Analisis Komparatif dan Validasi Metodologi Delineasi DAS Otomatis Berbasis Artificial Intelligence (AI) Comparative Analysis and Validation of Automated Watershed Delineation Methodology Based on Artificial Intelligence (AI)
Main Article Content
Abstract
Accurate watershed delineation is a fundamental stage in hydrological modeling. However, conventional methods based on the Deterministic 8 (D8) algorithm often suffer from reduced accuracy in complex landscapes and areas affected by human activities. This study presents a comparative performance analysis of three delineation approaches, all of which utilize the D8 algorithm with high-resolution DEMNAS as input. The approaches include the standard implementations in HEC-HMS and WMS software, as well as an implementation on a newly developed innovative platform, ACAP (Advanced Catchment Analysis Platform). ACAP leverages Artificial Intelligence (AI) to pre-process and refine DEM data by integrating information from satellite imagery. These three approaches were tested on three dam catchment areas in Indonesia with diverse characteristics: Napun Gete, Batutegi, and Greneng. Their spatial accuracy was subsequently evaluated using the Jaccard Index (IoU) metric against ground truth data. The findings indicate that while HEC-HMS and WMS are highly reliable in natural watersheds, the ACAP platform demonstrated significant superiority in the complex Greneng catchment, achieving the highest IoU score (0.98) compared to the conventional platforms (0.84-0.85). This study concludes that the most impactful innovation for enhancing D8 delineation accuracy is not replacing the algorithm itself, but rather empowering it through smarter input data preparation. This highlights the significant potential of AI-augmented approaches for future precision hydrological analysis.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Asdak, C. (2018). Hidrologi dan Pengelolaan Daerah Aliran Sungai. Gadjah Mada University Press.
Garbrecht, J., & Martz, L. W. (1997). The TOPAZ model for Digital Landscape Analysis. US Department of Agriculture, Agricultural Research Service, Grazinglands Research Laboratory.
Horton, R. E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological Society of America Bulletin, Volume 56, Issue 3, pp. 275-370, https://doi.org/10,1130/0016-7606(1945)56[275:EDOSAT]2.0,CO;2.
Jenson, S. K., & Domingue, J. O. (1988). Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric Engineering and Remote Sensing, Volume 54, Issue 11, pp. 1593-1600,
Maidment, D. R. (Ed.). (1993). Handbook of Hydrology. McGraw-Hill.
Nugroho, U. C. (2012). Validasi delineation daerah aliran sungai DAS Ciliwung Hulu segmen Depok dengan metode Arc-Hydro dan metode manual [Skripsi Sarjana]. Universitas Indonesia.
O’Callaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing, Volume 28, Issue 3, pp. 323-344. https://doi.org/10,1016/0734-189X(84)90011-0,
Pan, F., et al. (2022). A review of digital elevation model (DEM)-based methods for watershed delineation. Water, Volume 14, Issue 20, 3298. https://doi.org/10,3390/w14203298.
Pratama, A. R., et al. (2022). Application of Artificial Intelligence in Disaster Management in Indonesia: A Systematic Literature Review. 2022 IEEE International Conference on Cybernetics and Computational Intelligence (ICCI), Bandung, Indonesia. https://doi.org/10,1109/ICCI55223.2022.9843288.
Quinn, P., Beven, K., Chevallier, P., & Planchon, O. (1991). The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. Hydrological Processes, Volume 5, Issue 1, pp. 59-79. https://doi.org/10,1002/hyp.3360050106.
Setiyowati, Y. A., Harisuseno, D., & Sajali, M. A. (2025). Evaluation of Catchment area delineation Methods: Comparison of TOPAZ on WMS with HEC-HMS 4.12. Geosfera Indonesia, Volume 10, Issue 2, pp. 125-145. https://doi.org/10,19184/geosi.v10i2.53253.
Shen, C. (2018). A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Research, Volume 54, Issue 11, pp. 8558-8593. https://doi.org/10,1029/2018WR023361.
Stodt, J., Reich, C., & Clarke, N. (2024). Unified Intersection Over Union for Explainable Artificial Intelligence. In: Arai, K. (Ed.), Intelligent Systems and Applications. Proceedings of the 2023 Intelligent Systems Conference (IntelliSys). Lecture Notes in Networks and Systems, Volume 823. Springer, Cham. pp. 758-770, https://doi.org/10,1007/978-3-031-55552-0_51.
Tarboton, D. G. (1997). A new method for the determination of flow directions and contributing areas in grid digital elevation models. Water Resources Research, Volume 33, Issue 2, pp. 309-319. https://doi.org/10,1029/96WR03137.
U.S. Army Corps of Engineers, Hydrologic Engineering Center (USACE-HEC). (2023). HEC-HMS User's Manual Version 4.11. Davis, CA.