An IOT Based Landslide Detection and Early Warning System in Hilly Areas: A Case Study of Bududa District, Eastern Uganda

  • Lusiba Badru Busitema University
  • Semwogerere Twaibu, PhD Busitema University
  • Oguti Victoria Busitema University
  • Gilbert Gilibrays Ocen Busitema University
Keywords: Detection, Alert System, Disaster, GSM, IoT, Landslide
Share Article:

Abstract

Landslides are the gravitational movements of soil, rock down slopes that can cause several damages to the environment. It is one of the most common occurring natural phenomena worldwide in causing great loss of lives and property. The study aimed at developing a Web-Based Landslide Detection and Alert System to monitor and alert people in landslide-prone areas in time. Quantitative experimental designs were employed targeting parameters like soil pore pressure, soil vibration, soil movement, rain intensity and humidity. These are sensor-monitored and their values are analysed for the system development. These values are transmitted to the monitoring section via the internet and people get notified by a text “landslide detection alert” by using GSM. Findings are expected to contribute towards disaster preparedness and eventual loss of lives and property. It is therefore recommended for use in such prone areas by the ministry and other organs of concern

Downloads

Download data is not yet available.

References

Dinagar, A., Karthick, P., Karthi, K., Tamilvanan, P., & Premkumar, S. (2015). Landslide Monitoring System with GSM Module. International Journal of Innovative Research in Computer and Communication Engineering, 3(Special Issue 2).

Ramesh, M. V. (2009, June). Real-time wireless sensor network for landslide detection. In 2009 Third International Conference on Sensor Technologies and Applications (pp. 405-409). IEEE.

Tien Bui, D., Pham, B. T., Nguyen, Q. P., & Hoang, N. D. (2016). Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam. International Journal of Digital Earth, 9(11), 1077-1097.

Georgieva, K., Smarsly, K., König, M., & Law, K. H. (2012). An autonomous landslide monitoring system based on wireless sensor networks. In Computing in Civil Engineering (2012) (pp. 145-152).

Ahrens, J., & Rudolph, P. M. (2006). The importance of governance in risk reduction and disaster management. Journal of contingencies and crisis management, 14(4), 207-220.

Musinguzi, M., & Asiimwe, I. (2014). Application of geospatial tools for landslide hazard assessment for Uganda. South African Journal of Geomatics, 3(3), 302-314.

Krol, O., & Bernard, T. (2012). ELDEWAS-Online early warning system for landslide detection by means of dynamic weather nowcasts and knowledge-based assessment.

Tanaka, T. (2013). Landslide monitoring system. International Journal of Landslide and Environment, 1(1), 101-102.

Hinge, P. N., Hinge, P. N., & Bawage, R. R. (2014). Wireless Sensor Network for Detecting Vibrations Before Landslides. IJERT, 2278-0181.

Selby, M. J. (1993). Hillslope Materials and Processes Oxford Univ. Press.

Badru, L., Victoria, O., Patrick. A. Z., Felix. B. (2019). Water Parameters/Characteristics of the Rivers/Channels in the Landslide Prone Areas of Bududa Uganda.

Rastetter, E. B., Kwiatkowski, B. L., Le Dizès, S., & Hobbie, J. E. (2004). The role of down-slope water and nutrient fluxes in the response of Arctic hill slopes to climate change. Biogeochemistry, 69(1), 37-62.

“Rain Sensor Module”. Accessed on: Jan. 31st, 2020. [Online]. Available: https://www.openhacks.com/uploadsproductos/rain_sensor_module.pdf.

“Vibration Sensor Module,” Nov. 14, 2018. Accessed on: Jan. 31st, 2020. [Online].

Available: https://osoyoo.com/2018/11/14/arduino-lesson-vibration-sensor-module

“Soil Moisture Sensor”. Accessed on: Jan. 31st, 2020. [Online]. Available: https://create.arduino.cc/projecthub/MisterBotBreak/how-to-use-a-soil-moisture-sensor-ce769b.

“ESP8266-01”. Accessed on: Jan. 31st, 2020. [Online]. Available: https://www.electronicwings.com/sensors-modules/esp8266-wifi-module

“GPS- module”. Accessed on: Jan. 31st, 2020. [Online]. Available: https://www.electroschematics.com/neo-6m-gps-module/.

Published
21 March, 2022
How to Cite
Badru, L., Twaibu, S., Victoria, O., & Ocen, G. (2022). An IOT Based Landslide Detection and Early Warning System in Hilly Areas: A Case Study of Bududa District, Eastern Uganda. East African Journal of Information Technology, 5(1), 31-38. https://doi.org/10.37284/eajit.5.1.588

Most read articles by the same author(s)