Research & Development on Multi-Hazard Early Warning System (EWS)

• AI-enabled hazard detection and multi-hazard early warning systems, integrating AI-driven reconnaissance surface observations, IoT sensor-based, and acquisition of crowdsourced weather variables, hazard onset detection, event situational awareness, and hazard hotspot tracking.
• IoT sensor, AI, UAV, and drone–driven monitoring systems for climate change and multi-hazard exposure, risk, and vulnerability assessment of climate-vulnerable productive sectors, including agriculture, livestock, fisheries, water resources, environment, forests, ecology and biodiversity, and human and food security.
• IoT sensor, AI, UAV, and drone–enabled rapid post-disaster loss, damage, and needs assessment (RPDNA) to support timely response, recovery planning, and evidence-based decision-making.

Overall Goal

To design, develop, and operationalize a robust, AI- and ICT-driven multi-hazard early warning and disaster risk management system integrating satellite, UAV/Drone, ground sensors, and human intelligence, to minimize losses and damages (L&Ds) and protect vulnerable communities and ecosystems.

Specific Objectives

• Develop a multi-hazard early warning system (EWS) integrating advanced sensors, AI models, and multi-source data.
• Design and test an AI-driven Rapid Post-disaster Damage, Loss, and Needs Assessment (RPDNA) system.
• Conceptualize and prototype a new generation weather satellite system with multi-sensor payloads tailored for hazardous weather and risk element detection.
• Develop an integrated hazard detection and reconnaissance architecture using UAVs/Drones, satellites, and ground-based sensors.
• Design and validate AI-supported forecast-driven anticipatory early action protocols for humanitarian and climate frontline actors.
• Build a unified AI-ICT platform that fuses satellite anomalies, ground sensors, AI alerts, and human observations into a single operational EWS.
• Develop a monitoring system for environmental degradation and critical ecosystems (sanctuaries, estuaries, reserve forests, ecologically critical areas, agro-ecology).

R&D Components :

Research Package 1: Multi-Hazard Early Warning System (Core R&D)

Focus:
Robust research and development on a multi-hazard EWS that can handle cyclones, floods, landslides, heatwaves, storm surges, wildfires, etc.

Key Tasks:

• Define priority hazards, risk indicators, and thresholds.
• Design architecture for real-time data ingestion from:
  • Weather satellites
  • UAV/Drone platforms
  • Ground-based sensors (hydro-met, seismic, air quality, etc.)
  • Human/field reports
• Develop hazard-specific AI models for:
  • Nowcasting and short-term forecasting
  • Anomaly detection in environmental and weather parameters
• Integrate outputs into operational dashboards for:
  • Weather Department
  • National Disaster Management Organization (NDMO)
  • Humanitarian coordination centers

Outputs:

• System architecture & prototype multi-hazard EWS.
• Algorithms and thresholds for multiple hazard types.
• Operational guidelines and SOPs for EWS activation.

Research Package 2: AI-Driven Rapid Post-Disaster Damage, Loss, and Needs Assessment (RPDNA)

Focus:
An AI-driven system to conduct Rapid Post-disaster Damage, Loss, and Needs Assessment (RPDNA).

Key Tasks:

• Use before/after satellite imagery, UAV reconnaissance imagery, and ground photos to:
  • Detect building damage, road blockage, crop loss, and infrastructure disruption.
• Develop computer vision models for:
  • Change detection
  • Damage classification (none, minor, major, destroyed)
• Link RPDNA outputs to:
  • Loss & Damage (L&D) estimates
  • Immediate needs (shelter, WASH, health, food, logistics)
• Create RPDNA dashboards for NDMO, government agencies, and humanitarian clusters.

Outputs:

• AI-based RPDNA tool and interface.
• Standardized RPDNA methodology and indicators.
• Data-sharing protocols with humanitarian actors.

Research Package 3: New Weather Satellite Design with Multi-Sensor Payload

Focus:
New design of weather satellite with installations of multiple sensors for detecting hazardous weather variations and multi-hazard risk elements, capable of sending real-time alerts to the Weather Department and NDMO.

Key Tasks:

• Define sensor suite:
  • Multispectral/hyperspectral imagers
  • Microwave radiometers
  • Lightning mapper
  • Atmospheric sounders
  • Environmental monitoring bands (aerosols, dust, smoke, ash)
• Specify performance requirements:
  • Spatial, temporal, and spectral resolution
  • Latency for near-real-time alerts
• Design onboard AI concepts for:
  • Edge processing
  • Onboard anomaly detection
  • Priority downlink of critical data
• Integrate alert channels (e.g., direct broadcast to national receiving stations and NDMO).

