Natural Disaster Detection IoT Market by Component (Hardware, Services, Software), Technology (Advanced Computing & Big Data Analytics, Artificial Intelligence & Machine Learning, Mobile & Communication Technologies), Application, End-User - Global Foreca

Natural Disaster Detection IoT Market by Component (Hardware, Services, Software), Technology (Advanced Computing & Big Data Analytics, Artificial Intelligence & Machine Learning, Mobile & Communication Technologies), Application, End-User - Global Forecast 2024-2030

The Natural Disaster Detection IoT Market size was estimated at USD 6.68 billion in 2023 and expected to reach USD 8.45 billion in 2024, at a CAGR 27.85% to reach USD 37.32 billion by 2030.

Natural disaster detection using the Internet of Things (IoT) refers to the application of interconnected, sensor-equipped devices to collect and transmit data to detect, monitor, and respond to natural disasters. These IoT devices are deployed in areas susceptible to natural catastrophes such as earthquakes, tsunamis, hurricanes, floods, and wildfires, providing real-time data crucial for early warning and rapid response. Growth in the natural disaster detection IoT market is influenced by technological advancements in sensors and machine-to-machine communication, increased global prevalence of natural disasters due to climate change, urbanization in vulnerable areas, and governmental investment in disaster preparedness infrastructure. Additionally, integrating artificial intelligence (AI) and machine learning (ML) for predictive analytics and the growing adoption of cloud computing in IoT platforms further stimulate demand. However, several limitations and challenging factors include high initial set-up costs of IoT infrastructure, maintenance & updating of sensors & equipment, data privacy & security concerns, and the need for standardization across different technologies are hampering the market growth. Moreover, scaling IoT infrastructure, creating robust, low-latency communication networks for real-time alerts, and developing AI-driven predictive models are current opportunities that accurately anticipate disaster events. There is also expanding potential in public-private partnerships to enhance community resilience and the deployment of edge computing to process data closer to the source, thereby hastening response times. Furthermore, it is expected to focus more on research & development to enhance the precision of real-time data analysis, create adaptive learning systems for evolving threat scenarios, and explore blockchain technologies for secure and reliable data sharing during disaster events. Advancing public awareness and education programs on technology adoption could further drive market penetration and expansion.

Regional Insights

The Asia-Pacific region has witnessed an uptick in natural disaster occurrences, such as earthquakes in Japan, tsunamis in Southeast Asia, and cyclones in India, spawning increased consumer demand for IoT solutions in natural disaster detection. Japan strategically utilizes a sophisticated combination of IoT and seismography for early warnings. The region shows a surge in advanced sensor technology and communication patents, with China at the forefront of R&D investments. The Americas are focused on responding to hurricanes, tornadoes, and wildfires, with American companies pushing the envelope in disaster-detection-cum-mitigation IoT tools that blend seamlessly with smart homes and provide real-time alerts. Canada, facing challenges due to its diverse climate, is channeling efforts into tailored IoT responses for wildfires and floods. The U.S. FEMA's Integrated Public Alert and Warning System typifies government investment in harnessing IoT for widespread disaster alerts. Moreover, the EMEA region's response is shaped by its diverse geography and climatic conditions, with technological investments adhering to scientific rigor and sustainability. The Middle East concentrates on combating desertification, investing in IoT to anticipate sandstorms and manage water scarcity, reflecting the region's adaptation to its dry climate. In Africa, the focus is on affordable and deployable IoT systems to cope with droughts, floods, and locust swarms, highlighted by the African Union's Africa Disaster Risk Financing Programme supporting disaster risk reduction tech, including IoT.

Market Insights

• Market Dynamics
  
  The market dynamics represent an ever-changing landscape of the Natural Disaster Detection IoT Market by providing actionable insights into factors, including supply and demand levels. Accounting for these factors helps design strategies, make investments, and formulate developments to capitalize on future opportunities. In addition, these factors assist in avoiding potential pitfalls related to political, geographical, technical, social, and economic conditions, highlighting consumer behaviors and influencing manufacturing costs and purchasing decisions.
  • Market Drivers
    • Increasing frequency and severity of natural disasters with climate changeand global warming
    • Need for early warning systems to minimize damage to buildings and construction sites
    • Surge in adoption of IoT-enabled geographic information system for disaster risk prevention
    • Market Restraints
      • High costs associated with deployment and maintenance of natural disaster detection IoT
      • Market Opportunities
        • Advancements in IoT and sensing technology to gather real-time data on natural disasters
        • Government initiatives and investments in natural disaster management
        • Market Challenges
          • Lack of standardization and difficulties in accurately detecting and predicting
          • Market Segmentation Analysis
            • Component: Increasing reliance on hardware for 24/7 monitoring, real-time updates, and automated alerts of natural disaster
            • Technology: Increasing adoption of artificial intelligence (AI) to enhance understanding of natural disasters
            • Application: Increasing proliferation for detecting harsh weather conditions
            • End-User: Increasing investments from governments in natural disaster detection IoT systems for quick and effective response
            • Market Disruption Analysis
            • Porter’s Five Forces Analysis
            • Value Chain & Critical Path Analysis
            • Pricing Analysis
            • Technology Analysis
            • Patent Analysis
            • Trade Analysis
            • Regulatory Framework Analysis
            FPNV Positioning Matrix
            
            The FPNV positioning matrix is essential in evaluating the market positioning of the vendors in the Natural Disaster Detection IoT Market. This matrix offers a comprehensive assessment of vendors, examining critical metrics related to business strategy and product satisfaction. This in-depth assessment empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success, namely Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
            
            Market Share Analysis
            
            The market share analysis is a comprehensive tool that provides an insightful and in-depth assessment of the current state of vendors in the Natural Disaster Detection IoT Market. By meticulously comparing and analyzing vendor contributions, companies are offered a greater understanding of their performance and the challenges they face when competing for market share. These contributions include overall revenue, customer base, and other vital metrics. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With these illustrative details, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
            
            Recent Developments
            • SAP SE and Zynas Corporation collaborate with Ōita University to roll-out emergency-response collaboration tool
              
              Ōita University collaborated with SAP and Zynas Corporation to foster a transformative solution for disaster management. Their innovation, EDiSON (Earth Disaster Intelligent System Operational Network), is an emergency-response collaboration tool that employs SAP HANA Cloud's data management, advanced analytics, artificial intelligence, and machine learning. This platform revolutionizes disaster preparedness by integrating diverse data sources and enhancing real-time risk assessment and response coordination.
              
