Emergency Planning and Response
How can we better plan and respond to emergencies?
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Are the smart decision-making tools that Businesses and Government use are Smart Enough?
Outliers is a team of Smarts from the Smarts. We provide insights to various industries all over the globe and facilitate Data Driven Weather Intelligent decision making. We achieve this by using Inter-Disciplinary perspective i.e. combining two or more very different concepts and creating an innovative idea, merging and manipulating unstructured data sources from various departments and including a component of weather patterns in our decision making.
Here are few examples where we offer help to various industries:
1. Disaster Management: Weather intelligent Disaster Management Engine (WID-ME) enables disaster management team to predict and monitor real time disasters. WID-ME also incorporates weather intelligent machine learning capabilities to predict the possible events and envision customised Strategic Plans to help Disaster Management Teams and the Local Citizens to evacuate to a safer place.
Advertisements: Weather intelligent Advertising Tools help businesses target ‘Right Audience’ at the Right Time, Right Place and with Right Technology or Medium. This helps advertisers to optimize the cost on advertisements and improves ROI (return on investment).
Health-Care: Weather intelligent Alert System helps the businesses in healthcare industry as well as local population. Using machine learning tools, previous records of illness and mapping that to weather conditions, the system predicts the possible outbreak of a disease and generate alerts. This helps businesses to plan up with their manufacturing and supply of various drugs. This also helps users to contact specific and specialised doctors when needed.
Similarly, we work with other industries like Sports, Transportation, Adventure, Agriculture, Food, Construction, Power and many more to help them with various Data Driven Weather Intelligent solutions. Also, the maintenance of public services can be facilitated by the weather driven intelligence.
WID-ME: A Complete Support System for Disaster Management
WID-ME is one of the most advanced, innovative and simple tools that eases and advances the Disaster Management in Australia. With advance technology WID-ME ensures precise and accurate disaster prediction, anticipation of the situation using International weather data and builds an automated situation specific action plan.
1. The weather data is globally available for us to use.
2. We have real time weather data available.
3. We have the details for alerting citizens.
WIDME uses the real-time weather dataset and the dataset from various departments like BOM, to monitor and detect the disasters like fire outbreaks. Using the demographics and resources like hospitals, schools and reservoirs, WIDME provides personalized strategic plans to the citizens as well as the CFS. The citizens get alert to relocate to nearby safe places. The CFS also receives plans about the resources available and their locations to assist the citizens in their evacuation plan. Using real time monitoring, WIDME detects the possible area under threat, so that the CFS can focus on that particular area and supress the fire as early as possible.
This support system integrates data sources like:
1. Weather condition and Weather Predictions using International weather data
2. Past Disaster History
3. Location, Population and People Demographics
4. Resource Location [Hospitals, Schools, Dams or other water bodies, Fire Brigade etc.]
5. Government marked Safe and Dangerous location data
6. Alert and Contact details
7. Emergency Contacts List
Refer to individual datasets submitted for more details.
Evidence of Work
Description of Use Could be an attribution to the commonwealth of people.
Description of Use Digital Elevation Model (DEM) of Australia could help us track possible flood with ground water data.
Description of Use The residential price index could be affected by the possible emergency index.
Description of Use Help determine the logical dam break area.
Description of Use We could use the average household size data to give information about the disaster which has influences on the supply and demand.
Description of Use The vegetation land coverage could be used to monitor the higher risk of bushfire in a drier weather.
Description of Use We utilize the temperature datasets from the BOM and combines other datasets related to the disasters pattern and other indicators to support agencies to better make risk management with combined information. (Eg. Higher temperature + low humidity + high vegetation coverage = high risk of bushfire)
Description of Use The dataset is used to combine with the bushfire data, road data and climate data for agencies to lower the cost of human life when a disaster happens.
Description of Use We utilize the datasets from the Department of Environment and Waters and combines other datasets related to the disasters pattern, the public assets, the roads, and other indicators to support agencies to better make risk management and resource distribution with combined information.
Description of Use We combined ocean data with temperature data, rainfall data, climate data and etc. to provide insights to monitor ocean condition and the commonwealth of the citizens who live near the ocean or people whose living depends on the ocean condition.
Description of Use The road data is combined with climate and disaster datasets to help agencies evacuate or manage resources efficiently under severe condition.
Description of Use We utilize the conditions of major reservoirs data in California to provide risk management information for major agencies to help them come up with a better strategic plan for water distribution when a bushfire occurs (eg. bushfire could cause air pollution and water pollution).
Description of Use Knowing the location and extent of wildfire events that affect California may help resource managers predict and manage potential impacts burns may have on California's water quality (combined with the dam data), availability, movement and citizens' wealth.
Description of Use We utilize Metropolitan Fire Brigade location data combined with other datasets, such as bushfire data, solar data, humidity data and etc. to provide applicable information to multiple agencies and provide information such as the distance of the incident and the closest fire brigade location.
Description of Use We use assets at risks data combining with the bushfire data, the fire brigade location and water resource data from different councils to provide valuable information on bushfire risk management to the agencies in charge.
Description of Use We utilize bushfire safer places data to provide information combined with the bushfire data, the water dam data, the population data and etc. to support agencies to make appropriate decisions when incidents like bushfire happen.
Description of Use The purpose of using the data is to monitor the standing water level and track the humidity of the soil. The combination of the groundwater data could not only use for disaster and damage monitoring, it could be used in our strategic plan to help the government agencies to come up with better construction plan(when, where and how), recovery plan, water allocation and etc. in various hazard conditions.
Description of Use We use daily rainfall data to monitor the humidity, possible chance of flood, dam failures, and other disasters. Also, to provide a maintenance schedule for public facilities(eg. rusty facilities, equipment failures) to avoid public dangers by combining the data from different departments and collaborating together.
Description of Use We use the Population Density dataset of Australia to see which places are more populated that should be taken care of carefully. And also to come up with an evacuation plan (merged with streets and roads map)to manage people's mobility when disasters happen.
Description of Use We use the Near-Surface Wind Speed data to monitor the disaster. Such as if a bushfire incident is happening, we use the data to track the possible spreading speed of the bushfire.
Description of Use We use the Angas Catchment Dam's dataset to monitor the water volume in the dam. We could use it for water resource management and emergency planning and response when a huge fire disaster happens or when there is a need to adjust the water volume.
Description of Use We use daily solar exposure data in the SA area to monitor the possibility of bushfire happening and related disasters. Eg. Under how long of daily exposure put people at the risk of bushfires?
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