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November 9, 2020  |  By dynaquest_admin In Impact Technology, Smart City, Smart Regions

Technology for Disaster Preparedness and Risk Management

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Last week, many provinces have been affected by the super typhoon ‘Rolly’. Regions were flooded with some towns being submerged by over two metres in depth. Houses and their roofings were torn apart due to the harsh winds of the typhoon. This left many homeless and with the damage done to agricultural areas and commercial structures, livelihoods are greatly affected and this is happening at a time where the pandemic is still not subdued.

No technology can curb the devastation from storms or hurricanes but technology can greatly minimize the casualties – to near zero and boost the disaster readiness of provinces in such cases.

 

Information Sourced Nationally and Locally

The news is a vital information source for the people in order to be prepared and evacuate early. In the current structure of information management in disaster preparedness, findings from the weather and atmospheric bureau are then disseminated to provinces and regions. The same set of information are also broadcasted as news. But what if we enrich the process and increase the capabilities of provinces, regions and local townships as first-hand information sources in measuring the possible strength and impact of occurrences such as storms?

Sourcing local has its fair advantages: one is that we are gathering first-hand data from the immediate locations where collated data can be far more accurate compared to estimations coming from external sources. Second is that these regions, being responsible for its immediate constituents in the form of municipalities, townships and barangay, are able to mobilize grassroots information faster in ordering actionable mandates such as evacuations.

By improving the information and data gathering capabilities of provinces, the nation can further strengthen its national information gathering and collection. This gives provinces the self-sufficiency to take command at events where time is short and the need to do informed decisions are a matter of safety.

 

Predictive and Early Warning Systems

Sensors will play a more prominent role in the upcoming fourth industrial revolution as these sensors, will be the main actuators in starting a chain of automated processes. The same philosophy and augmentation can be applied to disaster preparedness. Sensors such as on weather balloons and buoys out in the sea can actively gather data in real time. Local provinces can use these data to make decisions that are guided by insights to lessen the calamitous effects of hurricanes on lives and livelihood.

By equipping more provinces with cutting-edge environmental sensor devises, early warning systems are made possible in safe guarding against storms, tsunamis and storm surges.

 

Analytics and Measurement

One of the most heart-breaking things about post-disaster are the toll it yields: casualties, damages and the amount of livelihood are lost as economic outputs are disrupted greatly. In order for these toll to be not lost in vain, purposeful statistics and the form of advance analytics and quantitative data must be thoroughly harnessed, cultivated and be used to account as historical occurences. In the future when we are confronted with a storm that resembles the strength and tenacity of the storm, we can draw upon historical data to project scenarios of worst cases. This way, we are able to devise counter-measures to lessen those tolls in our disaster preparedness.

Historical data are often visualized over a geographic map to give us a projection of the magnitude of the potential damage in a geographical span. For local authorities, these technology and imprint are immense in giving them an overview of the extent of previous disasters, so they will be better prepared from these historical occurrence.

 

Aid and Livelihood Impact

Food banks, medical supply chains and humanitarian logistics take action after the wake of the disaster. But what if through analytical insights from geographic visualization, we are able to strategically stock up supplies to the areas that are historically – disaster prone and the most affected by calamities such as stroms? These calculations must also take in consideration the numbers associated with the past humanitarian logistics: medicine dispensed, total weight of food dispensed, the shortages and what were in abundance from the last time.

Shortages in supplies are common in times of disaster, but with preparedness basing on historical data, we are able to artificially put a bridge on those supply gaps.

analytics disaster preparedness drone impact technology risk management rolly ph
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