DKI Jakarta, Indonesia's capital province and one of Southeast Asia's most densely urbanised regions, presents unique challenges for hydrological analysis. The province's complex topography, characterised by relatively flat coastal plains in the north transitioning to gently undulating terrain in the south, creates intricate drainage patterns that warrant detailed investigation. Understanding these patterns becomes particularly relevant given Jakarta's susceptibility to flooding and the ongoing need for comprehensive water management strategies.
This project emerged from a personal initiative to expand technical competencies in geospatial science, specifically focusing on automated hydrological modelling using contemporary tools and methodologies. By selecting DKI Jakarta as the study area, the work addresses both a technically challenging landscape and a geographically significant region, creating an opportunity to demonstrate analytical capabilities whilst engaging with meaningful spatial patterns.
The primary aim was to construct a comprehensive drainage network model for DKI Jakarta Province through systematic processing of digital elevation data. The work sought to achieve several interconnected objectives: deriving flow direction patterns across the provincial terrain, calculating flow accumulation to identify natural water concentration zones, extracting stream networks that accurately represent hydrological reality, and evaluating the efficacy of AI-assisted code generation within professional GIS workflows.
A secondary objective involved exploring DeepSeek AI as a collaborative tool for Python script development. This experimental approach aimed to assess whether artificial intelligence could accelerate geoprocessing workflow design whilst maintaining analytical rigour and methodological transparency. The investigation thus serves dual purposes as both a technical demonstration and a methodological exploration of emerging practices in geospatial analysis.
The analytical framework centred on ArcGIS Pro as the primary geospatial platform, leveraging its comprehensive hydrological analysis capabilities through the Spatial Analyst extension. ArcPy, the Python site package for ArcGIS, enabled workflow automation and reproducible processing sequences. DeepSeek AI served as a collaborative partner in code generation, providing initial script frameworks that were subsequently evaluated, refined, and adapted to meet specific project requirements.
The foundational dataset comprised SRTM (Shuttle Radar Topography Mission) 30-metre resolution digital elevation model covering DKI Jakarta Province, supplemented by administrative boundary shapefiles defining the precise extent of the study area. The methodology followed established hydrological analysis protocols, including terrain preprocessing through sink filling, flow direction computation using the D8 algorithm, flow accumulation calculation, and stream network extraction based on threshold values. This approach represents standard practice in terrain analysis whilst allowing for parameter optimisation specific to the Jakarta context.
The project commenced with careful consideration of data requirements and workflow architecture. Initial research identified SRTM 30-metre data as an appropriate resolution for provincial-scale analysis, offering sufficient detail to capture significant drainage features whilst remaining computationally manageable. Administrative boundary data for DKI Jakarta Province was obtained from Indonesian geospatial repositories, ensuring accurate delimitation of the study area.
A dedicated project environment was established within ArcGIS Pro, with appropriate coordinate system specifications to ensure spatial consistency. The workspace was organised to facilitate systematic data management, with clearly delineated folders for raw inputs, intermediate processing outputs, and final results. This organisational structure proved essential for maintaining workflow clarity and enabling iterative refinement of analytical parameters.
The decision to engage DeepSeek AI in the code development process represented a deliberate methodological choice. Rather than relying solely on conventional script development approaches, the project embraced an experimental mindset, exploring how artificial intelligence might contribute to geoprocessing workflow design. This choice reflected both curiosity about emerging tools and a pragmatic interest in workflow efficiency. DeepSeek was selected specifically for exploratory purposes, offering an opportunity to evaluate AI-generated code quality, adaptability, and integration with established GIS platforms.
Data preparation proceeded through carefully sequenced operations designed to transform raw elevation data into an analysis-ready format. The SRTM DEM tile covering DKI Jakarta was first assessed for completeness and quality. Whilst SRTM data is generally reliable, urban environments and flat coastal areas can present challenges, including reduced elevation variation that may obscure subtle topographic features or introduce processing artefacts.
The provincial boundary shapefile was used to clip the DEM to the precise extent of DKI Jakarta, creating a focused dataset that eliminated extraneous areas and optimised processing efficiency. This clipping operation ensured that subsequent analyses would concentrate computational resources on the study area itself, improving both processing speed and analytical clarity.
With the clipped DEM prepared, the ArcPy workflow was implemented following this sequence:
- Sink Filling: The Fill function identified and eliminated topographic depressions in the elevation surface. These depressions, often artefacts of data collection or interpolation processes, can interrupt continuous flow paths and create unrealistic drainage patterns. Filling ensures that every cell has a viable downslope pathway, enabling coherent flow routing across the entire surface.
- Flow Direction Calculation: The FlowDirection function applied the D8 algorithm to determine the steepest descent direction from each cell to its eight surrounding neighbours. This operation transforms the continuous elevation surface into a directional grid where each cell value encodes the direction water would travel based on local topographic gradients. The resulting flow direction raster forms the foundation for all subsequent flow-based analyses.
- Flow Accumulation Computation: Using the flow direction grid, the FlowAccumulation function calculated the cumulative number of upstream cells flowing into each downstream position. This operation effectively simulates water concentration across the landscape, with high accumulation values indicating locations where runoff naturally converges to form channels. The accumulation surface provides a quantitative basis for identifying potential stream locations.
- Stream Network Extraction: A threshold value was applied to the flow accumulation raster to distinguish stream cells from non-stream areas. Cells exceeding the threshold were classified as part of the drainage network and converted from raster to vector format using the StreamToFeature function. This transformation created polyline features representing the extracted stream network, suitable for cartographic display and further spatial analysis.
