Landslide Identification Using Optimized Deep Learning Framework Through Data Routing in IoT Application

Lijesh L. and G. Arockia Selva Saroja ()
Additional contact information
Lijesh L.: Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari District Tamil Nadu, India
G. Arockia Selva Saroja: Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari District Tamil Nadu, India

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 06, 1961-1989

Abstract: This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) — used for landslide identification — is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.

Keywords: Landslide detection; route maintenance; internet of things; routing; deep residual neural network (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S021962202250095X
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:22:y:2023:i:06:n:s021962202250095x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S021962202250095X

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().