Reference health
The role of artificial intelligence for early warning systems: Status, applicability, guardrails, and ways forward
100/100 checkable references clean · checked 2026-07-14
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a publisher retraction, withdrawal, or removal notice.
52 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 100 checked references that resolve
resolves10.3390/su151813493
Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health
resolves10.1038/s41586-019-0912-1
Deep learning and process understanding for data-driven Earth system science
resolves10.3390/s22062124
Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
resolves10.1029/2022EF002747
Caught Between Extremes: Understanding Human‐Water Interactions During Drought‐To‐Flood Events in the Horn of Africa
resolves10.5194/nhess-25-287-2025
Spatial identification of regions exposed to multi-hazards at the pan-European level
resolves10.1038/d41586-021-00927-x
More floods, fires and cyclones — plan for domino effects on sustainability goals
resolves10.1016/j.isci.2022.105491
Compound impacts from droughts and structural vulnerability on human mobility
resolves10.1007/s13753-025-00622-9
Multi-Hazard Early Warning Systems in the Sendai Framework for Disaster Risk Reduction: Achievements, Gaps, and Future Directions
resolves10.1038/s41467-025-57640-w
Early warning of complex climate risk with integrated artificial intelligence
resolves10.1038/d41586-024-03149-z
AI to the rescue: how to enhance disaster early warnings with tech tools
resolves10.1088/1748-9326/acf601
When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review
resolves10.1016/j.jhydrol.2024.131305
Modelling the role of multiple risk attitudes in implementing adaptation measures to reduce drought and flood losses
resolves10.1108/DPM-11-2018-0358
Progress, traditions and future directions in research on disasters involving slow-onset hazards
resolves10.1007/s12145-024-01253-2
A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence
resolves10.3390/cli10070097
Applying Machine Learning for Threshold Selection in Drought Early Warning System
resolves10.1016/j.scs.2022.104307
Urban flood susceptibility mapping based on social media data in Chengdu city, China
resolves10.1016/j.firesaf.2019.01.006
Predictive modeling of wildfires: A new dataset and machine learning approach
resolves10.1016/j.jhydrol.2023.130117
A flood Impact-Based forecasting system by fuzzy inference techniques
resolves10.1029/2024GL110672
Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning
resolves10.1016/j.jhydrol.2024.131059
Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis
resolves10.1088/1742-6596/1951/1/012056
Earthquake detection and location for Earthquake Early Warning Using Deep Learning
resolves10.1029/2023EF004211
Predicting Food‐Security Crises in the Horn of Africa Using Machine Learning
resolves10.1016/j.comcom.2024.07.016
A parallel machine learning-based approach for tsunami waves forecasting using regression trees
resolves10.1016/j.scitotenv.2018.10.406
Financing agricultural drought risk through ex-ante cash transfers
resolves10.3389/feart.2024.1342468
Universal machine learning approach to volcanic eruption forecasting using seismic features
resolves10.1038/s41586-022-04672-7
Instantaneous tracking of earthquake growth with elastogravity signals
resolves10.1016/j.jenvman.2023.117908
A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework
resolves10.1016/j.compgeo.2023.105924
Deep-learning-based landslide early warning method for loose deposits slope coupled with groundwater and rainfall monitoring
resolves10.1016/j.foreco.2023.121274
Trends in bushfire related tweets during the Australian ‘Black Summer’ of 2019/20
resolves10.3390/atmos14071141
Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems
resolves10.1016/j.acags.2024.100194
Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system
resolves10.1016/j.jhydrol.2024.131082
Real time probabilistic inundation forecasts using a LSTM neural network
resolves10.1029/2020GL088731
Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes
resolves10.1038/s41598-021-96674-0
Exploring deep learning capabilities for surge predictions in coastal areas
resolves10.1007/s10346-023-02132-5
Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture
resolves10.3390/s22166240
Capture and Prediction of Rainfall-Induced Landslide Warning Signals Using an Attention-Based Temporal Convolutional Neural Network and Entropy Weight Methods
resolves10.1016/j.jenvman.2024.121295
Forecasting of compound ocean-fluvial floods using machine learning
resolves10.1007/s10346-024-02287-9
Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
resolves10.1002/wat2.1698
Advances and gaps in the science and practice of impact‐based forecasting of droughts
resolves10.1016/j.ijdrr.2023.104123
Explainable artificial intelligence in disaster risk management: Achievements and prospective futures
resolves10.1038/s41561-025-01639-x
Explainability can foster trust in artificial intelligence in geoscience
