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-
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.
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.