IJMTES – LANDSLIDE PREDICTION SYSTEM BASED ON WIRELESS PERSONAL AREA NETWORK AND IOT

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : LANDSLIDE PREDICTION SYSTEM BASED ON WIRELESS PERSONAL AREA NETWORK AND IOT

Author’s Name : S Srinath | M Vignesh | T Vijayan  unnamed

Volume 03 Issue 09 2016

ISSN no:  2348-3121

Page no: 138-140

Abstract – Landslide susceptibility mapping is indispensable for disaster management and planning development operations in mountainous regions. Around the globe, landslides and mudslides are serious geological hazards affecting people, and causing significant damages every year. Approximately 15% of total area of India is susceptible to landslides. These areas are marked as Landslide Hazard Zones. Landslides occur mainly due to heavy rainfall experienced by these zones during the monsoon season and sometimes as an aftermath of an earthquake. The existing methods uses satellite image sensing technology or a camera based image sensing. But these methods are not the cheapest one also. The solution this project is based upon the concept of low cost wireless sensor networks (WSN).  WSN method uses a sensor network consisting of sensor columns. Sensor columns are deployed on hills to find the early signals preceding a mudslide or landslide. This sensor network consists of a collection of sensor columns placed inside the vertical holes drilled during the network deployment phase and they are installed in a distributed manner over the monitored area. Each sensor column has two components: the sensing component that is buried underground and contains all the instruments, and the computing component that stays above ground and contains the processor and radio module.  

Keywords— Wireless Personal Area Network, Arduino, ESP8266 WiFi Module, Soil Moisture sensor, Vibrational Sensor, MEMS Accelerometer

Reference

  1. X. Yao, L. Tham, and F. Dai, “Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China,” Geomorphology, vol. 101, no. 4, pp. 572–582, Nov. 2008.
  2. L. Ayalew, H. Yamagishi, and N. Ugawa, “Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan,” Landslides,vol. 1, no. 1, pp. 73–81, Mar. 2004.
  3. F. Guzzetti, A. Carrara, M. Cardinali, and P. Reichenbach, “Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy,” Geomorphology, vol. 31, no. 1, pp. 181–216, Dec. 1999.
  4. S.Leeand , B.Pradhan,“Landslide hazard mapping at Selangor,Malaysia using frequency ratio and logistic regression models,”Landslides,vol. 4,no.1,pp.33–41,Mar.2007.
  5. M.N.Jebur, B.Pradhan, and M.S.Tehrany,“Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia,using L-bandIn SAR technique,”Geosci.J.,vol.18,no.1, pp.61–68,Mar.2014,doi:10.1007/s12303-013-0053-8.
  6. A.Brenning,“Spatialpredictionmodelsforlandslidehazards:Review,comparison and evaluation,” Nat. Hazards Earth Syst.Sci., vol. 5, no.6,pp.853–862,Feb.2005.
  7. P.Aleottiand R.Chowdhury,“Landslide hazard assessment: Summary review and new perspectives,” Bull. Eng. Geol. Environ., vol.58,no.1, pp.21–44,Aug.1999.