Using Ambient Intelligence ‘AMI’ to Manage Disaster Risk Reduction ‘DRR’

Rasha Waheeb

doi:10.20944/preprints202401.0066.v1

Submitted:

31 December 2023

Posted:

02 January 2024

You are already at the latest version

Abstract

The use of artificial intelligence technologies may help companies obtain the optimal use of resources and accomplish more activities with an increase in productivity by 40%. With access to real-time data and the ability to learn from patterns and behaviors, AI can perform these routine tasks more efficiently. Ambient intelligence, Ami, is one of the applications of artificial intelligence, which is called artificial environmental intelligence, which is a concept that focuses on using technology to create an environment that is intelligent and responsive to the needs of its users. This technology has the potential to revolutionize disaster response and management, which in turn facilitates access to necessary information and resources. necessary vitality in the event of a crisis. It may use ambient intelligence to create a network of sensors that can detect and respond to changes in the environment. So that this network can be used to monitor the environment for potential disasters, such as floods, fires and earthquakes. Sensors can also be used to detect changes in temperature, humidity, and other environmental factors that may indicate a potential disaster. This information can then be used to alert emergency responders and provide them with the information needed to respond quickly and effectively. Ambient intelligence can also be used to create a comprehensive database of resources and information that can be accessed in times of crisis. This database may include information about the location of shelters, medical facilities, and other resources that can be used to assist those affected by a disaster. It may also provide information about the condition of roads, bridges, and other infrastructure that may be affected by a disaster. This information can be used to inform response and management decisions, helping to ensure that resources are used in the most efficient way possible. Ambient intelligence can be used to create a communication platform between emergency responders and those affected by a disaster. This platform can also be used to provide updates on the emergency situation, in addition to providing information about available resources to those affected. This can help ensure that those affected by a disaster can access the resources they need in a timely manner.

Keywords:

Ambient Intelligence

;

AMI

;

Disaster Management

;

Risk Reduction

;

DRR

Subject:

Engineering - Other

Introduction

Ambient intelligence has the potential to revolutionize the way we respond to and manage disasters. By providing access to vital information and resources, it can help ensure that those affected by a disaster can access the assistance they need in a timely manner. As the world becomes increasingly dependent on technology, the potential for ambient intelligence to revolutionize disaster response and management is enormous.

Leverage Artificial Intelligence and Surrounding Intelligence for Proactive Disaster Response

As the world continues to grapple with the devastating effects of natural disasters, the need for a proactive disaster response has become even more urgent. To meet this challenge, the power of artificial intelligence (AI) and ambient intelligence (AmI) is being used by researchers to upgrade systems that can detect and respond to disasters in real time.

AI-powered systems are used to monitor environmental conditions and detect changes that may indicate the onset of a disaster. For example, AI-powered sensors can detect seismic activity, changes in water levels, and other indicators of potential disasters. By detecting these changes in advance, these systems can alert authorities and provide them with the information they need to respond quickly and effectively.

In addition to monitoring environmental conditions, AI and artificial intelligence are used to analyze data from social media and other sources to detect potential disasters. For example, algorithms based on artificial intelligence can detect changes in the frequency and content of tweets and other social media posts to identify potential disasters. By monitoring social media, AI-driven systems can provide authorities with the information they need to take proactive steps to protect people and property.

Ambient artificial intelligence has also been used to develop systems that can predict the likelihood of a disaster. By analyzing data from multiple sources, these systems can generate predictions about the likelihood of a disaster occurring in a given area. This information can be used to inform disaster response plans and help authorities better prepare for potential disasters.

By taking advantage of the power of artificial intelligence, systems are being developed that can detect and respond to disasters before they happen because God Almighty taught man what he did not know. This technology has the potential to save lives and property by providing authorities with the information they need to take proactive steps to protect people and property.

Understand the Role of Ambient Intelligence in Disaster risk Reduction

Ambient Intelligence is a rapidly growing field of technology that has the potential to revolutionize Disaster Risk Reduction (DRR). AMI systems sense, learn and respond to the environment in which they are placed. This technology has been used in a wide variety of applications, including home automation, healthcare, and security.

In the context of disaster risk reduction, AmI systems can be used to detect and monitor environmental conditions that may lead to disasters. For example, AmI systems can be used to detect changes in air quality, water levels, and seismic activity. This information can then be used to inform decision makers and provide early warning systems for potential disasters.

