Map-Supported Decision Systems in Disaster Management

Climate change is infiltrating our lives rapidly and effectively. Droughts, floods, wildfires... Amidst this chaos, we have a powerful compass: Geographic Information Systems (GIS) and remote sensing technologies. With these technologies, we can monitor climate risks virtually live—pixel by pixel.

Modern maps don't just show rivers and mountains; they also reveal water scarcity, agricultural productivity, and climate stress. Thanks to satellite imagery and sensor data, maps can be updated almost daily, even hourly. Map-based decision-making systems are crucial in disaster management. Is a region at risk of flooding? Has the probability of forest fires increased? Where should urban planners shift construction, where should disaster response teams deploy first aid, and on what day should farmers irrigate? The answers to these questions are hidden in GIS-based climate maps. The exciting part is that, when combined with artificial intelligence, these maps can make predictions.

Data Sources and Technological Infrastructure

The first step in managing climate risks is accessing accurate and reliable data. Map-supported decision systems are not just visual guides; they are also strategic tools informed by scientific data. So, what sources feed these powerful systems, and how is the data collected?

LIDAR and Elevation Data

LIDAR technology, which reveals the three-dimensional structure of the surface with high precision, plays a critical role, particularly in flood risk analyses. Digital elevation models (DEMs) generated by LIDAR form the basis of risk maps by predicting where water will accumulate. Their cost-effectiveness and accuracy make them indispensable in engineering projects.

Satellite Images and Aerial Photographs

Satellites like Landsat, Sentinel, and MODIS collect data from around the globe, providing a wealth of information, from surface cover changes to the status of water resources. Radar (SAR) and infrared sensor technologies enable continuous and uninterrupted data flow, regardless of weather conditions. This enables long-term climate monitoring and the detection of sudden changes.

Sensor Networks (IoT)

Values such as soil moisture, water level, and air temperature can be monitored in real time thanks to installed sensors. This real-time data transforms decision support systems from static to dynamic, instantly responsive tools. This is precisely what keeping a finger on the pulse of nature is all about.

Climate and Meteorological Models

We must plan not only for today but also for tomorrow. Long-term climate models and weather simulations help us predict the likelihood of disasters such as extreme temperatures, droughts, or floods. These predictions create risk scenarios that inform pre-disaster planning.

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All this data is brought together in Geographic Information Systems (GIS) software. Professional tools like ArcGIS and QGIS integrate satellite imagery, sensor data, and other geographic layers, providing a powerful infrastructure for analysis. These systems not only visualize data but also enable data-driven decisions.

Strategic Observation with Remote Sensing

Remote sensing technologies play a key role in the data collection process. Data obtained from satellites, drones, and ground-based sensors are processed on big data platforms. For example, LIDAR data is compared with water levels for flood risk analysis, and drought monitoring is conducted using vegetation indices like NDVI. Every step is shaped by engineering principles.

Having a robust data infrastructure in the fight against climate change is not a choice; it's a necessity. These systems enable us to detect environmental risks earlier, implement more accurate interventions, and build a more sustainable future. In the fight against climate change, maps become part of the solution.

Artificial Intelligence-Assisted Spatial Analysis

Artificial Intelligence-Enabled Spatial Analysis: Analyzing large amounts of spatial data has become nearly impossible using traditional methods. Fortunately, artificial intelligence (AI) analyzes this data and transforms it into meaningful insights for strategic decisions. The role of AI in climate-focused decision support systems is not limited to analysis; it is also used to predict the future, mitigate risks, and manage resources effectively.

Object Recognition and Pattern Recognition

One of the most striking uses of artificial intelligence is uncovering hidden patterns in satellite imagery. Deep learning models (like CNN) can automatically detect forest roads, infrastructure, or waterways. For example, models developed in the Amazon forest can predict where deforestation will intensify by identifying new roads. 95% of deforestation occurs near roads.

Time Series and Change Analysis

Satellite data over time is used to monitor declines in vegetation, water volume, or fluctuations in humidity. These techniques make it possible to identify not only the current situation but also future risk areas.

Machine Learning and Predictive Models

Algorithms like XGBoost or Random Forest analyze climate and geographic factors to perform risk classification. Explanatory AI models that incorporate expert knowledge can simultaneously predict multiple hazards, such as floods, droughts, or heat waves. These models can be integrated into early warning systems and provide input for strategic planning.

Scenario Simulations and Optimization

AI also simulates alternative scenarios to analyze which intervention strategies would be most effective in which situations. It enables the most efficient allocation of resources in many areas, from agricultural irrigation and forest protection to water management and energy planning.

Decision Support with GIS Integration

AI analyses are visualized on GIS-based dashboards and presented to decision-makers. Features like zoom, filtering, and querying make it possible to examine risk maps interactively. For example, probability-based maps generated with XGBoost enable more effective risk management by identifying areas most likely to be affected by disasters.

This deep analytical capacity offered by AI has become our most powerful engineering tool against the climate crisis. It simplifies complex climate scenarios, makes risks predictable, and saves decision-makers time.

The Importance of Maps in Climate Risks and Disaster Management

Map-supported decision systems map climate risks and provide critical pre-intervention awareness. The following three examples illustrate how these technologies are translating into life-saving insights on the ground:

Drought Management

Data such as soil moisture, precipitation patterns, and vegetation indicators (NDVI, fAPAR) are integrated with artificial intelligence to create future scenarios. For example, indicators such as SPI (Standard Precipitation Index) predict how long a region can withstand drought. These systems shape irrigation planning and predict areas requiring urgent water management. Today, many agricultural regions are preparing for the season with this data.

Flood Management

Combining elevation models (DEMs), stream flow curves, and rainfall forecasts reveals how, when, and where flooding is likely to occur. LiDAR-assisted analysis takes into account elevation differences of even a few centimeters. For example, one energy company modeled its coastal facilities based on possible climate scenarios out to 2050 and repositioned their most vulnerable locations. Such systems can send street-by-street warnings and protect critical infrastructure.

Deforestation Monitoring

Algorithms that can predict the start of road clearing with 95% accuracy identify forest loss before trees are even cleared. Satellite imagery and AI models track pressures ranging from mining to illegal agriculture, identifying new risk areas. Monitoring the process offers the opportunity to inform data-driven conservation strategies.

All these applications demonstrate the power of systems that integrate diverse data sources and operate with engineering intelligence. Crises like drought, floods, and deforestation can be understood not only through their consequences but also through their causes. Maps are becoming a key tool for scientific decision-making.

Looking to the Future: Systems Development and Applications

In the near future, map-based decision systems will become merely guides and predictors. Global satellite networks like ESA Copernicus and NASA Earthdata make data instantly accessible. Cloud computing and big data infrastructures can process high-resolution climate models in real time. More importantly, artificial intelligence also explains the process—clearly identifying which variables pose which risks. This transforms complex data into an understandable map for decision-makers.

Digital twin cities and basins create testing grounds for risks by experiencing disaster scenarios in a simulated environment. Citizens, equipped with mobile devices and IoT sensors, become active participants in this system. Observations reach the center with a single click, and collective data intelligence is continuously fed. Such a structure provides early warning, guidance, and preparation.

All these innovations will form the backbone of disaster management and climate adaptation policies. GIS and remote sensing techniques have the power to pinpoint local climate impacts. These AI-powered systems offer governments, municipalities, and individuals a unique advantage in predicting risks and taking preventative measures. These smart platforms, powered by geographic data, are essential for a sustainable future. This structure, shaped by engineering discipline, will be the key to building the safe cities of tomorrow.