CRISIS AND DISASTER MANAGEMENT
Preventing mega destruction is the primary interest when technology is deployed in crisis and disaster field. There is a vast improvement in the last few years on how technology can be used to cut the destruction cost to reasonable amounts. Artificial intelligence deployment in the field focuses on giving correct predictions to any emerging or incoming disaster. The existing data on the different situations can be fed into analysis models to provide accurate simulation models of an absolute catastrophe extend.
Top government agencies are working on the best programs to implement to deal with the rising cost of destruction each time a disaster or a crisis strikes. The paper focuses on determining the various artificial intelligence technologies which can be adopted to improve on disaster management department operations in the future (T & S 2019). The paper investigates the statistical data provided on disasters that have happened for the last few years and assess the potential benefits of employing AI technology in the field. Don't use plagiarised sources.Get your custom essay just from $11/page
The general cost of natural related disasters and crises is generally high. These disasters create havoc internationally. Every year, huge damages are experienced, which are even impossible to recover. The scale of loss resulting from the various crisis is beyond appreciation. By investigating the table below, it is clear that these disasters can hardly be prevented. However, preparing for them is the best approach.
Image source: NOAA
Artificial Intelligence Technology Recommendations in Crisis and Disaster Management
Emerging technologies such as artificial intelligence are playing a significant role in the crisis and disaster management field. AI influence does not only reduce the damage extend but also allows the relevant disaster management agencies to take the right mitigation strategies in case a disaster has been predicted. Several technologies should be adopted in various sectors within the department to improve response time in case a disaster strikes ((T & S 2019). These technologies focus on data analysis from different geographical locations which are susceptible to disaster.
Internet of Things can be useful in the Scenarios department since it allows the collection of real data and information from the surrounding. Real-time data can then be translated into either disaster-related or non-harmful cases. If incorporated in the emergency department, the collected data from the surrounding can be then sent to the respective department for quick actions. Natural disasters have been the primary cause of delays in response to the affected areas. IoT can solve these related issues by sending the right information to the response team for quick actions such as updating the individuals and other operations.
In exercise operations such as tabletop or field operations, the OneConcern platform can be deployed. The platform uses artificial intelligence to calculate the possible disaster estimates from the analytical disaster assessment. A model of the geographical location can be created within the simulated software through the available data such as population hence monitoring the possible extent of the damage. The information gained can then be used to lay proper plans on resources needed to carry out rescue operations in case the disaster happens.
Other technologies, such as speech to text recognition software, can be incorporated within sectors such as operations, risk management, and intelligent fields. Speech-to-Text analytic software can assist in monitoring a real-time conversation between individuals. The software includes several natural languages which are then processed to give the exact location of the disaster through the existing models such as Cell Site Analysis. Currently, the 911 act as partnered with Speech to text analytics engineers to give an immediate response to the various location found to be suspicious of incoming disasters. These agencies believe that natural languages can be processed and analyzed to provide current or possible predictable events.
References
Advancing Mitigation Technologies and Disaster Response for Lifeline Systems. (2013). doi: 10.1061/9780784406878
T, K., & S, S. (2019). Smart Technologies for Emergency Response and Disaster Management. Emergency and Disaster Management, 939-979. doi:10.4018/978-1-5225-6195-8.ch044