Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices Article Swipe
Pravin R. Kshirsagar
,
Hariprasath Manoharan
,
Samir Kasim
,
Asif Irshad Khan
,
Md. Mottahir Alam
,
Yoosef B. Abushark
,
Worku Abera
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/3211512
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/3211512
The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect. This paper illustrates the effectiveness of Wireless Sensor Networks (WSN) and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The Wireless Sensors Network (WSN) and Artificial Intelligence (AI) give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/3211512
- https://downloads.hindawi.com/journals/cin/2022/3211512.pdf
- OA Status
- hybrid
- Cited By
- 12
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281396317
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281396317Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/3211512Digital Object Identifier
- Title
-
Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling PracticesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-05-23Full publication date if available
- Authors
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Pravin R. Kshirsagar, Hariprasath Manoharan, Samir Kasim, Asif Irshad Khan, Md. Mottahir Alam, Yoosef B. Abushark, Worku AberaList of authors in order
- Landing page
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https://doi.org/10.1155/2022/3211512Publisher landing page
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https://downloads.hindawi.com/journals/cin/2022/3211512.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cin/2022/3211512.pdfDirect OA link when available
- Concepts
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Landslide, Wireless sensor network, Computer science, Real-time computing, Context (archaeology), Wireless, Wireless network, GRASP, Event (particle physics), Computer network, Telecommunications, Engineering, Geology, Quantum mechanics, Programming language, Paleontology, Geotechnical engineering, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 7, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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