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Artificial Intelligence in Disaster Risk Market

Artificial Intelligence in Disaster Risk Market Size, Share & Trends Analysis Report, By Type (Supervised Learning, Unsupervised Learning, Reinforcement Learning) By Application (Early Warning Systems, Risk Assessment and Analysis, Response and Recovery Optimization, Damage Assessment and Monitoring) By Sector, By Application, By Region, Forecasts, 2024-2031

Report ID : 2757 | Published : 2024-09-25 | Pages: 170 | Format: PDF/EXCEL

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.

ai in disaster risk

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.

Competitive Landscape

Some of the Major Key Players in the Artificial Intelligence in Disaster Risk Market are

  • 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.
  • Others

Market Segmentation:

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).

Based On Type, The Supervised Learning Segment Accounts For A Major Contributor To Artificial Intelligence In The Disaster Risk Market.

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.

Early Warning Systems Segment Witnessed Growth at a Rapid Rate.

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.

In the Region, North American Artificial Intelligence in the Disaster Risk market Holds a Significant Revenue Share.

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.

Recent Developments:

  • In March 2024, AI-powered flood monitoring systems can monitor water levels in rivers and detect signs of imminent flooding, allowing authorities to issue timely warnings and evacuate at-risk areas. Similarly, AI algorithms can analyze seismic activity to predict earthquakes and tsunamis, enabling communities to take proactive measures to mitigate risks.

Artificial Intelligence in Disaster Risk Market Report Scope

Report Attribute

Specifications

Market Size Value In 2023

USD 479.5 Bn

Revenue Forecast In 2031

USD 2,150.1 Bn

Growth Rate CAGR

CAGR of 21.3% from 2024 to 2031

Quantitative Units

Representation of revenue in US$ Bn and CAGR from 2024 to 2031

Historic Year

2019 to 2023

Forecast Year

2024-2031

Report Coverage

The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends

Segments Covered

By Type, By Application, By Sector and By Region

Regional Scope

North America; Europe; Asia Pacific; Latin America; Middle East & Africa

Country Scope

U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea

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

Customization Scope

Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape.

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.

Segmentation of Artificial Intelligence in Disaster Risk Market

Artificial Intelligence in Disaster Risk Market- By Type

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

ai in disaster risk market

Artificial Intelligence in Disaster Risk Market- By Application

  • Early Warning Systems
  • Risk Assessment and Analysis
  • Response and Recovery Optimization
  • Damage and Assessment Monitoring

Artificial Intelligence in Disaster Risk Market- By Sector

  • Government and Public Sector
  • Insurance and Risk Management
  • Infrastructure and Utilities
  • Non-Governmental Organizations (NGOs)

Artificial Intelligence in Disaster Risk Market- By Region

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of Middle East and 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:

  • Company websites, financial reports, annual reports, investor presentations, broker reports, and SEC filings.
  • External and internal proprietary databases, regulatory databases, and relevant patent analysis
  • Statistical databases, National government documents, and market reports
  • Press releases, news articles, and webcasts specific to the companies operating in the market

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: 

  • Industry participants: CEOs, CBO, CMO, VPs, marketing/ type managers, corporate strategy managers, and national sales managers, technical personnel, purchasing managers, resellers, and distributors.
  • Outside experts: Valuation experts, Investment bankers, research analysts specializing in specific markets
  • Key opinion leaders (KOLs) specializing in unique areas corresponding to various industry verticals
  • End-users: Vary mainly depending upon the market

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.

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Frequently Asked Questions

how big is the Artificial Intelligence in Disaster Risk Market Size?

The Artificial Intelligence in Disaster Risk Market is expected to grow at a 21.3% CAGR during the forecast period for 2024-2031.

IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Intel Corporation, NVIDIA Corporation, Cisco Systems, Inc., SAP SE, Ora

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