Outputs:

• Concept design document for next-generation weather satellite.
• Sensor requirement specifications for hazardous weather and risk monitoring.
• Data format and downlink protocol specifications.

Research Package 4: Multi-Platform Hazard Detection (Satellite + UAV/Drone + Recon UAV + Ground Sensors)

Focus:
Research a robust hazard detection system that fuses information from UAV/Drone, Satellite sensors, Reconnaissance UAVs, ground sensors, etc.

Key Tasks:

• Map the roles of each platform:
  • Satellites – wide-area, continuous monitoring
  • UAV/Drone – high-resolution local reconnaissance
  • Ground sensors – localized, high-precision, real-time data
• Develop data fusion algorithms for combining:
  • Spaceborne
  • Airborne
  • Ground-based datasets
• Optimize tasking logic: AI triggers a UAV reconnaissance mission when:
  • Satellite detects anomalies
  • Ground sensor crosses threshold
  • Human observation report is validated

Outputs:

• Hazard detection and reconnaissance workflow.
• Data fusion algorithms and operational prototype.
• Procedures for coordinated satellite–UAV–ground deployment.

Research Package  5: AI-Supported Forecast-Driven Anticipatory Early Action Protocol

Focus:
Conduct research on an AI-system-supported, forecast-driven, precision early warning + anticipatory action protocol, synchronized with risk and vulnerability elements and risk repository databases.

Key Tasks:

• Build a risk repository database:
  • Exposure (population, housing, infrastructure, crops)
  • Vulnerability (poverty, disability, access to services, protection needs)
  • Historical impact and L&D data
• Link forecast outputs (probabilistic hazard forecasts) with:
  • Triggers for anticipatory actions
  • Pre-defined response plans and financing windows
• Design AI decision-support tools that:
  • Propose where, when, and what anticipatory actions should be taken
  • Optimize resource allocation (cash, NFIs, evacuation, protection measures)
• Co-create protocols with:
  • Humanitarian actors
  • Government agencies
  • Local “climate frontline” communities and organizations

Outputs:

• Forecast-based financing/early action trigger model.
• Anticipatory action SOPs and protocol.
• Risk and vulnerability repository and interface.

Research Package 6: AI-ICT System for Integrated Reconnaissance & Early Warning

Focus:
AI and ICT system design for Reconnaissance (satellite, Drone, UAV, ground-level sensors) of rapidly developing hazardous conditions, synthesizing multiple sources of datasets and building a robust AI-ICT-driven EWS.

Data sources to be synchronized:

• Satellite-detected parametric anomalies
• Ground-level sensor anomalies
• AI-processed and generated alerts
• Human observation (crowdsourced, field teams)
• UAV/Drone reconnaissance data

Key Tasks:

• Design a unified data platform:
  • Real-time ingestion
  • Harmonization and standardization of formats
  • Metadata and quality flags
• Develop AI engines for:
  • Multi-source anomaly correlation
  • Confidence scoring and false-alarm reduction
  • Automatic generation of coherent alerts and situation summaries
• Implement role-based dashboards for:
  • Weather Dept, NDMO, line ministries
  • Humanitarian actors and local authorities

Outputs:

• Integrated AI-ICT EWS platform (prototype).
• APIs and data-sharing standards.
• Training materials and capacity-building modules.

Research Package  7: Environmental Degradation and Ecosystem Monitoring System

Focus:
System design for monitoring AI-driven UAV/Drone/sensor-based environmental degradation in:

• Wildlife sanctuaries
• Estuaries and coastal zones
• Reserve forests
• Ecologically critical areas (ECA)
• Protected/conservation areas
• Agro-ecological zones

Key Tasks:

• Define ecological indicators:
  • Deforestation, land cover change, erosion
  • Wetland shrinkage, salinity intrusion
  • Habitat fragmentation, fire scars
  • Crop health and agro-ecological stress
• Develop satellite + UAV monitoring workflows:
  • Regular baseline mapping
  • Event-based reconnaissance (e.g., after storms, floods)
• Design AI models for:
  • Land cover classification
  • Degradation trend analysis
  • Hotspot identification
• Integrate outputs into EWS as slow-onset hazard signals (e.g., long-term environmental degradation increasing disaster risk).

Outputs:

• Ecosystem monitoring system prototype.
• Ecological risk layers integrated into the risk repository.
• Maps and dashboards for environmental authorities.

5. Cross-Cutting Themes

• Data Governance & Interoperability
  Open standards, APIs, and data-sharing agreements among the Weather Dept, NDMO, and humanitarian actors.
• Ethics, Privacy, and Community Engagement
  Responsible AI, community participation, inclusion of marginalized groups.
• Capacity Building & Sustainability
  Training for government, local universities, and humanitarian agencies to run and maintain the systems.

