Natural Disaster Detection IoT Market by Component, Technology, Application, End-User

Description

List of Tables

[192 Pages Report] 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.

KEY MARKET STATISTICS
Base Year [2023]	USD 6.68 billion
Estimated Year [2024]	USD 8.45 billion
Forecast Year [2030]	USD 37.32 billion
CAGR (%)	27.85%

Component: Increasing reliance on hardware for 24/7 monitoring, real-time updates, and automated alerts of natural disaster

Natural disaster detection IoT hardware involves the tangible components & devices, such as IoT sensors, actuators, computer chips, cables, and smart devices, employed to enable connectivity and detect environmental changes. Computational & storage devices are major in disaster detection IoT, providing the processing power and data archival capabilities required for complex algorithms and long-term information retention. Data transmission devices ensure seamless connectivity and communication between sensors and central data centers, facilitating the swift relay of information. Power supply & energy storage systems are essential for consistent operation, especially in remote or power-deficient areas impacted by natural disasters. The sensors & detectors are the frontline components, tasked with capturing environmental data and signals indicative of potential disasters, while user interface & notification systems democratize access to the information, enabling timely warnings and alerts to stakeholders and the public for prompt action.

The natural disaster detection IoT market offers services to quickly monitor and alert users of potential risks. These services commonly include 24/7 monitoring, real-time updates, automated alerts, and visual dashboards to help track pathways and conditions related to any upcoming natural disaster conditions in real-time. The software solution commonly provides centralized detection systems that send alerts to a command center. Natural disaster detection IoT software can offer more accurate results by integrating advanced technology, such as artificial intelligence and machine learning. Natural disaster management authorities can create better management by equipping a particular region with sensor devices, microcontrollers, and various software applications to detect and analyze environmental conditions. Communication & networking software establishes robust channels for transmitting sensor data and ensures interoperability among various devices and platforms, enabling real-time alerts and coordination during crises. Data analysis & management software processes the vast inflow of environmental data, using sophisticated algorithms to detect patterns, predict events, and support decision-making, enhancing response times and reducing false alarms. Geographic information system (GIS) software visually represents data on maps, integrating layers of information about terrains, populations, and infrastructure, essential for planning, risk assessment, and executing efficient evacuation strategies.

Technology: Increasing adoption of artificial intelligence (AI) to enhance understanding of natural disasters

Advanced computing and big data analytics are pivotal in processing vast amounts of environmental data. These technologies are crucial for interpreting sensor data and weather patterns and providing predictive insights to preempt the effects of natural disasters. High-performance computing systems can manage the vast throughput of data from IoT networks, which are essential for near-real-time analysis. Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of natural disaster detection by enabling systems to understand historical data and improve predictions over time. They assist in forecasting disasters by identifying patterns that usually precede natural disturbances and can provide authorities with actionable insights to mitigate the risks. IoT relies extensively on mobile and communication technologies to transmit data from sensors to the servers where analysis occurs. These technologies are essential for ensuring a seamless flow of information even in the most adverse conditions. Satellites, cellular networks, and wireless communication systems, including 5G, are all part of this infrastructure that makes real-time data transmission possible.

Application: Increasing proliferation for detecting harsh weather conditions

IoT-based real-time earthquake detector systems can be used at the provincial, national, or global levels. The earthquake detection system can be deployed in every city, and every earthquake detector device can be monitored in real time utilizing the IoT platform. The emerging IoT-based automatic flood detection & prevention systems are used to continuously monitor and provide alerts by natural disaster management authorities. Similarly, these IoT-based systems are used to monitor conditions for extreme drought. IoT technology in fire alert systems uses temperature, flame, and smoke sensors to detect cases of fire and provide alerts for early response, even in remote locations. The weather monitoring technologies present over the several years to continuously monitor the weather conditions and update accordingly, which has been enhanced with the integration of the IoT technology. Real-time monitoring of landslides is one of the challenging research areas that can also be monitored with the wireless sensor network for critical and emergency responses.

End-User: Increasing investments from governments in natural disaster detection IoT systems for quick and effective response

Government organizations use this natural disaster detection IoT system for quick and effective response. Governments are increasingly investing in new weather monitoring technologies and tracking natural conditions. Law enforcement agencies utilize IoT systems for disaster detection, mainly for evacuation planning, forensic analysis post-disaster, and maintaining public order during emergencies. Their requirements often focus on mobility, rapid deployment capabilities, and secure communication channels. Private companies may use natural disaster detection IoT systems to protect assets, comply with regulations, and ensure the safety of their personnel, focusing on site-specific needs. They are interested in cost-effective, tailored solutions that can be easily integrated with their operational processes. Rescue personnel, such as firefighters and paramedics, require portable, durable, and user-friendly IoT solutions. They prioritize equipment that aids in locating victims, assessing structural' integrity, and monitoring environmental conditions in real-time during rescue operations.

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.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Natural Disaster Detection IoT Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis 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: 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 examination of the current state of vendors in the Natural Disaster Detection IoT Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. 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 this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

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 Tecnologicas 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

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the Natural Disaster Detection IoT Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Natural Disaster Detection IoT Market?

