The Artificial Intelligence in Disaster Risk Market Size was valued at USD 479.5 Bn in 2023 and is predicted to reach USD 2,150.1 Bn by 2031 at a 21.3% CAGR during the forecast period for 2024-2031.
Artificial Intelligence (AI) has emerged as a transformative force in various industries, revolutionizing traditional practices and propelling growth through innovation. One such sector that has witnessed the integration of AI in recent years is disaster risk management. AI's ability to process vast amounts of data, analyze patterns, and make informed decisions has significantly improved disaster preparedness, response, and recovery efforts. This article explores AI's market growth and segmentation trends in disaster risk management, shedding light on its promising potential and benefits.
Furthermore, AI-powered decision support systems enhance disaster preparedness and response efforts. These systems integrate data from multiple sources and provide actionable insights to decision-makers, enabling them to make informed choices in real time. For example, during a natural disaster such as a hurricane or earthquake, AI algorithms can analyze evacuation routes, assess the vulnerability of critical infrastructure, and allocate resources more efficiently. It saves lives, reduces economic losses, and facilitates faster recovery post-disaster.
Artificial intelligence in the disaster risk market is segmented by type, application, and sector. The market is segmented based on type into supervised, unsupervised, and reinforcement learning. The market is segmented by application into Early warning systems, risk assessment and analysis, response and recovery optimization, and damage and assessment monitoring. Based on sector, the market is segmented into government and public sector, insurance and risk management, infrastructure and utilities, and non-governmental organizations (NGOs).
The Supervised Learning category is expected to hold a major share in the global artificial intelligence in disaster risk market in 2023. It is attributed to its ability to analyze and predict disaster scenarios based on historical data effectively. This type of machine learning is advantageous for training models with labeled datasets, enabling accurate forecasting and risk assessment. Supervised Learning algorithms can process large volumes of data to identify patterns, enhance decision-making, and improve response strategies. Its application in disaster risk management includes predicting natural disasters, optimizing emergency response, and minimizing potential damages. The increasing availability of extensive disaster-related datasets and the need for precise predictive analytics drive the prominence of Supervised Learning in this market.
The early warning systems segment is witnessing rapid growth in artificial intelligence in the disaster risk market. This surge is driven by the increasing need for timely and accurate disaster predictions to mitigate damage and save lives. AI-enhanced early warning systems utilize advanced algorithms, machine learning, and data analytics to predict natural disasters such as earthquakes, hurricanes, and floods more precisely. Governments and organizations invest heavily in these technologies to enhance preparedness and response strategies. Integrating AI with IoT devices and real-time data collection further boosts the efficiency of these systems, making them indispensable tools in disaster risk management.
The North America artificial intelligence (AI) in the disaster risk market holds a significant revenue share due to the region's advanced technological infrastructure and strong government support for disaster management initiatives. The increasing frequency of natural disasters, such as hurricanes, wildfires, and floods, has driven the demand for AI-driven predictive analytics and early warning systems. Key factors contributing to this market dominance include substantial investments in AI research and development, a robust ecosystem of tech companies and startups, and a high adoption rate of cutting-edge technologies by emergency management agencies. Furthermore, collaboration between public and private sectors enhances the deployment of AI solutions, thereby solidifying North America's leading position in the AI disaster risk market.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 479.5 Bn |
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Revenue Forecast In 2031 |
USD 2,150.1 Bn |
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Growth Rate CAGR |
CAGR of 21.3% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, By Application, By Sector and By Region |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Intel Corporation, NVIDIA Corporation, Cisco Systems, Inc., SAP SE, Oracle Corporation, Huawei Technologies Co., Ltd., Palantir Technologies Inc., ESRI, Hitachi, Ltd., Accenture PLC, NEC Corporation, Fujitsu Limited, Honeywell International Inc., Siemens AG, General Electric Company, SAS Institute Inc., Splunk Inc., Rockwell Automation, Inc., Panasonic Corporation, Cognizant Technology Solutions Corporation, TIBCO Software Inc., and Others |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence in Disaster Risk Market Snapshot
Chapter 4. Global Artificial Intelligence in Disaster Risk Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
5.2.1. Supervised Learning
5.2.2. Unsupervised Learning
5.2.3. Reinforcement Learning
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:
6.2.1. Early Warning Systems
6.2.2. Risk Assessment and Analysis
6.2.3. Response and Recovery Optimization
6.2.4. Damage Assessment and Monitoring
Chapter 7. Market Segmentation 3: by Sector Estimates & Trend Analysis
7.1. by Sector & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Sector:
7.2.1. Government and Public Sector
7.2.2. Insurance and Risk Management
7.2.3. Infrastructure and Utilities
7.2.4. Non-Governmental Organizations (NGOs)
Chapter 8. Artificial Intelligence in Disaster Risk Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.1.2. North America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.1.3. North America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Sector, 2024-2031
8.1.4. North America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.2. Europe
8.2.1. Europe Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.2.2. Europe Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.2.3. Europe Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Sector, 2024-2031
8.2.4. Europe Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.3. Asia Pacific
8.3.1. Asia Pacific Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.3.2. Asia Pacific Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.3.3. Asia-Pacific Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Sector, 2024-2031
8.3.4. Asia Pacific Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.4. Latin America
8.4.1. Latin America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.4.2. Latin America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.4.3. Latin America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Sector, 2024-2031
8.4.4. Latin America Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.5. Middle East & Africa
8.5.1. Middle East & Africa Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.5.2. Middle East & Africa Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.5.3. Middle East & Africa Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by Sector, 2024-2031
8.5.4. Middle East & Africa Artificial Intelligence in Disaster Risk Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IBM Corporation
9.2.2. Microsoft Corporation
9.2.3. Google LLC
9.2.4. Amazon Web Services, Inc.
9.2.5. Intel Corporation
9.2.6. NVIDIA Corporation
9.2.7. Cisco Systems, Inc.
9.2.8. SAP SE
9.2.9. Oracle Corporation
9.2.10. Huawei Technologies Co., Ltd.
9.2.11. Palantir Technologies Inc.
9.2.12. ESRI
9.2.13. Hitachi, Ltd.
9.2.14. Accenture PLC
9.2.15. NEC Corporation
9.2.16. Fujitsu Limited
9.2.17. Honeywell International Inc.
9.2.18. Siemens AG
9.2.19. General Electric Company
9.2.20. SAS Institute Inc.
9.2.21. Splunk Inc.
9.2.22. Rockwell Automation, Inc.
9.2.23. Panasonic Corporation
9.2.24. Cognizant Technology Solutions Corporation
9.2.25. TIBCO Software Inc.
Artificial Intelligence in Disaster Risk Market- By Type
Artificial Intelligence in Disaster Risk Market- By Application
Artificial Intelligence in Disaster Risk Market- By Sector
Artificial Intelligence in Disaster Risk Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