AmI systems can also be used to provide support during disaster response and recovery. For example, AmI systems can be used to track the location of personnel and resources, as well as monitor the status of critical infrastructure. This information can be used to improve resource allocation and ensure that the most effective response is taken.

In addition, AmI systems can be used to collect data from the environment and provide real-time analysis of the situation. This data can be used to inform decision-making and help identify potential risks and vulnerabilities.

Overall, AmI systems have the potential to revolutionize disaster risk reduction by providing real-time data and analytics to inform decision-making. By leveraging the power of this technology, disaster risk reduction practitioners can be better equipped to respond to disasters and reduce disaster risk in the future.

Recent technological developments have created new possibilities for improving disaster recovery. One of the most promising of these is the use of ambient intelligence, or artificial intelligence, to create a more responsive and effective response to disasters.

Environmental intelligence is a class of artificial intelligence that relies on the use of sensors and data analysis in order to detect and react to changes in the surroundings. It can be used to monitor a wide range of environmental conditions, from air quality to temperature, and to provide real-time feedback on the state of the environment. This data can then be used to create predictive models that can help anticipate and respond to disasters more quickly and effectively.

The potential of ambient intelligence for disaster recovery is great. By monitoring the environment, the AI can detect changes in the environment that could signal the onset of a disaster, such as an earthquake or hurricane. This data can then be used to alert authorities and aid organizations, allowing them to respond more quickly and effectively.

In addition, ambient intelligence can be used to create predictive models that can help anticipate and respond to disasters more quickly and effectively. By analyzing data from past disasters, AI can create models that can help predict the likelihood of future disasters, and the best ways to respond to them. This can help ensure that the appropriate resources are available to respond quickly and effectively.

Ambient intelligence can be used to create more efficient and effective response plans. By analyzing data from past disasters, AI can create models that can help determine the most effective disaster response strategies. This can help ensure that the right resources are available to respond quickly and effectively.

The potential of ambient intelligence for disaster recovery is clear. By using artificial intelligence to monitor the environment and create predictive models, authorities and aid organizations can respond quickly and effectively to disasters. This can help save lives and reduce the impact of disasters on communities.

Disasters caused by natural hazards are increasing in frequency and severity, reflecting the immediate reality of climate change and leading to an ever-growing succession of humanitarian crises.

New technologies can help detect and prepare for severe weather and other hazards, as well as communicate effectively to people and communities about the necessary response.

The potential of artificial intelligence (AI) may help enhance disaster mitigation around the world.

Recent decades have made advances in modeling natural hazards and disasters, leading to better tools for responding to extreme weather events. One frequently cited case is Cyclone Phailin in eastern India in 2013, when the guidance of an accurate digital model prevented a tragedy of the kind that followed a similar storm 15 years earlier.

And there is still much room for improvement, as according to the 2020 State of Climate Services report by WMO, one in three people in the world is not adequately covered by the early warning system.

Climate and weather disturbances may increase risks across the planet, including areas that were not affected much by extreme natural phenomena until recently.

AI can help response teams understand natural hazards, monitor events in real time, and anticipate specific risks in the face of imminent or ongoing disasters.

The more early warning we have, the more people will be prepared, and the less human tragedy will be. While satellites and other existing meteorological infrastructure provide valuable information for weather forecasting, artificial intelligence can take the process even further. Impact system modeling can indicate, for example, the potential consequences of natural hazards on populations and ecosystems.

To get value to people, we need to not only understand what the weather is going to do, but what the weather is going to do to people and the environment and that’s impact-based prediction, where AI can play a huge role.

Interoperable and Cross-Border Solutions

The impact of extreme natural events is also determined by social and economic resilience, with inequalities exacerbating risks and vulnerabilities. Experts stress the need to explore interoperable solutions that can work in different contexts and even across borders to reach areas with disaster infrastructure. least developed.

Several case studies show the value of AI during the different phases of disaster management: first, prediction and anticipation; Next, to help communicate what happened; and in monitoring and early detection of potential new risks.

A team from Vrije Universität Amsterdam, in the Netherlands, examined different statistical models, using data from coastal regions around the world, in an attempt to predict the risk of floods and storm surges through deep learning methods in the face of rising sea levels caused by climate change. Open data can provide valuable insights into the type of ecosystems that are increasingly at risk.