3. What are the technology trends and regulatory frameworks in the Natural Disaster Detection IoT Market?

4. What is the market share of the leading vendors in the Natural Disaster Detection IoT Market?

5. Which modes and strategic moves are suitable for entering the Natural Disaster Detection IoT Market?

Product Code: MRR-F611BFBC6293

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Increasing frequency and severity of natural disasters with climate change

and global warming

5.1.1.2. Need for early warning systems to minimize damage to buildings and construction sites
5.1.1.3. Surge in adoption of IoT-enabled geographic information system for disaster risk prevention

5.1.2. Restraints
- 5.1.2.1. High costs associated with deployment and maintenance of natural disaster detection IoT
5.1.3. Opportunities
- 5.1.3.1. Advancements in IoT and sensing technology to gather real-time data on natural disasters
- 5.1.3.2. Government initiatives and investments in natural disaster management
5.1.4. Challenges
- 5.1.4.1. Lack of standardization and difficulties in accurately detecting and predicting

5.2. Market Segmentation Analysis
- 5.2.1. Component: Increasing reliance on hardware for 24/7 monitoring, real-time updates, and automated alerts of natural disaster
- 5.2.2. Technology: Increasing adoption of artificial intelligence (AI) to enhance understanding of natural disasters
- 5.2.3. Application: Increasing proliferation for detecting harsh weather conditions
- 5.2.4. End-User: Increasing investments from governments in natural disaster detection IoT systems for quick and effective response
5.3. Market Trend Analysis
- 5.3.1. Presence of a robust and highly developed technological framework and awareness of the need for proactive disaster management and prevention solutions
- 5.3.2. Enhancing disaster preparedness and assessment of international practices and recommendations for policy improvements for IoT implementation in Disaster Risk Reduction among APAC region
- 5.3.3. Government policies and funding for disaster risk reduction and resilience through supportive regulatory frameworks, encouraging innovation in EMEA
5.4. Cumulative Impact of High Inflation
5.5. Porter's Five Forces Analysis
- 5.5.1. Threat of New Entrants
- 5.5.2. Threat of Substitutes
- 5.5.3. Bargaining Power of Customers
- 5.5.4. Bargaining Power of Suppliers
- 5.5.5. Industry Rivalry
5.6. Value Chain & Critical Path Analysis
5.7. Regulatory Framework Analysis
5.8. Client Customization
- 5.8.1. Natural Disaster Detection IoT: Pacific Ocean Islands Trends & Findings

6. Natural Disaster Detection IoT Market, by Component

6.1. Introduction
6.2. Hardware
6.3. Services
6.4. Software

7. Natural Disaster Detection IoT Market, by Technology

7.1. Introduction
7.2. Advanced Computing & Big Data Analytics
7.3. Artificial Intelligence & Machine Learning
7.4. Mobile & Communication Technologies

8. Natural Disaster Detection IoT Market, by Application

8.1. Introduction
8.2. Drought Detection & Management
8.3. Earthquake Management
8.4. Flood Monitoring & Management
8.5. Forest Fire Management
8.6. Landslide Detection & Management
8.7. Weather Monitoring

9. Natural Disaster Detection IoT Market, by End-User

9.1. Introduction
9.2. Government Organizations
9.3. Law Enforcement Agencies
9.4. Private Companies
9.5. Rescue Personnel

10. Americas Natural Disaster Detection IoT Market

10.1. Introduction
10.2. Argentina
10.3. Brazil
10.4. Canada
10.5. Mexico
10.6. United States

11. Asia-Pacific Natural Disaster Detection IoT Market

11.1. Introduction
11.2. Australia
11.3. Bangladesh
11.4. China
11.5. Fiji
11.6. India
11.7. Indonesia
11.8. Japan
11.9. Malaysia
11.10. Pakistan
11.11. Philippines
11.12. Singapore
11.13. South Korea
11.14. Taiwan
11.15. Thailand
11.16. Vietnam

12. Europe, Middle East & Africa Natural Disaster Detection IoT Market

12.1. Introduction
12.2. Denmark
12.3. Egypt
12.4. Finland
12.5. France
12.6. Germany
12.7. Israel
12.8. Italy
12.9. Netherlands
12.10. Nigeria
12.11. Norway
12.12. Poland
12.13. Qatar
12.14. Russia
12.15. Saudi Arabia
12.16. South Africa
12.17. Spain
12.18. Sweden
12.19. Switzerland
12.20. Turkey
12.21. United Arab Emirates
12.22. United Kingdom

13. Competitive Landscape

13.1. Market Share Analysis, 2023
13.2. FPNV Positioning Matrix, 2023
13.3. Competitive Scenario Analysis
- 13.3.1. SAP SE and Zynas Corporation collaborate with Oita University to roll-out emergency-response collaboration tool
- 13.3.2. IBM Advances Geospatial AI to Address Climate Challenges
- 13.3.3. Drones and AI Systems Developed to Detect Natural Disasters
- 13.3.4. Tack One & Infineon develop palm-sized flood monitor for delivering accurate measurements
- 13.3.5. PNNL Researchers Using AI to Aid Disaster Response and Recovery
- 13.3.6. NEC develops technology for disaster damage assessment using a Large Language Model (LLM) and image analysis
- 13.3.7. UCLA Geologists are Using Artificial Intelligence to Predict Landslides
- 13.3.8. Fujitsu and Hexagon digital twin tech aids predictive disaster and traffic safety management
- 13.3.9. NASA Partnership Launches Groundbreaking New Global Flood Early Warning Technology
- 13.3.10. RapidSOS, a big data platform for emergency first responders, raises USD 75 Million
- 13.3.11. China Mobile, Ericsson and partners aim to deploy China-wide 5G natural disaster management solution
- 13.3.12. AiDash launches new climate risk intelligence system to manage the natural disasters and extreme weather events

14. Competitive Portfolio

14.1. Key Company Profiles
14.2. Key Product Portfolio