resolves10.1007/s11069-022-05468-8
Exploring the integration of local and scientific knowledge in early warning systems for disaster risk reduction: a review
resolves10.1007/s11069-020-04429-3
Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices
resolves10.1016/j.compeleceng.2024.109409
A systematic review of trustworthy artificial intelligence applications in natural disasters
resolves10.1016/j.earscirev.2021.103752
Exploring machine learning potential for climate change risk assessment
resolves10.1016/j.isci.2024.110066
A transformative framework reshaping sustainable drought risk management through advanced early warning systems
resolves10.1016/j.ijdrr.2020.101749
An evaluation of availability and adequacy of Multi-Hazard Early Warning Systems in Asian countries: A baseline study
resolves10.1016/j.ijdrr.2020.101642
Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques
resolves10.1016/j.isci.2025.112353
Opportunities and challenges for people-centered multi-hazard early warning systems: Perspectives from the Global South
resolves10.1038/s41598-022-05364-y
Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques
resolves10.1016/j.tws.2023.110749
Hybrid AI-Bayesian-based demand models and fragility estimates for tall buildings against multi-hazard of earthquakes and winds
resolves10.3390/ijgi12060245
Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation
resolves10.1016/j.jrmge.2024.01.003
Data-augmented landslide displacement prediction using generative adversarial network
resolves10.1109/JSTARS.2024.3422503
Auditing Geospatial Datasets for Biases: Using Global Building Datasets for Disaster Risk Management
resolves10.3390/rs12172839
Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment
resolves10.1088/1748-9326/aba4ca
Near-real-time drought impact assessment: a text mining approach on the 2018/19 drought in Germany
resolves10.1061/(ASCE)CF.1943-5509.0001594
Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation
resolves10.1038/s41597-020-0381-2
Generation of a global synthetic tropical cyclone hazard dataset using STORM
resolves10.1038/s41467-025-56906-7
Global gridded population datasets systematically underrepresent rural population
resolves10.1016/j.pdisas.2024.100347
Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions
resolves10.1109/JIOT.2021.3114420
A Deep Learning Model for Earthquake Parameters Observation in IoT System-Based Earthquake Early Warning
resolves10.1016/j.jhydrol.2024.131863
A hybrid surrogate model for real-time coastal urban flood prediction: An application to Macao
resolves10.1007/s10346-024-02350-5
Early warning of landslides based on statistical analysis of landslide motion characteristics and AI Earth Cloud InSAR processing system: a case study of the Zhenxiong landslide in Yunnan Province, China
resolves10.3390/w16142069
A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques
resolves10.1002/eqe.4029
A real‐time seismic damage prediction framework based on machine learning for earthquake early warning
resolves10.1016/j.gsf.2022.101425
Multi-hazard susceptibility mapping based on Convolutional Neural Networks
resolves10.1016/j.coastaleng.2017.10.006
An impact-oriented Early Warning and Bayesian-based Decision Support System for flood risks in Zeebrugge harbour
resolves10.1038/s41612-024-00834-8
Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
resolves10.3390/s22134774
A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems
resolves10.1016/j.ijdrr.2018.03.031
The influence of impact-based severe weather warnings on risk perceptions and intended protective actions
resolves10.1016/j.isci.2025.112977
From multi-hazard early warning systems (MHEWS) to all-vulnerability warning systems (AVWS)
resolves10.1016/j.envsoft.2024.106246
Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management
resolves10.3390/su16062366
Exploring the Intelligent Emergency Management Mode of Rural Natural Disasters in the Era of Digital Technology
resolves10.17645/pag.v8i4.3158
The Changing Face of Accountability in Humanitarianism: Using Artificial Intelligence for Anticipatory Action
resolves10.17645/pag.v8i4.3161
The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance
resolves10.1038/s41467-022-29285-6
Facilitating adoption of AI in natural disaster management through collaboration
resolves10.1162/dint_a_00213
The State of the Art of Natural Language Processing—A Systematic Automated Review of NLP Literature Using NLP Techniques
The 52 references without a DOI — listed, not checked
no DOI — not checkedArtificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature
no DOI — not checked10.1016/j.isci.2025.113689_bib3
no DOI — not checkedArtificial intelligence for geoscience: Progress, challenges, and perspectives
no DOI — not checked10.1016/j.isci.2025.113689_bib6
no DOI — not checkedEarly Warning Systems and Their Role in Disaster Risk Reduction
no DOI — not checkedEarly Warning Systems Defined
no DOI — not checked10.1016/j.isci.2025.113689_bib11
