Keywords:
Structural health monitoring
drainage
vos viewer
machine learning
bibliometric

  • Abstract

    This research aims to review machine learning methods or techniques used to classify or predict structural monitoring on sustainable urban drainage. This study used a bibliometric approach with Scopus data related to classification for sustainable urban drainage system studies, searched using the keywords drainage classification and prediction using machine learning published in the Scopus journal, and found 94 articles. The data in this article uses articles published between 2014 – 2023, with relevant topics. Hopefully, this research can help researchers develop machine learning techniques to improve their ability to classify and better predict structural health monitoring on drainage systems. The findings from this study are as follows: First, the number of published articles has increased by 23.1% from 2019 to 2022. Second, the piece by Koizumi K. and Oda, K. became the article with the highest citations. Third, the 15 keywords are classified into three clusters with leading and supporting keywords. Fourth, researchers use general keywords such as drainage classification, while keywords directly referring to machine learning methods are rarely used. Fifth, China is the largest and most dominant country in discussing drainage systems classification and prediction. Sixth, the distribution of articles based on subject area is dominated by Engineering and Environmental science subjects. Developing deep learning methods and adding feature extraction algorithms in selecting features used to model data can increase the efficiency and accuracy of the classification process – structural health monitoring on drainage prediction for the drainage structure design. The development of research data using Vos viewers images with this type of image processing research can also be maximized for research related to the classification - prediction of drainage using machine learning methods.

  • References

    1. Ahmed, S., & Kopsaftopoulos, F. (2023). Active sensing ultrasonic guided wave-based damage diagnosis via stochastic stationary time-series models. Structural Health Monitoring. https://doi.org/10.1177/14759217231201975
    2. Ahrens, B. (2006). Distance in spatial interpolation of daily rain gauge data. Hydrol. Earth Syst. Sci., 10(2), 197-208. https://doi.org/10.5194/hess-10-197-2006
    3. Amedu, J., & Nwokolo, C. (2013). Improved Well and Reservoir Production Performance in Waterflood Reservoirs-Revolutionalizing the Hall Plot. SPE Nigeria Annual International Conference and Exhibition,
    4. Attwa, M., & Zamzam, S. (2020). An integrated approach of GIS and geoelectrical techniques for wastewater leakage investigations: Active constraint balancing and genetic algorithms application. Journal of Applied Geophysics, 175. https://doi.org/https://doi.org/10.1016/j.jappgeo.2020.103992
    5. Billa, L., Mansor, S., Mahmud, A. R., & Ghazali, A. H. (2005). AVHRR data for real-time operational flood forecasting in Malaysia. In Geo-information for Disaster Management (pp. 1357-1370). Springer, Berlin, Heidelberg. https://doi.org/https://doi.org/10.1007/3-540-27468-5_93
    6. Casal-Campos, A., Sadr, S. M. K., Fu, G., & Butler, D. (2018). Reliable, Resilient and Sustainable Urban Drainage Systems: An Analysis of Robustness under Deep Uncertainty. Environmental Science & Technology, 52(16), 9008-9021. https://doi.org/10.1021/acs.est.8b01193
    7. Charlesworth, S. M., Perales-Momparler, S., Lashford, C., & Warwick, F. (2013). The sustainable management of surface water at the building scale: preliminary results of case studies in the UK and Spain. Journal of Water Supply: Research and Technology-Aqua, 62(8), 534-544. https://doi.org/10.2166/aqua.2013.051
    8. Chen, I. H., Chen, P.-A., & Su, M. (2019). Applying Computer Vision Technology to Health Monitoring for Underground Structure in a Landslide Area. https://doi.org/10.12783/shm2019/32455
    9. Cramer, S., Kampouridis, M., Freitas, A. A., & Alexandridis, A. K. (2017). An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 85, 169-181. https://doi.org/https://doi.org/10.1016/j.eswa.2017.05.029
    10. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/https://doi.org/10.1016/j.jbusres.2021.04.070
    11. Fang, G., Li, J., Guifen, P., Yijun, C., & Benqiang, X. (2015). Evaluation and prediction of bioreactor landfill gas production. Electron. J. Geotech. Eng., 20(7), 1981-1989.
    12. Feng, X.-T., Li, Z.-W., Mei, S.-M., Tian, J., Yang, C.-X., Yao, Z.-B., & Gao, J.-K. (2023). A Novel Large-Scale Three-Dimensional Physical Model Experimental System for Deep Underground Engineering. Rock Mechanics and Rock Engineering, 56(11), 8395-8413. https://doi.org/10.1007/s00603-023-03495-w
    13. Fross, M., & Biberschick, P. (1991). Deformations of landslide stabilization structures. Proc Int Conf Soil Mech Found Eng, 2, 711-716.
