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Expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices

Авторы
Кширсагар П.Р.Факультет искусственного интеллекта Инженерного колледжа имени Г.Х. Райсони, г. Нагпур, Индия
Манохаран Х.Факультет электроники и техники связи Технологического института «Панималар», адм. округ Пунамалли, г. Ченнаи, Индия
Касим С.Кафедра электротехники и вычислительной техники инженерного факультета Университета Короля Абдулазиза, г. Джидда, Саудовская Аравия
Кхан А.И.Кафедра компьютерных наук факультета вычислительной техники и информационных технологий Университета короля Абдулазиза, г. Джидда, Саудовская Аравия
Алам М.М.Кафедра электротехники и вычислительной техники инженерного факультета Университета Короля Абдулазиза, г. Джидда, Саудовская Аравия
Абушарк Ю.Б.Кафедра компьютерных наук факультета вычислительной техники и информационных технологий Университета короля Абдулазиза, г. Джидда, Саудовская Аравия
Абера В.Факультет пищевой инженерии Инженерно-технологического колледжа Университета Уольките, г. Уольките, Эфиопия

Abstract: We present to the readers a slightly abridged and adapted translation of the article “Expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices” by Indian and Saudi Arabian researchers. This work was published in the peer-reviewed journal “Computational Intelligence and Neuroscience” by the Hindawi Publishing Corporation under the CC BY 4.0 license. This license permits copying and distributing the article in any medium and format, adapting, modifying, and creating new works based on it for any purpose, including commercial use, provided that the original source is referenced. In our  case, the full reference to the original source is provided at the end of the translation.

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 (WSN) 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 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, 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 WSN and 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.

Keywords: landslides; landslide hazard; operational landslide monitoring; landslide prediction; wireless sensors; wireless sensor network; artificial intelligence; machine learning; logistic regression; support vector machine; stochastic gradient descent

DOI: 10.58339/2949-0677-2025-7-3-54-67

UDC: 624.131.543; 624.131.3; 004

For citation: Kshirsagar P.R., Manoharan H., Kasim S., Khan A.I., Alam M.M., Abushark Y.B., Abera W. Operativnaya kolichestvennaya otsenka pokazatelei opolznevoi opasnosti s ispol'zovaniem besprovodnykh datchikov i upravleniya dannymi na osnove iskusstvennogo intellekta (sokr. per. s angl.) [Expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices (abridged translation from English into Russian)] // Geoinfo. 2025. T. 7. № 3. S. 54–67. DOI:10.58339/2949-0677-2025-7-3-54-67

Funding: The project under which this work was carried out was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant D-994-135-1443. Accordingly, the authors express their gratitude to the DSR for its technical and financial support.

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Article in RSCI: https://www.elibrary.ru/item.asp?id=85275396