Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

102
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...
102
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

155
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...
155
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

124
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
124
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

11.0K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
11.0K
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

119
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
119
Levels of Use of a GIS01:29

Levels of Use of a GIS

103
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...
103

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explainable prediction of healthcare waste generation using hybrid PCA-GPR with SHAP for enhanced environmental protection.

Journal of environmental health science & engineering·2026
Same author

A standardized parametric weighting system for water quality indexing: advancing water resource management through improved decision support.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2026
Same author

A synthesis of human health and ecological risk assessment indicators for microplastics in Nigerian water systems.

International journal of environmental health research·2026
Same author

Regime-based interpretation of groundwater pollution using Gaussian mixture modelling and water pollution index in aquifer systems.

Scientific reports·2026
Same author

Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.

Sensors (Basel, Switzerland)·2026
Same author

Which Strategy When? Designing an Adaptive Search System for Virtual Reality.

IEEE transactions on visualization and computer graphics·2026

Related Experiment Video

Updated: Sep 13, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K

Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms.

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki2, Sani I Abba3

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

Scientific Reports
|July 31, 2025
PubMed
Summary

Optimizing machine learning models with swarm intelligence significantly improves flood susceptibility mapping accuracy. This novel approach enhances flood management strategies by providing more reliable predictions.

Keywords:
Boosting algorithmFlood managementFlood-prone area mappingSemi-arid climateSwarm-based metaheuristics

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates
05:57

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

3.7K

Related Experiment Videos

Last Updated: Sep 13, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates
05:57

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

3.7K

Area of Science:

  • Environmental Science
  • Geographic Information Science
  • Artificial Intelligence

Background:

  • Accurate flood susceptibility mapping (FSM) is crucial for effective flood risk management.
  • Existing FSM methods often lack optimal hyperparameter tuning for machine learning models, leading to reduced accuracy.
  • There is a need for advanced techniques to enhance FSM precision for better decision-making.

Purpose of the Study:

  • To introduce and evaluate a novel approach for improving FSM accuracy.
  • To optimize the CatBoost machine learning algorithm using swarm-based metaheuristic algorithms (Zebra Optimization Algorithm - ZOA, Whale Optimization Algorithm - WOA).
  • To enhance FSM in Shushtar County, Iran, by applying optimized CatBoost models.

Main Methods:

  • Utilized 13 flood-influencing parameters and flood occurrence points as input data for FSM.
  • Applied the CatBoost algorithm and optimized versions using ZOA (CatBoost-ZOA) and WOA (CatBoost-WOA).
  • Evaluated model performance based on accuracy metrics for flood susceptibility maps.

Main Results:

  • The standard CatBoost model achieved 84.2% accuracy.
  • The CatBoost-WOA model reached 85% accuracy.
  • The CatBoost-ZOA model demonstrated the highest accuracy at 87.2%, showing a 3.0% absolute improvement over the non-optimized CatBoost model.

Conclusions:

  • Integrating swarm-based optimization algorithms with machine learning significantly enhances FSM accuracy.
  • The optimized CatBoost models (CatBoost-ZOA and CatBoost-WOA) offer a more accurate and reliable approach to FSM.
  • This non-structural approach provides valuable insights for flood management and decision-making.