Digital Regulation Platform

Preparedness phase: hazard monitoring


Monitoring environmental conditions using specialized equipment has long been a necessary part of preparedness. Equipment has been falling in cost and rising in capability. There are now many cheap and portable sensors and actuators available in Internet of Things (IoT) devices that can be powered using solar panels or long-life batteries and that can communicate over long-range wireless networks. They are well suited to risky and remote locations. Even if they do not individually provide information of the same quality and quantity as more expensive equipment, they can compensate by being installed in bulk and communicating their information to systems for “big data” processing. The following are examples of trials and deployments for hazard monitoring.

Landslide site monitoring

Near Gangtok, India, there are now 200 sensors on about 0.6 sq. km of land. They measure and report to the data centre temperature, pore pressure, and seismic activity to issue landslide warnings 24 hours in advance. Rainfall measurements are used to provide long-term projections over several days, but as more measurements are received, they are used to provide a short-term projection in a real-time adaptation of the model (Ramesh 2018). To cover the areas of India prone to landslides might require 500 such systems.

Volcano monitoring

In Nicaragua, 80 sensors were installed around Masaya to measure temperature, humidity, atmospheric pressure and gas concentration. The measurements were transmitted across a long-range (LoRa) network to servers for analysis in a cloud. Along with information from 20 years of field studies, they will be used to provide early warnings and to build a digital twin that can simulate digitally what is happening inside the crater (Libelium 2017).

Glacial lake monitoring

In 2010, the first Unmanned Surface Vehicle (USV) was equipped with an ultrasound sensor to check the depth and likelihood of outbursts in Lake Imja, Nepal (Patterson and others 2012). In 2015, another USV was equipped similarly and found the depth to be much greater than it had been in previous surveys. In 2016, the depth and drainable volume of Lake Imja were reduced by 3.4 m and 3.6 million m3 respectively (2.2% and 10% respectively, relative to what they had been), and early warning systems were installed downstream.

COVID-19 infection monitoring

Crowdsourcing is a feature of symptom tracking, where users report symptoms on a smartphone on a periodic basis, typically daily. If there are enough reports, analysts can identify new outbreaks, alert health services, and make informed predictions of future caseloads. In scenarios when information about a disease is scarce or there is no available test, symptom tracking can also allow analysts to determine the predominant symptoms so people can recognize the infection readily and take suitable steps. For instance, a symptom tracking app used by 2.6 million people demonstrated that for its population (biased by self-selection and smartphone use), loss of the senses of taste and smell (as well as fatigue, persistent cough and loss of appetite) pointed to infection with COVID-19 Menni and others 2020).


Libelium. 2017. Predicting Eruptions in the Masaya Volcano with Wireless Sensors. Libelium. January 25, 2017.

Menni, C., A.M. Valdes, M.B. Freidin, C.H. Sudre, L.H. Nguyen, D.A. Drew, S. Ganesh, T. Varsavsky, M.J. Cardoso, J.S. El-Sayed Moustafa, A. Visconti, P. Hysi, R.C.E. Bowyer, M. Mangino, M. Falchi, J. Wolf, S. Sebastien Ourselin, A.T. Chan, C.J. Steves, and T.D. Spector. 2020. “Real-Time Tracking of Self-Reported Symptoms to Predict Potential COVID-19.” Nature Medicine, May11, 2020.

Patterson, M., R. Marston, S. Christopher, A. Jacobs, A. McDonald, J. Nicinska, and R. Chadwick. 2012. “Control of Tactical-Scale, Micro-Unmanned Surface Vehicles (USVs). 2012 Oceans.

Ramesh, S. 2018. “Landslide-Prone Sikkim Gets its Own Early Warning System.” The Print, September 25, 2018.

Last updated on: 09.10.2020