Digital illustration of a safe route through a layered landscape.

Safe Passage: Predicting Disaster Evacuation Routes with Digital Elevation Models

Soraya Malik in Tech & Innovation August 2025 • 5 min read.

"Discover how cutting-edge digital elevation models are revolutionizing disaster recovery by identifying safe evacuation routes, ensuring communities can reach safety faster and more efficiently."

Natural disasters disrupt normal routes, stranding individuals in hazardous zones, and making communication and evacuation difficult. Globally, natural disasters are becoming more frequent and intense, leading to increased life and property loss. Predicting these disasters remains challenging, with unexpected events like flash floods causing extreme disruption, underscoring the critical need for technological solutions to enhance rescue operations and support affected populations.

Existing disaster prediction and alert systems offer valuable information about intensity, date, and time, but the situation changes drastically post-disaster, making pre-existing geographic data obsolete. Determining damage intensity becomes problematic because the geographic landscape is different. The ability to dynamically assess and adapt to post-disaster conditions is key to effective disaster response. Technology must provide updated, real-time analysis to guide victims and relief organizations.

One promising approach leverages Digital Elevation Models (DEMs) to analyze terrain and identify safe evacuation routes. By integrating DEMs with Geographic Information Systems (GIS), rescue efforts can be significantly enhanced. This article explores how a prototype system, developed using Arc geographic information system runtime SDK and APIs, predicts safe routes based on elevation values, providing a lifeline for those affected by disasters.

How Digital Elevation Models are Changing Disaster Response

Digital illustration of a safe route through a layered landscape.

Traditional methods for managing climatic hazards involve geographic information scientists assessing risks over time. Early studies used composite flood hazard indices, considering factors like distance to water sources, population density, and the availability of wetlands and high ground areas. Geographic Information System (GIS) techniques, combined with remote sensing technology, enhance prediction accuracy. These methods provide initial-level solutions to manage flood-related problems using GIS and remote sensing.

The proposed system uses services provided by Environmental Systems Research Institute (ESRI), California and their tool, ArcGIS. ArcGIS enables the user to manipulate data and add maps from online services. It relies on a Digital Elevation Model (DEM) layer added to the basemap, which provides elevation values from sea level. The system performs spatial analysis to display these elevation values as point features. Key points are marked on the map, and a spatial analysis tool extracts DEM levels for these points. Route analysis identifies the quickest and safest paths, factoring in elevation levels.

DEM Layer Integration: Adding elevation data to enhance map detail.
Spatial Analysis: Extracting key elevation data for precise points.
Route Analysis: Identifying the safest path based on elevation.
ArcGIS Utilization: Leveraging advanced mapping tools for disaster response.

The system operates by taking user inputs in the form of latitude and longitude to mark the disaster point on the map. It then generates a radius around the disaster point, and calculates a threshold value based on the average DEM level of the area within the radius. The rescue or evacuation point is marked outside the disaster radius, and has a higher or lower DEM value than the threshold value, based on the disaster type. The system analyzes pre-disaster scenarios to predict safe areas, using a world street map basemap retrieved from ESRI online services. The route-finding algorithm considers the disaster type and its properties, such as water accumulation in floods. By identifying areas with lower DEM levels, the system helps determine flood-affected zones and guides users to higher, safer elevations.

Conclusion: Charting a Course to Safety

In disaster scenarios, predictable routes to safety are essential. This research provides a method for predicting routes to rescue points from disaster locations, enhancing rescue operations. By integrating digital elevation models with GIS technology, this system offers a crucial tool for disaster preparedness and response. As technology advances, these predictive tools will become increasingly vital for safeguarding communities and minimizing the impact of natural disasters. People will be able to shift to the refuge point as the routes are unknown, hence our work provided a way to predict the routes to reach the rescue point from the disaster point. These routes could be even used by the relief providers to reach the disaster point.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.22159/ajpcr.2017.v10s1.19539, Alternate LINK

Title: Disaster Recovery Through Prediction Of Safe Route Using Dem Levels

Subject: Pharmacology (medical)

Journal: Asian Journal of Pharmaceutical and Clinical Research

Publisher: Innovare Academic Sciences Pvt Ltd

Authors: Sangavi Vp, N Mounika, S Graceline Jasmine

Published: 2017-04-01

Everything You Need To Know

How do Digital Elevation Models improve disaster recovery efforts?

Digital Elevation Models, or DEMs, enhance disaster response by providing detailed terrain analysis. Integrating DEMs with Geographic Information Systems, or GIS, allows for the prediction of safe evacuation routes, enabling faster and more efficient rescue operations in disaster-stricken areas. This is especially crucial because traditional geographic data becomes obsolete after a disaster changes the landscape.

How does the prototype system utilizing ArcGIS predict safe evacuation routes?

The prototype system uses the ArcGIS geographic information system runtime SDK and APIs to predict safe routes. It integrates a Digital Elevation Model, or DEM, layer providing elevation values, and performs spatial analysis to identify key points. Route analysis then determines the safest paths based on elevation, factoring in disaster type to guide individuals to safer, higher elevations during events like floods. The Environmental Systems Research Institute, ESRI, provides services and tools like ArcGIS that are crucial to its operation.

How is using Digital Elevation Models with Geographic Information Systems better than traditional disaster management methods?

Traditional methods use composite flood hazard indices and GIS techniques combined with remote sensing technology for risk assessment. The enhancement is in using Digital Elevation Models, or DEMs, within a Geographic Information System, or GIS. This enables a dynamic adaptation to post-disaster conditions, offering updated, real-time analysis to guide victims and relief organizations by analyzing terrain and identifying safe evacuation routes based on elevation values. This is a significant improvement over pre-existing, static geographic data.

How does the system analyze disaster zones to determine the safest evacuation routes?

The system uses user-provided latitude and longitude to mark the disaster point and generates a radius. It calculates a threshold based on the average Digital Elevation Model, or DEM, level within that radius. The system then analyzes the pre-disaster scenarios using the ESRI world street map basemap and the route-finding algorithm considers the disaster type (e.g., water accumulation in floods) to guide users to safe elevations. Factoring in disaster properties ensures a more accurate assessment of affected zones and safer evacuation routes.

What are the limitations of using Digital Elevation Models for disaster evacuation, and how could these be improved?

While Digital Elevation Models, or DEMs, integrated with Geographic Information Systems, or GIS, significantly improve disaster response by predicting safer evacuation routes, there are still limitations. The success relies on the accuracy and availability of DEM data, and the real-time adaptability of the system. Future advancements could include integrating real-time sensor data, improving predictive algorithms, and enhancing communication systems to guide individuals more effectively during disasters. Addressing these limitations will further minimize the impact of natural disasters and enhance community safeguarding.