Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing Article Swipe
Talha Ahmed Khan
,
Muhammad Mansoor Alam
,
Zeeshan Shahid
,
Mazliham Mohd Su’ud
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3038812
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3038812
Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.
Related Topics
Concepts
Particle swarm optimization
Computer science
Constant false alarm rate
Flash flood
Artificial neural network
Real-time computing
Wireless sensor network
Warning system
Remote sensing
Artificial intelligence
Data mining
Flood myth
Machine learning
Telecommunications
Philosophy
Geology
Computer network
Theology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3038812
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09261487.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3104553723
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3104553723Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.3038812Digital Object Identifier
- Title
-
Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution SensingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Talha Ahmed Khan, Muhammad Mansoor Alam, Zeeshan Shahid, Mazliham Mohd Su’udList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3038812Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09261487.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09261487.pdfDirect OA link when available
- Concepts
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Particle swarm optimization, Computer science, Constant false alarm rate, Flash flood, Artificial neural network, Real-time computing, Wireless sensor network, Warning system, Remote sensing, Artificial intelligence, Data mining, Flood myth, Machine learning, Telecommunications, Philosophy, Geology, Computer network, TheologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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