no DOI — not checkedReflections and Future Directions for Multi-Hazard Risk in the Context of the Sendai Framework and Discussions Beyond
no DOI — not checkedArtificial intelligence for disaster risk reduction: Opportunities, challenges, and prospects
no DOI — not checked10.1016/j.isci.2025.113689_bib28
no DOI — not checkedMulti-modal Learning for Geospatial Vegetation Forecasting
no DOI — not checkedChapter 5 - Drought forecasting based on machine learning techniques
no DOI — not checkedCrowd detection and estimation for an earthquake early warning system using deep learning
no DOI — not checkedAn Introduction to Convolutional Neural Networks
no DOI — not checkedToward earthquake early warning: A convolutional neural network for rapid earthquake magnitude estimation
no DOI — not checkedConstraint Free Early Warning System for Flood Using Multivariate LSTM Network
no DOI — not checkedAn AI-Powered, Low-Cost IoT Node Oriented to Flood Early Warning Systems
no DOI — not checkedEarthformer: Exploring Space-Time Transformers for Earth System Forecasting
no DOI — not checkedAttention is all you need for an improved CNN-based flash flood susceptibility modeling. The case of the ungauged Rheraya watershed, Morocco
no DOI — not checkedDynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach
no DOI — not checkedxBD: A Dataset for Assessing Building Damage from Satellite Imagery
no DOI — not checkedOn the Foundations of Earth and Climate Foundation Models
no DOI — not checkedTowards a Trajectory-powered Foundation Model of Mobility
no DOI — not checkedExploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
no DOI — not checkedDetecting Spatiotemporal Dynamics of Western European Heatwaves Using Deep Learning
no DOI — not checkedDecentralized Landslide Disaster Prediction for Imbalanced and Distributed Data
no DOI — not checkedApplication of Artificial Intelligence Technology in Flood Early Warning System
no DOI — not checkedIntegrating IoT and Machine Learning for Enhanced Forest Fire Detection and Temperature Monitoring
no DOI — not checkedDeep Learning Driven Detection of Tsunami Related Internal GravityWaves: A Path Towards open-Ocean Natural Hazards Detection
no DOI — not checkedCollaborative Control Technology and Prospects of Drone Swarms in Earthquake Emergency Rescue Scenarios
no DOI — not checkedReal-Time Earthquake Early Warning With Deep Learning: Application to the 2016 M 6.0 Central Apennines, Italy Earthquake
no DOI — not checkedHybrid Physics–AI Model to Improve Hydrological Forecasts
no DOI — not checkedRapid Prediction of Storm Wave Run-Up Using a Hybrid Physics-Informed Machine Learning
no DOI — not checkedSocial Sensing in Disaster City Digital Twin: Integrated Textual–Visual–Geo Framework for Situational Awareness during Built Environment Disruptions
no DOI — not checkedSmart Cities with Digital Twin Systems for Disaster Management
no DOI — not checkedA global approach for mapping multi-hazard susceptibility using deep learning: A case study in Japan
no DOI — not checked10.1016/j.isci.2025.113689_bib156
no DOI — not checkedLearning from the past in moving to the future: Invest in communication and response to weather early warnings to reduce death and damage
no DOI — not checkedReview article: Stocktaking of methods for assessing dynamic vulnerability in the context of flood hazard research
no DOI — not checked10.1016/j.isci.2025.113689_bib130
no DOI — not checkedTowards Impact-Based Communication During Climate Emergencies
no DOI — not checkedImpact-Based Forecasting: Moving from what weather will be to what it will do for more effective disaster risk management
no DOI — not checked10.1016/j.isci.2025.113689_bib134
no DOI — not checkedFloodWatch: Suggesting an IoT-Driven Flood Monitoring and Early Warning System for the Flood-Prone Cuddalore District in the Indian State of Tamilnadu
no DOI — not checkedAI and IoT Integration for Natural Disaster Management: A Comprehensive Review and Future Directions
no DOI — not checkedArtificial Intelligence Support to the Paradigm Shift from Reactive to Anticipatory Action in Humanitarian Responses
no DOI — not checked10.1016/j.isci.2025.113689_bib139
no DOI — not checked10.1016/j.isci.2025.113689_bib140
no DOI — not checked10.1016/j.isci.2025.113689_bib141
no DOI — not checkedAccelerating Epidemiological Investigation Analysis by Using NLP and Knowledge Reasoning: A Case Study on COVID-19
no DOI — not checkedAI for Smart Disaster Resilience among Communities
no DOI — not checkedVan den Homberg, M., Mouakkid Soltesova, K., Tozier de la Poterie, A., Schiano Lomoriello, R. & Gortzak, I. Bridging the gaps in disaster loss data to support early warning early action. United Nations Office for Disaster Risk Reduction, Geneva.
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a publisher retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-07-14 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.1016/j.isci.2025.113689"><img src="https://citestamp.com/citestamped/10.1016/j.isci.2025.113689/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.1016/j.isci.2025.113689)