<![CDATA[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.]]>
