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Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices
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....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152392/ https://www.ncbi.nlm.nih.gov/pubmed/35655498 http://dx.doi.org/10.1155/2022/3211512 |
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author | Kshirsagar, Pravin R. Manoharan, Hariprasath Kasim, Samir Khan, Asif Irshad Alam, Md Mottahir Abushark, Yoosef B. Abera, Worku |
author_facet | Kshirsagar, Pravin R. Manoharan, Hariprasath Kasim, Samir Khan, Asif Irshad Alam, Md Mottahir Abushark, Yoosef B. Abera, Worku |
author_sort | Kshirsagar, Pravin R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9152392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91523922022-06-01 Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices Kshirsagar, Pravin R. Manoharan, Hariprasath Kasim, Samir Khan, Asif Irshad Alam, Md Mottahir Abushark, Yoosef B. Abera, Worku Comput Intell Neurosci Research Article 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. Hindawi 2022-05-23 /pmc/articles/PMC9152392/ /pubmed/35655498 http://dx.doi.org/10.1155/2022/3211512 Text en Copyright © 2022 Pravin R. Kshirsagar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kshirsagar, Pravin R. Manoharan, Hariprasath Kasim, Samir Khan, Asif Irshad Alam, Md Mottahir Abushark, Yoosef B. Abera, Worku Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title | Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title_full | Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title_fullStr | Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title_full_unstemmed | Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title_short | Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices |
title_sort | expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152392/ https://www.ncbi.nlm.nih.gov/pubmed/35655498 http://dx.doi.org/10.1155/2022/3211512 |
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