The threshold selection process required careful consideration. Too low a value would generate an overly dense network dominated by ephemeral or spurious channels, whilst too high a threshold might omit significant tributaries. Through iterative testing and visual assessment against the underlying terrain, an appropriate threshold was identified that balanced network completeness with hydrological plausibility.
Analysis and Interpretation: The resulting flow direction output revealed a coherent drainage structure aligned with the province's topographic gradient. Flow paths predominantly originate from the elevated southern portions of the province and converge northward towards Jakarta Bay, reflecting the natural north-sloping terrain. This directional consistency validated the hydrological model's fidelity to actual landscape conditions.
Notably, the flow direction raster exhibited some rectangular zones, particularly in the upper sections of the mapped area. These patterns likely reflect Jakarta's extensive urban development, where built infrastructure and flattened terrain create localised areas of minimal elevation variation. Such features are characteristic challenges in urban hydrological modelling, where anthropogenic modifications complicate natural drainage interpretation. Despite these artefacts, the lower portions of the flow direction output displayed detailed branching patterns indicative of successful terrain analysis.
The stream network visualisation presented purple polylines overlaying a shaded relief base, creating intuitive visual communication of drainage structure. The network exhibited dendritic characteristics with coherent branching and convergence patterns, typical of natural watersheds where tributaries progressively merge into larger channels. The network density appeared well-calibrated: sufficiently detailed to capture meaningful hydrological features without excessive fragmentation that would suggest over-sensitivity to minor topographic variations.
This balance validated both the threshold selection and the overall methodological approach. The stream network credibly represents potential surface runoff pathways across DKI Jakarta, with flow paths originating from higher elevations and converging towards the northern coastal areas. The spatial distribution and hierarchical structure of extracted streams align well with topographic controls, demonstrating that the automated workflow successfully captured essential drainage characteristics despite the complexities of urban terrain.
Reflections on AI-Assisted Development: The engagement with DeepSeek AI proved strategically valuable throughout the code development process. DeepSeek provided initial script frameworks that demonstrated modular structure, efficient logic flow, and appropriate geoprocessing sequences. The AI-generated code required minimal adaptation, primarily involving path specifications and parameter fine-tuning rather than fundamental restructuring.
This experience validated the potential of artificial intelligence as a collaborative partner in geospatial analysis. DeepSeek's code exhibited several strengths: clear commenting that enhanced readability, logical organisation of processing steps, appropriate error handling through workspace configuration, and efficient use of ArcPy functions. These qualities accelerated workflow development whilst maintaining full transparency over processing logic and parameter control.
Beyond mere efficiency gains, engaging with AI-generated code offered opportunities for methodological reflection. Reviewing DeepSeek's approach illuminated alternative coding structures and prompted consideration of why certain functions were sequenced in particular ways. This dialogue between human expertise and artificial intelligence fostered a deeper understanding of geoprocessing workflows, demonstrating how AI tools can serve educational as well as practical purposes.
The decision to experiment with DeepSeek thus proved well-founded, positioning the workflow within contemporary trends towards integrated human-AI collaboration in scientific computing. This approach represents not simply a technical convenience but a forward-looking engagement with the evolving methodological landscape of geospatial science.
The project successfully generated a comprehensive drainage network model for DKI Jakarta Province, producing several interconnected outputs that together characterise the region's hydrological structure. The hydrologically corrected DEM provides a terrain surface free from spurious depressions, establishing a reliable foundation for flow-based analyses. The flow direction raster quantifies water movement patterns across the province, revealing the dominant north-flowing drainage that characterises Jakarta's topography.
The flow accumulation surface identifies concentration zones where surface runoff naturally converges, providing insight into potential channel locations and relative drainage importance. The extracted stream network, comprising vector polylines derived from threshold-based classification, represents the synthesised product of these analytical layers. This network credibly delineates drainage pathways that align with topographic controls and hydrological principles. From a technical perspective, the work demonstrates proficiency in automated geospatial workflows, systematic data preprocessing, hydrological modelling techniques, and cartographic visualisation. The successful integration of AI-assisted code generation illustrates adaptability to emerging tools and willingness to explore innovative methodological approaches. The project thus serves its intended purpose as a portfolio demonstration, showcasing capabilities relevant to hydrological analysis, environmental assessment, and spatial planning applications.
Beyond technical outputs, the project offers insights into Jakarta's drainage structure that could inform various applied contexts. Understanding natural flow pathways becomes particularly relevant for flood risk assessment, infrastructure planning, urban drainage design, and environmental management. Whilst this demonstration project was not conducted for operational purposes, the methodological approach and resulting network could be adapted for applied studies requiring hydrological characterisation of the Jakarta region.
This drainage network analysis for DKI Jakarta Province successfully demonstrates advanced capabilities in automated hydrological modelling using contemporary GIS platforms and workflows. Through systematic processing of SRTM elevation data, the project generated a coherent stream network that credibly represents surface water flow patterns across this complex urban province.
The work showcases technical proficiency across multiple domains: spatial data management, automated geoprocessing with Python, hydrological analysis techniques, parameter optimisation, and cartographic communication. The successful integration of DeepSeek AI into the workflow development process illustrates openness to emerging tools and methodological innovation, positioning the approach within contemporary trends towards human-AI collaboration in scientific computing. As geospatial science continues to evolve through integration with artificial intelligence, machine learning, and increasingly sophisticated analytical techniques, projects such as this demonstrate how traditional hydrological methods can be enhanced through contemporary tools whilst maintaining analytical rigour and methodological transparency. The approach embraces innovation without abandoning established principles, reflecting a balanced perspective on the future trajectory of spatial analysis.