Artificial Intelligence and Natural Disasters, New Roles for the Machine

At a time when the roles of artificial intelligence are increasing, and natural disasters pose a frightening threat to humans. From earthquakes and hurricanes to forest fires and floods, the search for ways to confront disaster, or at least to predict it, becomes the only concern of humans at the present time and the near future.

AI experts who focus on managing disasters try to prevent or predict them. Technology and research are emerging that are trying to help governments better predict and respond to disasters such as floods, tsunamis, and earthquakes.

In recent years, technology and research have emerged that are trying to help governments better predict and respond to disasters such as floods, tsunamis, and earthquakes.

The researchers use deep learning algorithms to filter out city noise so that earthquake data can be better collected. Algorithms analyze seismic data according to previous earthquakes to predict disasters earlier and notify people more quickly.

Artificial intelligence researchers and geophysicists at Stanford University consider that artificial intelligence can be fast and give the right time to warn people, and here 10 seconds can save many lives.

In recent years, using deep learning and advanced artificial intelligence, we have had promising results in terms of predicting and predicting ground shaking, and technology can predict the intensity of ground shaking according to what seismic monitoring stations witnessed, and these observations can be used to predict the intensity of shaking within seconds.

The Role of Artificial Intelligence

Here comes the role of artificial intelligence (AI) as one of the most promising solutions. With the help of AI technologies, organizations and individuals can gain valuable insights into data surrounding weather and climate patterns, allowing them to take proactive steps to protect themselves and their property.

Artificial intelligence can also be used in many ways to help identify, monitor and predict natural disasters.

By leveraging AI-based analytics, organizations can better understand the potential risks of natural disasters and use this data to plan ahead and take proactive steps to mitigate potential damage.

Artificial Intelligence and Corporate Empowerment

For example, AI-driven weather forecasting could enable companies to determine when a storm is likely to form, as well as how fast it is moving. This can help companies plan evacuation routes, take preventative measures, and reduce the risk of a natural disaster.

Artificial intelligence can also be used to analyze satellite data to identify changes in the environment. This data can be used to detect changes in the environment that can lead to increased risk of natural disasters, as well as changes in water levels and soil erosion that can also be indicators of an impending event. With the help of AI, organizations can better prepare for natural disasters by taking the necessary steps to protect their assets and communities.

In addition to predictive modeling, AI can also be used to analyze historical data in order to better understand how natural disasters occur. AI can be used to identify patterns in the data and develop more accurate models of how natural disasters will occur in the future. This knowledge can then be used to create improved emergency response plans.

Moreover, AI can also be used to improve disaster preparedness and response. AI-based software can be used to monitor natural disasters in real time, allowing organizations to quickly respond to potential risks. AI can also be used to process sensor data in order to provide a better understanding of the disaster, making it easier to deploy the right rescue resources to the right areas.

Reducing the Effects of the Disaster

In addition to helping organizations prepare for natural disasters, AI can also be used to reduce the impact of disasters.

AI-based analytics can help identify and target critical areas, allowing for more targeted and effective responses to the event. By understanding the types of damage that a natural disaster can cause, organizations can use AI to develop strategies to help reduce the severity of the event or minimize the impact on affected areas.

