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Related Concept Videos

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Manipulation and Analysis01:21

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Updated: May 30, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Farman Ali2

  • 1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.

Journal of Environmental Management
|January 28, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances flood susceptibility mapping (FSM) by combining Decision Tree (DT) with metaheuristic algorithms. The DT-Chaos Game Optimization (DT-CGO) model significantly improved accuracy, offering better flood risk assessment and mitigation planning.

Keywords:
Flood-prone areasGeospatial artificial intelligence (GeoAI)Machine learningMetahurestic algorithmsSatellite imagery

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Machine Learning Applications

Background:

  • Flooding poses significant risks to human safety and infrastructure, necessitating accurate flood-prone area mapping for effective mitigation.
  • Current machine learning models for flood susceptibility mapping (FSM) face limitations including data dependency, interpretability issues, and overfitting.

Purpose of the Study:

  • To improve the accuracy and reliability of flood susceptibility mapping (FSM) by integrating the Decision Tree (DT) algorithm with advanced metaheuristic optimization techniques.
  • To evaluate the performance of DT combined with Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), and Arithmetic Optimization Algorithm (AOA) for FSM.

Main Methods:

  • Developed four models: basic Decision Tree (DT), DT-Arithmetic Optimization Algorithm (DT-AOA), DT-Gradient-Based Optimizer (DT-GBO), and DT-Chaos Game Optimization (DT-CGO).
  • Evaluated models using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Area Under the Curve (AUC).

Main Results:

  • The DT-CGO model demonstrated superior performance, achieving the lowest RMSE (0.17) and MAE (0.06), and the highest R² (0.871) and AUC (0.978) on the training set.
  • All enhanced models (DT-AOA, DT-GBO, DT-CGO) outperformed the basic DT model on the test set, with DT-CGO showing the highest efficacy.
  • The optimized models exhibited strong predictive power, validating their effectiveness in flood susceptibility mapping.

Conclusions:

  • Combining the Decision Tree algorithm with metaheuristic optimization algorithms significantly enhances flood susceptibility mapping accuracy.
  • The DT-CGO model offers a robust and accurate approach for identifying flood-prone areas, providing crucial data for policymakers.
  • Accurate FSM is vital for developing effective flood mitigation strategies and reducing disaster impacts.