A Bibliometric Analysis of Artificial Intelligence (AI) Applications in Hydrological Modeling

Dewi Ayu Sofia, Naufal Ariq Pratama

Abstract


The rapid advancement of artificial intelligence (AI) has opened new opportunities for developing hydrological models that are more adaptive, accurate, and efficient. This study aims to examine the research trends and directions concerning the application of AI in hydrological modeling using bibliometric analysis. A total of 136 relevant articles published between 2015 and 2025 were retrieved from the Semantic Scholar database using the Publish or Perish software. These records were then analyzed with VOSviewer to map keyword relationships and identify current research focuses. The results reveal a consistent upward trend in AI-based hydrological modeling publications, particularly since 2020. Among the top 15 cited articles, a total of 2,316 citations were recorded, averaging 17.03 citations per article. Keywords such as “LSTM,” “RNN,” “streamflow,” and “hydrological forecasting” appeared with the highest frequency and recentness, signifying a shift toward more adaptive and predictive modeling approaches. Furthermore, density visualization highlights a strong focus on deep learning models particularly LSTM and Support Vector Machines while showing opportunities for further exploration in hybrid models and climate resilience applications. Although limited to a single database, the study provides a methodologically robust overview of the current research landscape. The findings underscore the transformative role of AI, not merely as a computational tool, but as a key enabler for designing hydrological models that are data-driven, responsive, and capable of supporting sustainable water resource management in the face of environmental uncertaintie.

Keywords


Artificial Intelligence; Bibliometric Analysis; Deep Learning; Hydrological Modeling; Research Trends.

Full Text:

PDF

References


Aldina, S. N., & Maulana, A. (2023). Pemetaan gamifikasi pada mata kuliah hidrologi berdasarkan motivasi belajar mahasiswa pendidikan teknik bangunan. Jurnal Pendidikan Teknik Bangunan, 3(1), 21–32.

Al Husaeni, D. F., & Nandiyanto, A. B. D. (2022). Bibliometric using vosviewer with publish or perish (using google scholar data): from step-by-step processing for users to the practical examples in the analysis of digital learning articles in pre and post covid-19 pandemic. ASEAN Journal of Science and Engineering, 2(1), 19–46.

Alhajir, A. D., Dodgson, J., Lim, J., Phi, T. M., Peh, J., Pattirane, A. R. J., & Poovaragan, L. (2025). Generalising from Self-Produced Data: Model Training Beyond Human Constraints. 1–16.

Allawi, M. F., Jaafar, O., Hamzah, F. M., Abdullah, S. M. S., & El Shafie, A. (2018). Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environmental Science and Pollution Research, 25, 13446–13469.

Ardabili, S., Mosavi, A., Dehghani, M., & Varkonyi-Koczy, A. R. (2019). Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. Engineering for Sustainable Future, 52–62.

Chia, M. Y., Huang, Y. F., Koo, C. H., & Fung, K. F. (2020). Recent advances in evapotranspiration estimation using artificial intelligence approaches with a focus on hybridization techniques—A review. Agronomy, 10(1), 1-33.

Chow, V. Te, Maidment, D. R., & Mays, L. W. (1988). Applied Hydrology. McGraw-Hill.

Diop, L., Samadianfard, S., Bodian, A., Yaseen, Z. M., Ghorbani, M. A., & Salimi, H. (2020). Annual rainfall forecasting using hybrid Artificial Intelligence Model: integration of multilayer perceptron with Whale Optimization Algorithm. Water Resources Management, 34, 733–746.

Fahimi, F., Yaseen, Z. M., & El-shafie, A. (2017). Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and Applied Climatology, 128, 875–903.

Faruq, M. Al, & Putra, Y. H. S. (2024). Determinan keputusan membayar zakat pada BAZNAS dan LAZ: Studi Bibliometric VOSviewer dan Literature Review. Jurnal Samudra Ekonomi Dan Bisnis, 15(1), 144–161.