References

Waheeb, Rasha Abdulrazzak. "Identification of Delay Causes in Construction Projects and Emergency Reconstruction “Iraq as a case study”." (2018).
Waheeb, Rasha A., Bjørn S. Andersen, and Rafea AL Suhili. "Using ANN in emergency reconstruction projects post disaster." International Journal of Engineering Business Management 12 (2020): 1847979020967835. [CrossRef]
Waheeb, Rasha A., and Bjørn S. Andersen. "Causes of problems in post-disaster emergency re-construction projects—Iraq as a case study." Public Works Management & Policy 27.1 (2022): 61-97.
Waheeb, Rasha Abdulrazzak. "APP innovation to control projects risks management during crises." EAI Endorsed Transactions on Internet of Things 8.30 (2022). [CrossRef]
Waheeb, Rasha A. "Strength & Conduct of Reinforced Concrete Corner Joint under Negative Moment Effect." Journal of Architectural Environment & Structural Engineering Research 4.4 (2021): 10-17. [CrossRef]
Waheeb, Rasha A., et al. "Impact of Pandemic SARS COVID-19 on Different Construction Project Management: Problems and Solutions." Public Works Management & Policy (2022): 1087724X221113579. [CrossRef]
Waheeb, Rasha, et al. "The prospective of Artificial Neural Network (ANN’s) model application to ameliorate management of post disaster engineering projects." Available at SSRN 4208375 (2022). [CrossRef]
Waheeb, Rasha. "Diagnoses of Delay Causes in Construction Projects During Disaster." Available at SSRN 4255634 (2022). [CrossRef]
Waheeb, Rasha. "Sustainability of Societies Using Artificial Intelligence in Green Buildings Baghdad." Available at SSRN 4255832 (2022).
Waheeb, Rasha, et al. "Design of earthquake-resistant buildings by using reinforced concrete or steel flexible corner joints." Available at SSRN 4362461 (2023). [CrossRef]
Waheeb, Rasha. "How to Prepare the Project Schedule?" Available at SSRN 4297325 (2022). [CrossRef]
Waheeb, Rasha. "Quality Control of Steel in Steel Iron Product Factory System." Available at SSRN 4408235 (2023). [CrossRef]
Waheeb, Rasha. "Using Digital Twin Technology to Improve Projects Management." Available at SSRN 4255641 (2022).
Waheeb, Rasha. "Dealing with Stabilization and Sustainability issues in Emergency Reconstruction Projects." Available at SSRN 4255645 (2022).
Waheeb, Rasha. "Successful Character of Project Manager (Presentation Slides)." Available at SSRN 4386359 (2018). [CrossRef]
Waheeb, Rasha. "Dealing with Stabilization and Sustainability issues in Emergency Reconstruction Projects." Available at SSRN 4255645 (2022).
Waheeb, Rasha. "How to Prepare An Estimated Bill of Quantities for a Project? Iraqi House as a Case Study’Model’." Iraqi House as a Case Study'Model’(April 17, 2023) (2023). 17 April. [CrossRef]
Waheeb, Rasha. "How to Build Your House?" Available at SSRN 4432814 (2023). [CrossRef]
Waheeb, Rasha A., and Bjørn S. Andersen. "Causes of Problems in Post-Disaster Emergency Re-Construction Projects—Iraq as a Case Study." Public Works Management & Policy 1 (2021): 37. [CrossRef]
Waheeb, Rasha. "Protect Oil Industry Sector in Iraq by Using AI." Available at SSRN 4440154 (2023). [CrossRef]
Waheeb, Rasha. "‘Energy Saving by Using Thermal Insulation Works in Buildings’." Available at SSRN 4447979 (2023). [CrossRef]
Waheeb, Rasha. "Identification of Delay Causes in Construction Projects and Emergency Reconstruction." Available at SSRN 4449802 (2023). [CrossRef]
Waheeb, Rasha. "Mesopotamia Post Disaster 2003 (Emergency Reconstruction Problems and Solutions)." Available at SSRN 4433323 (2023). [CrossRef]
Waheeb, Rasha. "The Genesis of Sustainable Development." Available at SSRN 4451312 (2023). [CrossRef]
Waheeb, Rasha. "Educating societies on sustainable development." Available at SSRN 4452724 (2023). [CrossRef]
Waheeb, Rasha. "The Concept of Sustainable Development." Available at SSRN 4452385 (2023). [CrossRef]
Waheeb, Rasha Abdulrazzak. "Diagnoses of Delay Causes in Construction Projects During Disaster." (2022). [CrossRef]
Waheeb, Rasha. "Risk Management Mechanism." Available at SSRN 4462360 (2023). [CrossRef]
Waheeb, Rasha. "Sustainable ENERGY by using AI." Available at SSRN 4465582 (2023). [CrossRef]
Waheeb, Rasha. "Waste Recycling to Save Energy by Using AI." Available at SSRN 4468539 (2023). [CrossRef]
Waheeb, Rasha. "The Concept of Urbanization in Developing Countries." Available at SSRN 4468913 (2023). [CrossRef]
Waheeb, Rasha. "Enough Pollution and Let’s Work Towards a Sustainable Environment: Baghdad as An Emergency Case Study." Available at SSRN 4473744 (2023). [CrossRef]
Waheeb, Rasha. "Building Countries by Eliminating Corruption." Available at SSRN 4475261 (2023). [CrossRef]
Waheeb, Rasha. "Reducing the Severity of Global Warming to Preserve a Sustainable Environment." Available at SSRN 4468810 (2023). [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.