              IBM Advances Geospatial AI to Address Climate Challenges
              
              IBM collaborated with NASA to leverage its advanced geospatial AI capabilities in tackling climate change-related issues. A notable partnership with NASA has led to the creation of a comprehensive AI foundation model focusing on weather and climate analysis. IBM's ongoing projects include groundbreaking work in the United Arab Emirates with the Mohamed Bin Zayed University of Artificial Intelligence to map and mitigate urban heat islands in Abu Dhabi. In Kenya, these efforts extend to reforestation initiatives. At the same time, in the UK, collaborations with the Science and Technology Facilities Council Hartree Centre aim to bolster climate resiliency within the aviation sector.
              
              Drones and AI Systems Developed to Detect Natural Disasters
              
              Manchester Metropolitan University spearheads an innovative initiative to enhance natural disaster responsiveness by developing a cutting-edge early-warning system. This advanced approach integrates the cutting-edge capabilities of unmanned aerial vehicles (drones) with the analytical prowess of artificial intelligence. The system is tailored to identify and monitor various natural disasters more accurately and quickly. The deployment of this technology aims to significantly improve reaction times in crises, potentially saving lives and mitigating damage by enabling faster and more informed decision-making.
            Strategy Analysis & Recommendation
            
            The strategic analysis is essential for organizations seeking a solid foothold in the global marketplace. Companies are better positioned to make informed decisions that align with their long-term aspirations by thoroughly evaluating their current standing in the Natural Disaster Detection IoT Market. This critical assessment involves a thorough analysis of the organization’s resources, capabilities, and overall performance to identify its core strengths and areas for improvement.
            
            Key Company Profiles
            
            The report delves into recent significant developments in the Natural Disaster Detection IoT Market, highlighting leading vendors and their innovative profiles. These include ABB Ltd., Accenture PLC, ALE International SAS, Aplicaciones Tecnológicas S.A., AT&T Inc., Atos SE, BlackBerry Limited, Cisco Systems Inc., Eaton Corporation PLC, Environmental Systems Research Institute, Inc, Google LLC by Alphabet Inc., Green Stream Technologies, Inc., Grillo Holdings Inc., Hala Systems, Inc., Hitachi Ltd., InfiSIM Ltd., Infosys Limited, Intel Corporation, International Business Machines Corporation, Knowx Innovations Pvt. Ltd., Mitsubishi Electric Corporation, NEC Corporation, Nokia Corporation, One Concern, Inc., Optex Co., Ltd., OroraTech GmbH, Responscity Systems Private Limited, Sadeem International Company, SAP SE, Scanpoint Geomatics Ltd., Semtech Corporation, Sony Group Corporation, Telefonaktiebolaget LM Ericsson, Tractable Ltd., Trinity Mobility Private Limited, Venti LLC, and Zebra Technologies Corporation.
            
            Market Segmentation & Coverage
            
            This research report categorizes the Natural Disaster Detection IoT Market to forecast the revenues and analyze trends in each of the following sub-markets:
            • Component
              • Hardware
                • Computational & Storage Devices
                • Data Transmission Devices
                • Power Supply & Energy Storage
                • Sensors & Detectors
                • User Interface & Notification Systems
                • Services
                • Software
                  • Communication & Networking Software
                  • Data Analysis & Management Software
                  • Geographic Information System (GIS) Software
                  • Technology
                    • Advanced Computing & Big Data Analytics
                    • Artificial Intelligence & Machine Learning
                    • Mobile & Communication Technologies
                    • Application
                      • Drought Detection & Management
                      • Earthquake Management
                      • Flood Monitoring & Management
                      • Forest Fire Management
                      • Landslide Detection & Management
                      • Weather Monitoring
                      • End-User
                        • Government Organizations
                        • Law Enforcement Agencies
                        • Private Companies
                        • Rescue Personnel
                        • Region
                          • Americas
                            • Argentina
                            • Brazil
                            • Canada
                            • Mexico
                            • United States
                              • California
                              • Florida
                              • Illinois
                              • New York
                              • Ohio
                              • Pennsylvania
                              • Texas
                              • Asia-Pacific
                                • Australia
                                • Bangladesh
                                • China
                                • Fiji
                                • India
                                • Indonesia
                                • Japan
                                • Malaysia
                                • Pakistan
                                • Philippines
                                • Singapore
                                • South Korea
                                • Taiwan
                                • Thailand
                                • Vietnam
                                • Europe, Middle East & Africa
                                  • Denmark
                                  • Egypt
                                  • Finland
                                  • France
                                  • Germany
                                  • Israel
                                  • Italy
                                  • Netherlands
                                  • Nigeria
                                  • Norway
                                  • Poland
                                  • Qatar
                                  • Russia
                                  • Saudi Arabia
                                  • South Africa
                                  • Spain
                                  • Sweden
                                  • Switzerland
                                  • Turkey
                                  • United Arab Emirates
                                  • United Kingdom
                                  
                                  
                                  Please Note: PDF & Excel + Online Access - 1 Year