    14. Gattinoni, P., & Scesi, L. (2010). An empirical equation for tunnel inflow assessment: application to sedimentary rock masses. Hydrogeology Journal, 18(8), 1797-1810. https://doi.org/10.1007/s10040-010-0674-1
    15. Gharehbaghi, V. R., Noroozinejad Farsangi, E., Noori, M., Yang, T. Y., Li, S., Nguyen, A., Málaga-Chuquitaype, C., Gardoni, P., & Mirjalili, S. (2022). A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives. Archives of Computational Methods in Engineering, 29(4), 2209-2235. https://doi.org/10.1007/s11831-021-09665-9
    16. Giraldo, G., Lo, M., Davies, M., & Ellis, E. (2012). Thornton Creek Water Quality Channel, Urban Water Quality, and Environmental Benefits. In Low Impact Development: New and Continuing Applications (pp. 220-225). https://doi.org/https://doi.org/10.1061/41007(331)20
    17. Giudici, P., Gramegna, A., & Raffinetti, E. (2023). Machine Learning Classification Model Comparison. Socio-Economic Planning Sciences, 87, 101560. https://doi.org/https://doi.org/10.1016/j.seps.2023.101560
    18. Herold, T., Jordan, P., & Zwahlen, F. (2000). The influence of tectonic structures on karst flow patterns in karstified limestones and aquitards in the Jura Mountains, Switzerland. Eclogae Geologicae Helvetiae, 93, 349-362. https://doi.org/https://doi.org/10.5169/seals-168827
    19. Ibarra, E. M. (2012). A geographical approach to post-flood analysis: The extreme flood event of 12 October 2007 in Calpe (Spain). Applied Geography, 32(2), 490-500. https://doi.org/https://doi.org/10.1016/j.apgeog.2011.06.003
    20. Infante, D., Di Martire, D., Confuorto, P., & Ramondini, M. (2020, 2020//). Planning and Monitoring of Mitigation Measures in a Landslide-Affected Area Through Numerical Modelling, Conventional and Satellite Data. Geotechnical Research for Land Protection and Development, Cham.
    21. Jia, J., & Li, Y. (2023). Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends. Sensors, 23(21), 8824. https://www.mdpi.com/1424-8220/23/21/8824
    22. Johnson, R. H. (2008). Geologic controls on metal transport in ground water within an alpine watershed affected by historical mining. Geologic controls on metal transport in ground water within an alpine watershed affected by historical mining(1328), 36-41.
    23. Kwon, S. H., & Kim, J. H. (2021). Machine Learning and Urban Drainage Systems: State-of-the-Art Review. Water, 13(24), 3545. https://www.mdpi.com/2073-4441/13/24/3545
    24. Lee, E. H., Lee, Y. S., Joo, J. G., Jung, D., & Kim, J. H. (2016). Flood Reduction in Urban Drainage Systems: Cooperative Operation of Centralized and Decentralized Reservoirs. Water, 8(10), 469. https://www.mdpi.com/2073-4441/8/10/469
    25. Li, J. (2021). Exploring the potential of utilizing unsupervised machine learning for urban drainage sensor placement under future rainfall uncertainty. Journal of Environmental Management, 296, 113191. https://doi.org/https://doi.org/10.1016/j.jenvman.2021.113191
    26. Li, J., Wang, H., Zhou, L., Hu, J., Hu, J., & Liang, Y. (2011). Ecological effect, monitoring and warning of water pollution in Drainage basin. Chin. J. App. Eviron. Biol., 17(2), 268-272.
    27. Liao, L. (2019). Design of Optimal Model for Drainage Capacity of Marine Peripheral Building Drainage System. Journal of Coastal Research, 248-253. https://www.jstor.org/stable/26853808
    28. Marais, M., & Armitage, N. (2004). The measurement and reduction of urban litter entering stormwater drainage systems: Paper 2-Strategies for reducing the litter in the stormwater drainage systems. Water S.A, 30. https://doi.org/10.4314/wsa.v30i4.5100
    29. Oyewola, D. O., & Dada, E. G. (2022). Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer. SN Applied Sciences, 4(5), 143. https://doi.org/10.1007/s42452-022-05027-7
    30. Pania, H. G., Tangkudung, H., Kawet, L., & Wuisan, E. M. (2013). PERENCANAAN SISTEM DRAINASE KAWASAN KAMPUS UNIVERSITAS SAM RATULANGI. Jurnal Sipil Statik1(3), 164-170.
