Identification of Flash floods using Soil Flux and CO2: An Implementation of Neural Network with Less False Alarm Rate Article Swipe
Talha Ahmed Khan
,
Kushsairy Kadir
,
Muhammad Mansoor Alam
,
Zeeshan Shahid
,
Mazliham Mohd Su’ud
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.30880/ijie.2018.10.07.014
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.30880/ijie.2018.10.07.014
Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.30880/ijie.2018.10.07.014
- OA Status
- diamond
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2902163062
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2902163062Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.30880/ijie.2018.10.07.014Digital Object Identifier
- Title
-
Identification of Flash floods using Soil Flux and CO2: An Implementation of Neural Network with Less False Alarm RateWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-11-01Full publication date if available
- Authors
-
Talha Ahmed Khan, Kushsairy Kadir, Muhammad Mansoor Alam, Zeeshan Shahid, Mazliham Mohd Su’udList of authors in order
- Landing page
-
https://doi.org/10.30880/ijie.2018.10.07.014Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.30880/ijie.2018.10.07.014Direct OA link when available
- Concepts
-
Flash flood, Environmental science, Wind speed, Flux (metallurgy), Flash (photography), Typhoon, Meteorology, Remote sensing, Soil science, Geology, Materials science, Flood myth, Theology, Art, Physics, Visual arts, Philosophy, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1, 2019: 2, 2016: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.30880/ijie.2018.10.07.014 |
| publication_date | 2018-11-01 |
| publication_year | 2018 |
| referenced_works_count | 0 |
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