Figure: Internet of Things Solutions for Early Warning Systems: A Review (by  Marco Esposito ,Lorenzo Palma  ,Alberto Belli ,Luisiana Sabbatini and Paola Pierleoni

Technical concept of GITEWS

31.05.2026 :: German :: Print  Site: Concept /  Concept New scientific processes and innovative technologies distinguish this system from the previous tsunami warning systems. Due to the specific geological situation in Indonesia, the previously used, established tsunami warning systems are not optimal for Indonesia. The earthquakes in the Indian Ocean at Indonesia originate along the Sunda Trench, a subduction zone which extends in an arch from the northwest tip of Sumatra to Flores in eastern Indonesia. If a tsunami originates here, in an extreme case, the waves reach the coast within 20 minutes, so that only very little time remains for an early warning. Therefore, the concept of the entire system was based on this prevailing condition. Technical concept of GITEWS Technical Implementation Due to the local geology, the advance warning time is extremely short. Therefore, an alarm must be triggered within five minutes after a strong earthquake. That is why a new approach was developed, which is primarily based on model-based coupling of seismological data with GPS measurements and level measurements. More than 300 sensors are distributed across all of Indonesia and supply their data to the warning centre in real time. From this, a newly developed, automated Decision Support System (cf. below Decision Support System DSS) compiles a picture of the situation from this, on the basis of which the decision is made whether to issue an alarm. Therefore, the following steps are taken in the process: Quake location and strength: Ascertainment with seismological data; all earthquakes (worldwide) are recorded. All earthquakes M≥2 are evaluated in the national warning centre. Earthquake information is basically provided. Tsunami warning alerts are only issued if a tsunami is expected (earthquake magnitude >7). Fracture mechanism: only strong sub-oceanic earthquakes with a distinct vertical component can cause tsunamis. An initial assessment as to whether the ocean floor has moved vertically can be determined using land fixed points (cf. below GPS Shield). Ascertainment of a tsunami: On the coasts and on the offshore islands of Indonesia, tide gauges were installed with GPS components, which monitor the sea level. The data are integrated into the warning process (cf. below GPS Level). Decision-making: within less than five minutes, the decision can be made as to whether a warning needs to be issued and – if so – to which coastal sections (cf. below DSS). Technical innovation, modernised approach GITEWS forms the core structure of the InaTEWS Indonesian Tsunami Early Warning System. With the setup of GITEWS, due to the specific conditions of Indonesia, with its extremely short advance warning times, the experiences of the previously existing tsunami early warning systems for the Pacific in the USA and Japan could only be exploited to a limited extent. As a result of this challenge, the newly developed components and procedures and their interaction in GITEWS/InaTEWS make the system one of the most state-of-the-art tsunami early warning systems worldwide. Seiscomp3 The basic requirement for the early warning system is fast and reliable ascertainment of the site and magnitude of an earthquake. SeisComp3 was developed by the GEOFON working group of the GFZ and can reliably determine earthquake strength and location within around four minutes, even with strong earthquakes. This makes SeisComp3 unique worldwide. GFZ provided this system to the community free of charge, so that all countries bordering the Indian Ocean have implemented this system quasi as standard. GPS-Shield Strong quakes cause a considerable horizontal and vertical displacement on the Earth’s surface, which can be several metres long, both horizontally and vertically and can be measured with GPS. Subject to an accordingly dense measurement network, this “GPS Shield”, together with the seismological data, is able to characterise the earthquake fracture within 5 minutes, so that the strength and expansion of a tsunami can be calculated. This new procedure has been made ready for use in GITEWS and is now used as a standard method for tsunami identification in the near field. GPS Tide Gauges GPS tide gauges monitor the sea level. The changes in water level caused by a tsunami are recorded and integrated into the warning process. The GITEWS gauges record the changes using three types of sensors: Pressure, radar and floaters and are additionally equipped with GPS receivers to determine a possible vertical displacement of the surface. In the meantime, reliable level data are not only available in Indonesia, but also in other countries bordering the Indian Ocean. The data are also available in public databases of the IOC. Webcams are also installed for observation at individual exposed sections of coastline. DSS Decision Support System The Decision Support System is one of the key elements of the warning centre in Jakarta. The results of the sensor data networks merge here, are compared to pre-calculated modelling and thereby create a picture of the situation and propose a warning alert, if necessary, which must then be released by the scientists on duty. If necessary, pre-calculated risk maps can also be displayed for decision-making. The DSS was developed by the German Aerospace Centre (DLR) within the context of the GITEWS. Modelling System The situation assessment and generation of warning alerts is based on modelling results. From a small amount of data, which is available within the first approx. 5 minutes after the occurrence of an earthquake (earthquake location, magnitude, information of the GPS Shield, if applicable), an extensive situation status can only be generated using modelling. This takes place, on the one hand, with pre-calculated, high-resolution scenarios in a database and on the other hand (and only in the last few years) through a less high-resolution, but online calculating computer process. In addition to the tsunami calculation (running time to the coast, wave height at the coast), the high-resolution scenarios also contain calculations of the subsequent floods, which is a crucial input factor for all risk assessments and e.g. evacuation measures. This is supplemented with constant updates using the online tool. Therefore, both options (pre-calculated scenarios and online tool) will always be used. The dispatch of the warning alerts by BMKG takes place through various and technically autonomous communication channels and is defined by “standard operating procedures”. Buoy System Tsunami buoys (also referred to as tsunameters) are not autonomous WARNING systems. In all tsunami warning systems worldwide, they are MEASUREMENT instruments for the verification of a tsunami. The most important information, namely, the fast earthquake location and magnitude, without which either a simulation or a warning can be generated, can NOT be supplied by buoy systems. Buoy systems for the direct measurement of a tsunami were initially part of the research concept. The further development of the GPS Shield made it possible to discontinue pursuing the buoy concept. Therefore, buoys have no longer by part of the operational warning system since 2010, so that the high maintenance cost of buoy installations near coastlines can also be omitted. Chronological sequence of the warning process The system is based on 300 different land-based sensor systems. The data from these sensors are transferred in real-time to the control room in the warning centre and are aggregated there in the state-of-the-art Decision Support System (DSS) and implemented into a situation status. The warning takes place on the basis of very fast, precise earthquake recording and evaluation, which forms the heart of the warning system. The fast determination of earthquake parameters (location, depth, magnitude) through 160 seismometers on land is the first and most important basis for the tsunami preview through modelling and the generation of a warning alert, which is based on this. The first situation status is then substantiated further through additional data from GPS stations and tide gauges along the coast of Indonesia. The verification of a tsunami takes place with tide gauges, which are also equipped with GPS sensors. Implementation process Right from the start, GITEWS was planned with an end-to-end approach. This is comprised of setting up instrument networks for measuring the natural disaster (tsunami, earthquake), the decision-making support on the basis of a modelling system for generating situation assessments, a country-wide risk assessment with the creation of hazard, vulnerability and risk maps and the capacity development with authorities, local decision-makers and administrations, as well as affected local companies and the hotel industry. This work on the various fields of activity was performed in parallel right from the start, whereas constant coordination took place between the fields of activity and the national and international partners involved. The installation phase of GITEWS was characterised by the development of necessary system components, on the one hand, and by the development of appropriate strategies, information materials, standards and approaches, on the other hand. The BMKG (Badan Meteorologi, Klimatologi dan Geofisika), operator of the early warning system, reached the operational status step-by-step and over various phases of setting up the system. During the GITEWS phase (2005 to 2011), a series of German institutions were involved, under the auspices of the German Centre for Geosciences GFZ, whose task was the technical setup of the system. The contributions of other donor countries were integrated. Capacity development measures in the downstream area (Disaster Reduction Strategy) were implemented in pilot regions, in cooperation with the local administrations and the population. The approaches, processes and products, e.g. the

