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.
| 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. |
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-
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.