{"id":27172,"date":"2025-12-11T05:09:22","date_gmt":"2025-12-11T05:09:22","guid":{"rendered":"https:\/\/dataforpublicgood.org.in\/?p=27172"},"modified":"2025-12-15T05:10:23","modified_gmt":"2025-12-15T05:10:23","slug":"integrating-spatial-analytics-and-routing-algorithms-for-flood-response-the-firs-approach","status":"publish","type":"post","link":"https:\/\/dataforpublicgood.org.in\/cdpg\/blog\/integrating-spatial-analytics-and-routing-algorithms-for-flood-response-the-firs-approach\/","title":{"rendered":"Integrating Spatial Analytics and Routing Algorithms for Flood Response: The FIRS Approach"},"content":{"rendered":"\t\t
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Every monsoon, India braces itself for one of its most persistent natural hazards: flooding. It is driven by intense monsoons, cyclones, tsunamis, or earthquake-damaged dams failure causing extensive loss of life, infrastructure damage, and long term economic disruption. According to the National Flood Commission (1980), about 0.4 million km\u00b2 of India\u2019s area is affected by floods annually, including 0.037 million km\u00b2 of crop area, with average financial losses of roughly INR 13,000 million each year (Yogesh Kumar Singh et.al; \u201cFlood Response System\u2014A Case Study\u201d 2017)<\/p>

Among the cities that repeatedly feel this impact is Varanasi, perched along the curving left bank of the Ganga. Its low-lying alluvial terrain and dense population make it particularly vulnerable when river levels rise. Even a small spike in water levels can inundate roads, isolate neighbourhoods, and cut off access to essential services.<\/p>

But what if communities, administrators, and responders could see rising flood levels before they arrived? What if they had a clear, map-based plan for where to evacuate people down to the nearest school or hospital?<\/p>

The Flood Impact & Response System (FIRS), an internally developed web dashboard by CDPG, is a prototype designed to address this challenge by leveraging the datasets available on the GDI platform, outlined below:<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Overview<\/h2>

The Flood Impact and Response System (FIRS) Dashboard provides a localized, interactive platform tailored to Varanasi\u2019s flood-prone environment, combining flood inundation simulation with evacuation route planning through elevation-aware routing and clustering techniques. In contrast to broader national and state platforms such as the Central Water Commission\u2019s flood forecasting portal or state disaster management dashboards FIRS delivers fine-scale, village-level decision support aligned with local administrative boundaries and known evacuation sites.<\/p>

The FIRS (Flood Information and Response System) is built using Python Shiny, which manages the system\u2019s logic, user interactions, and dynamic updates, enabling real-time data-driven decisions. Spatial and optimization algorithms in Python handle key tasks such as computing village centroids, identifying safe evacuation centers, and generating optimal evacuation routes. Data preprocessing and management are carried out using Pandas, ensuring efficient handling of large datasets and maintaining data integrity throughout the workflow.<\/p>

The user interface of FIRS is built with HTML and CSS for a clean, responsive layout. JavaScript is used to convert CSV data into JSON, enabling dynamic visualizations and real-time updates. For mapping, the system uses Leaflet, which allows accurate and interactive display of village centroids, evacuation centers, and optimized routes. Together, these frameworks create an efficient and user-friendly platform for flood risk assessment and evacuation planning.<\/p>

The dashboard is structured into two functional modules: the Flood Inundation Visualizer and the Evacuation Planner.<\/p>

Flood Inundation Mapping:<\/h2>

The Flood Impact and Response System (FIRS) interface provides an intuitive way to model flood inundation using Digital Elevation Model (DEM) data. The system employs a threshold-based flood fill algorithm, which first converts the Digital Elevation Model into a binary raster, marking all pixels below the selected elevation as potential flood zones. Using the lowest-elevation point along the riverbank as the seed, the algorithm performs a connected-region traversal to delineate only those low-lying areas that are hydrologically reachable, thereby filtering out isolated depressions that would not naturally flood. The dashboard provides real-time visualization of both the \u201cbefore flood\u201d terrain and \u201cafter flood\u201d inundation layer, dynamically updating results as the elevation threshold slider is adjusted. Additionally, an animation feature illustrates the progressive spread of floodwaters, enhancing user understanding of flood dynamics. By combining elevation-driven modeling with interactive visualization, the flood inundation mapping module provides fast, actionable insights essential for early warning, damage assessment, and emergency planning in flood-prone urban environments. A color-coded elevation legend green for low areas, yellow for moderate elevation, and brown for high ground helps users interpret the map easily. As the slider moves, the system recalculates and updates the flood extent in real time, showing how rising water levels could impact different parts of Varanasi. This interactive design enables quick assessment of vulnerable zones and supports informed decision-making during flood events.<\/p>

<\/p>

Figure 1. Flood Inundation Visualizer illustrating the pre- and post-flood scenario derived using an elevation threshold of 85 m.<\/em><\/p>