Ginanjar, M. R., & Yuningsih, S. M. (2018). Penerapan model kendali mutu data hidrologi dalam rangka peningkatan kualitas data. Jurnal Sumber Daya Air, 13(2), 131–146.

Guzman, S. M., Paz, J. O., Tagert, M. L. M., & Mercer, A. E. (2019). Evaluation of seasonally classified for the prediction of daily groundwater levels: NARX Networks Vs Support Vector. Environmental Modeling & Assessment, 24, 223–234.

Karbasi, M. (2018). Forecasting of multi-step ahead reference evapotranspiration using Wavelet- Gaussian process regression model. Water Resources Management, 32, 1035–1052.

Khaki, M., Yusoff, I., & Islami, N. (2014). Application of the Artificial Neural Network and Neuro-fuzzy System for assessment of groundwater quality. CLEAN Soil Air Water, 43(4), 551–560.

Kumanlioglu, A. A., & Fistikoglu, O. (2019). Performance enhancement of a Conceptual Hydrological Model by integrating Artificial Intelligence. Journal of Hydrologic Engineering, 24(11).

Ley, A., Bormann, H., & Casper, M. (2024). Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model. Hydrology Research, 55(6), 613–627.

Long, J., Wang, L., Chen, D., Li, N., Zhou, J., Li, X., Guo, X., Liu, H., Chai, C., & Fan, X. (2024). Hydrological projections in the third pole using artificial intelligence and an observation-constrained cryosphere-hydrology model. Earth’s Future, 12(4), 1–20.

Mashaly, A. F., & Fernald, A. G. (2020). Identifying capabilities and potentials of system dynamics in hydrology and water resources as a promising modeling approach for water management. Water (Switzerland), 12(5), 1432.

McMillan, H. K., Westerberg, I. K., & Krueger, T. (2018). Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews: Water, 5(6), 1–14.

Mufhidin, A., & Maksum, A. (2021). Desain road barrier untuk persimpangan jalan (studi kasus: jalan layang jakarta, bandung). Jurnal Pendidikan Teknik Bangunan, 1(2), 69–78.

Nandiyanto, A. B. D., Biddinika, M. K., & Triawan, F. (2020). How bibliographic dataset portrays decreasing number of scientific publication from Indonesia. Indonesian Journal of Science and Technology, 5(1), 154–175.

Nema, M. K., Khare, D., & Chandniha, S. K. (2017). Application of artificial intelligence to estimate the reference evapotranspiration in sub-humid Doon valley. Applied Water Science, 7(7), 3903–3910.

Oyebode, O., & Stretch, D. (2019). Neural network modeling of hydrological systems: A review of implementation techniques. Natural Resource Modeling, 32(1), 1-22.

Ramzi, K., Nadir, M., Mohamed Tewfik, B., & Hakim, D. K. (2024). Hydrological forecasts modeling using artificial intelligence and conceptual models of kébir-rhumel watershed, algeria. Ecological Engineering & Environmental Technology, 25(9), 68–80.

Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. (2018). Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theoretical and Applied Climatology, 135, 449–462.

Tanty, R., & Desmukh, T. S. (2015). Application of artificial neural network in hydrology- a review. International Journal of Engineering Research and Technology, 4(6), 184-188..

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

Yaseen, Z. M., Allawi, M. F., Yousif, A. A., Jaafar, O., Hamzah, F. M., & El-shafie, A. (2018). Non-tuned machine learning approach for hydrological time series forecasting. Neural Computing and Applications, 30, 1479–1491.

Yaseen, Z. M., Faris, H., & Al-Ansari, N. (2020). Hybridized extreme learning machine model with salp swarm algorithm: a novel predictive model for hydrological application. Complexity, 2020(1), 10-14.

Zhu, S., & Piotrowski, A. P. (2020). River/stream water temperature forecasting using artificial intelligence models: a systematic review. Acta Geophysica, 68, 1433–1442.




DOI: https://doi.org/10.17509/jptb.v5i1.87062

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Universitas Pendidikan Indonesia (UPI)

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

Jurnal Pendidikan Teknik Bangunan

 

 
slot online

POSTOTO787

Suge789