Please Support Research projects :

Seeking partnerships, collaboration, and support from international donor agencies, global climate foundations, glocal R&D organizations, and global multi-national enterprises to provide financial support for conducting extensive research, robust EWS system design, IT programming, and AI system development.

The Multi-hazard Early Warning System Design & Implementation Center (MHEWC) www.mhewc.org engaged in Research and Development (R&D) to develop L&D assessment tools to quantify the ground-level elements impacted by disasters using GIS, RS (SAR image) & GPS tools, and UAV (drone, aerial photographs) captured elements to compare pre- & post-disaster impact levels and develop a strategy on how to minimize L&Ds. Please finance MHEWC to create the most advanced tools (GIS, RS, GPS, drone/UAV-captured elements, repository database queried by Artificial Intelligence) to support the sector department in minimizing L&Ds.

R&D on hazard detection, automated public alerts, command & control systems on emergency preparedness, evacuation deployment for lifesaving, extent of areas and L&Ds elements, and overall combat readiness(inclusive social participatory dynamics) for addressing multi-hazards, disaster threats, and emergencies.

Pleading for funds from climate funding windows, foundations, multinational companies-led CSRs, the UN, and INGOs, etc., please provide funds to carry out much-needed R&D to detect threats of impending dangerous weather events, instrumentalize the EOC/Command & control room with robust multi-hazard early warning systems, and enhance the hazard-combat readiness of inclusive social-participatory dynamics forces to combat disasters.

Seeking funds for establishing a big complex of the Multi-hazard Early Warning System Design & Implementation Center (MHEWC)