water Research, 238, 120012.

    Article  CAS  Google Scholar 

  • Robinson, C., Li, L., & Barry, D. A. (2007). Effect of tidal forcing on a subterranean estuary. Advances in Water Resources, 30(4), 851–865.

    Article  Google Scholar 

  • Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., … & Villez, K. (2021). The value of human data annotation for machine learning based anomaly detection in environmental systems. Water Research, 206, 117695.

  • Sahin, A. U. (2016). A new parameter estimation procedure for pumping test analysis using a radial basis function collocation method. Environmental Earth Sciences, 75, 1–13.

    Article  Google Scholar 

  • Şahin, A. U., & Çiftçi, E. (2023). Cessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test data. Hydrological Sciences Journal, 68(11), 1578–1590.

    Article  Google Scholar 

  • Sgueglia, A., Di Sorbo, A., Visaggio, C. A., & Canfora, G. (2022). A systematic literature review of IoT time series anomaly detection solutions. Future Generation Computer Systems, 134, 170–186.

    Article  Google Scholar 

  • Shaukat, K., Alam, T. M., Luo, S., Shabbir, S., Hameed, I. A., Li, J., … & Javed, U. (2021). A review of time-series anomaly detection techniques: A step to future perspectives. In Advances in information and communication: Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 1 (pp. 865–877). Springer International Publishing.

  • Sherif, M., Kacimov, A., Javadi, A., & Ebraheem, A. A. (2012). Modeling groundwater flow and seawater intrusion in the coastal aquifer of Wadi Ham, UAE. Water Resources Management, 26, 751–774.

    Article  Google Scholar 

  • Song, Z., Lu, C., Zhang, Y., Chen, J., Liu, W., Liu, B., & Shu, L. (2022). Spatiotemporal distribution and statistical analysis of abnormal groundwater level rising in Poyang Lake basin. Water, 14(12), 1906.

    Article  Google Scholar 

  • Tornyeviadzi, H. M., Mohammed, H., & Seidu, R. (2023). Semi-supervised anomaly detection methods for leakage identification in water distribution networks: a comparative study. Machine Learning with Applications, 14, 100501.

    Article  Google Scholar 

  • Veena, S., Mahesh, K., Rajesh, M., & Salmon, S. (2018). The survey on smart agriculture using IOT. International Journal of Innovative Research in Engineering (IJRIREM), 5(2), 63–66.

    Google Scholar 

  • Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023). LSTM-autoencoder-based anomaly detection for indoor air quality time-series data. IEEE Sensors Journal, 23(4), 3787–3800.

    Article  Google Scholar 

  • Xintong, G., Hongzhi, W., Song, Y., & Hong, G. (2014). Brief survey of crowdsourcing for data mining. Expert Systems with Applications, 41(17), 7987–7994.

    Article  Google Scholar 

  • Zaib Jadoon, K., Zeeshan Ali, M., Yousafzai, H. U. K., Rehman, K. U., Shah, J. A., & Shiekh, N. A. (2023, May). Smart groundwater monitoring system for managed aquifer recharge based on enabled real-time internet of things. In EGU General assembly conference abstracts (pp. EGU-12909).

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