Flood Evacuation Route Planner:<\/h2>

The Evacuation Planner module of the FIRS dashboard delivers a sophisticated, elevation-aware decision-support system designed to guide safe and efficient evacuation during flood events. Leveraging the DEM-based inundation layer, the module first identifies all affected village centroids and applies K-Means clustering to form spatially coherent evacuation zones, reducing routing redundancy and improving computational efficiency. Each cluster is assigned a representative village centroid that functions as the primary origin for evacuation modeling. A topologically clean, elevation-annotated road network is then integrated, where each edge is weighted using a hybrid cost function combining Euclidean distance with elevation-based penalties to avoid vulnerable, low-lying segments. The system employs a modified Dijkstra algorithm with early-exit optimization to rapidly compute elevation constrained routes to the five nearest education or healthcare facilities identified as potential evacuation centers. Results are visualized through an interactive map and a detailed tabular interface that reports optimal routes, recommended centers, distances, estimated travel times, and centroid coordinates for each village.<\/p>

<\/p>

Figure 2. Evacuation Planner tab of the dashboard displaying functionalities like map display, option to select evacuation center and to export<\/em><\/p>

Beyond its technical workflow, the Evacuation Planner offers significant operational advantages. By integrating clustering, hazard-aware routing, and multi-destination analysis, it ensures that evacuation decisions are both spatially coherent and contextually grounded in real flood dynamics. The elevation-aware routing enhances safety by prioritizing high-ground pathways, while the early-exit search strategy reduces processing time, enabling near\u2013real-time response even under resource constraints. The combination of visual and tabular outputs improves situational awareness, supports transparent decision-making, and allows emergency teams to quickly prioritize villages, allocate resources, and coordinate field operations. Overall, the module transforms complex spatial analytics into actionable evacuation strategies, making it a powerful tool for disaster response and urban flood resilience.<\/p>

<\/p>

Figure 3. Evacuation Planner tab of the dashboard displaying tabular view of distance and time of travel from zone centroids to evacuation centers<\/em><\/p>

Conclusion:<\/h2>

The Flood Impact and Response System (FIRS) Dashboard offers a comprehensive and practical solution for flood preparedness, impact assessment, and evacuation planning in Varanasi\u2019s flood-prone urban environment. By integrating DEM-based flood inundation simulation with elevation-aware evacuation routing, the platform delivers fine-scale, actionable insights that support informed and timely decision-making. Its interactive maps, real-time visualization, and exportable route and impact summaries make it especially useful for emergency responders, planners, and local authorities who must quickly identify at-risk areas, evaluate safe routes, and coordinate evacuations during critical hours. The dashboard\u2019s lightweight computational design ensures rapid scenario generation, allowing users to examine flood impacts for varying water-level thresholds and immediately understand which villages, roads, and facilities are affected.<\/p>

While the system still faces limitations such as reliance on DEM accuracy, static evacuation site data it effectively demonstrates how open-source spatial data and efficient algorithms can be transformed into an accessible, operational tool for disaster management. Looking ahead, integrating real-time flood and traffic updates, dynamic population data, multi-hazard layers, and broader accessibility features would significantly enhance its effectiveness. With these advancements, FIRS has strong potential for scaling to other flood-vulnerable regions across India, contributing meaningfully to improved urban flood resilience and emergency response capabilities.<\/p>

For more information, please visit: https:\/\/firs.geospatial.org.in\/<\/a><\/p>

Reference<\/h2>
  1. https:\/\/ndrf.gov.in\/sites\/default\/files\/FLOOD.pdf<\/a><\/li>
  2. Mallikarjun Mishra*1 , Vikas Dugesar2 , and K.N.Prudhvi Raju3 \u201cFlood Risk and Impact Analysis of Varanasi City Region, India\u201dVolume 66, Issue 1, 2022 Journal of Scientific Research of The Banaras Hindu University. DOI: 10.37398\/JSR.2022.660103<\/li>
  3. Vikas Yadav1 \u00b7 Ashutosh Kainthola1 \u00b7 Gaurav Kushwaha1 \u00b7 Vishnu H. R. Pandey1 \u00b7 Abhi S. Krishna\u201d Assessing the impact of urbanization on flood patterns in Varanasi,
    India using Google Earth Engine\u201d     https:\/\/doi.org\/10.1007\/s44327-025-00051-9<\/a><\/li>
  4. Yogesh Kumar Singh 1,*,Upasana Dutta 1,T. S. Murugesh Prabhu 1,I. Prabu 1,Jitendra Mhatre 1,Manoj Khare 1,Sandeep Srivastava 1 and Subasisha Dutta 2
    \u201cFlood Response System\u2014A Case Study\u201d     https:\/\/doi.org\/10.3390\/hydrology4020030<<\/a> \/li><\/li><\/ol>
    Authors<\/b>
    Sneha R<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t
    <\/div>","protected":false},"excerpt":{"rendered":"

    Every monsoon, India braces itself for one of its most persistent natural hazards: flooding. It is driven by intense monsoons, cyclones, tsunamis, or earthquake-damaged dams failure causing extensive loss of life, infrastructure damage, and long term economic disruption. 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