    31. Pozo, F., Tibaduiza, D. A., & Vidal, Y. (2021). Sensors for Structural Health Monitoring and Condition Monitoring. Sensors, 21(5), 1558. https://www.mdpi.com/1424-8220/21/5/1558
    32. Revathi, B., & Usharani, C. (2021 ). RAINFALL PREDICTION USING MACHINE LEARNING CLASSIFICATION ALGORITHMS. Machine Learning, CART, IDA, Prediction, Volume 9( 1 ), 2320-2882.
    33. Rjeily, Y. A., Abbas, O., Sadek, M., Shahrour, I., & Chehade, F. H. (2017). Flood forecasting within urban drainage systems using NARX neural network. Water Science and Technology, 76(9), 2401-2412. https://doi.org/10.2166/wst.2017.409
    34. Sakuradani, K., Koizumi, K., Oda, K., & Tayama, S. (2018). Development of a slope disaster monitoring system for expressway operation and maintenance control. Development of a slope disaster monitoring system for expressway operation and maintenance control, 13(4), 189-195.
    35. Shibuo, Y., & Furumai, H. (2021). Advances in Urban Stormwater Management in Japan: A Review. Journal of Disaster Research, 16(3), 310-320. https://doi.org/10.20965/jdr.2021.p0310
    36. Shukla, S., Srivastava, S., & D. Hardin, J. (2006). DESIGN, CONSTRUCTION, AND INSTALLATION OF LARGE DRAINAGE LYSIMETERS FOR WATER QUANTITY AND QUALITY STUDIES. Applied Engineering in Agriculture, 22(4), 529-540. https://doi.org/https://doi.org/10.13031/2013.21221
    37. Suliman, B., Meek, R., Hull, R., Bello, H., Portis, D., & Richmond, P. (2013). Variable Stimulated Reservoir Volume (SRV) Simulation: Eagle Ford Shale Case Study. In Unconventional Resources Technology Conference, Denver, Colorado, 12-14 August 2013 (pp. 544-552). https://doi.org/10.1190/urtec2013-057
    38. Tibaduiza, D., Torres-Arredondo, M. Á., Vitola, J., Anaya, M., & Pozo, F. (2018). A Damage Classification Approach for Structural Health Monitoring Using Machine Learning. Complexity, 2018, 5081283. https://doi.org/10.1155/2018/5081283
    39. van der Pol, T. D., van Ierland, E. C., Gabbert, S., Weikard, H. P., & Hendrix, E. M. T. (2015). Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change. Journal of Environmental Management, 154, 40-47. https://doi.org/https://doi.org/10.1016/j.jenvman.2015.02.016
    40. Vilarrasa, V., Carrera, J., Jurado, A., Pujades, E., & Vázquez-Suné, E. (2011). A methodology for characterizing the hydraulic effectiveness of an annular low-permeability barrier. Engineering Geology, 120(1), 68-80. https://doi.org/https://doi.org/10.1016/j.enggeo.2011.04.005
    41. Wang, Y., Xiang, C., Zhao, P., Mao, G., & Du, H. (2016). A bibliometric analysis for the research on river water quality assessment and simulation during 2000–2014. Scientometrics, 108(3), 1333-1346. https://doi.org/10.1007/s11192-016-2014-2
    42. Wedenig, M., Eichinger, S., Boch, R., Leis, A., Wagner, H., & Dietzel, M. (2023). Understanding of tunnel drainage scale formation by in-situ monitoring. Tunnelling and Underground Space Technology, 131, 104853. https://doi.org/https://doi.org/10.1016/j.tust.2022.104853
    43. Yaghoubzadeh-Bavandpour, A., Rajabi, M., Nozari, H., & Ahmad, S. (2022). Support Vector Machine Applications in Water and Environmental Sciences. In O. Bozorg-Haddad & B. Zolghadr-Asli (Eds.), Computational Intelligence for Water and Environmental Sciences (pp. 291-310). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2519-1_14
    44. Zhou, Q. (2014). A Review of Sustainable Urban Drainage Systems Considering the Climate Change and Urbanization Impacts. Water, 6(4), 976-992. https://www.mdpi.com/2073-4441/6/4/976
    45. Zhu, W., Tao, T., Yan, H., Yan, J., Wang, J., Li, S., & Xin, K. (2023). An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system. Hydrol. Earth Syst. Sci., 27(10), 2035-2050. https://doi.org/10.5194/hess-27-2035-2023
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Ngong Denga, A. A., Zaki, A., Nugroho, G., & Ikhsan, J. (2025). Bibliometric study: Structural health monitoring on drainage for classification learners using machine learning methods (2014-2023). Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/2272
  • Article viewed - 71