Natural disasters are becoming more frequent, more intense, and more costly. In 2023 alone, 399 disaster events linked to natural hazards claimed over 86,000 lives, affected 93.1 million people, and caused an estimated US$202.7 billion in economic losses.
With climate change accelerating weather-related events such as floods, droughts, tropical storms, and wildfires, global disaster frequency is projected to rise by 37% annually by 2025 compared to previous decades. The economic toll is escalating too, with insured losses expected to reach US$145 billion in 2025 and spiking even higher in catastrophic years.
Asia remains the most disaster-prone region globally. In recent years, South and Southeast Asia have endured extreme events, like deadly heatwaves in India and Pakistan, and large-scale floods and tropical storms in the Philippines and Indonesia.
Meanwhile, Malaysia is no exception to this regional trend. In Sarawak, the monsoon season of late 2024 into early 2025 brought prolonged heavy rainfall, resulting in severe flooding and landslides. Hard-hit areas like Miri and Bintulu saw mass evacuations, extensive property damage, and multiple fatalities.
As the scale and complexity of disasters grow, traditional early warning and emergency response systems face increasing strain. Weather patterns are becoming harder to predict, populations are expanding into risk-prone areas, and the window for effective action is shrinking.
This urgent landscape has catalysed the development and global uptake of AI-based disaster warning systems, advanced technologies capable of detecting, analysing, and responding to hazards with unmatched speed and precision.
From forecasting cyclone paths and flood surges to guiding real-time evacuations and resource deployment, AI-driven systems offer a transformative leap in disaster preparedness and resilience. In a world where seconds can save lives, AI is increasingly becoming a necessity for regions like Sarawak that must navigate both urban development and environmental vulnerability.
What Are AI-Based Disaster Warning Systems?
AI-based disaster warning systems are advanced technological platforms that use artificial intelligence to predict, detect, and communicate the risk of natural hazards.
Unlike traditional systems, which often rely on static forecasts and delayed alerts, AI systems harness large datasets, real-time monitoring, and predictive analytics to generate more accurate, timely, and actionable warnings.
These tools are designed to anticipate a wide range of disasters with the ultimate goal of reducing loss of life, minimising property damage, and strengthening community resilience.
At their core, these systems combine several key components:
1. Data Collection and Monitoring
AI systems continuously gather and process data from satellites, weather stations, ground sensors, drones, and historical records. This allows them to monitor environmental changes (e.g., rainfall, seismic activity, temperature, or soil movement) and detect early signs of potential threats.
2. Predictive Analytics
Using machine learning algorithms, these systems analyse patterns in the data to forecast when, where, and how a disaster may occur. For instance, AI can forecast floods by analysing rainfall trends, river levels, and land use data days or even weeks in advance.
3. Multi-Hazard Risk Assessment
By integrating data across different disaster types, AI can evaluate regional risk profiles and generate priority maps. This helps authorities focus preparedness efforts and allocate resources to the most vulnerable areas.
4. Personalised Alerts and Recommendations
Modern AI warning platforms use large language models (LLMs) to provide context-specific advice, tailored to local infrastructure, demographics, and environmental risks. This ensures that different communities (whether urban or rural) receive warnings that are relevant and actionable.
5. Efficient Communication
AI-based systems are designed to communicate warnings through multiple channels, including SMS, mobile apps, social media, and public broadcasts. This multi-platform approach ensures messages reach diverse populations quickly and clearly.
Global and Malaysian Advances in AI-Based Disaster Warning Systems
Across the world, countries are turning to artificial intelligence to improve how they predict and respond to disasters. From floods and cyclones to wildfires and typhoons, AI-based systems are helping nations act faster, coordinate better, and save more lives. The diversity of technologies and applications reflects the flexibility of AI — and offers important lessons for regions like Sarawak.
Global Innovations
The Ciseres Project deploys AI-powered micro-satellites to transmit critical disaster data directly to first responders in real time. By filtering out non-essential information before transmission, these satellites overcome communication delays typical of traditional systems, making them highly effective during rapidly evolving emergencies.
2. India (Cyclone Prediction with Google DeepMind)
In 2024, India’s National Disaster Management Authority partnered with Google DeepMind to develop an AI model that predicted cyclone trajectories and intensities up to 72 hours in advance. During Cyclone Megha, the system enabled the evacuation of over 500,000 people, drastically reducing casualties and setting a new benchmark for regional coordination and risk communication.
Bangladesh’s “FloodGuard” system, co-developed with MIT and local startups, uses AI to combine rainfall data, river flow sensors, and drone imagery to predict flood zones with 90% accuracy. In 2025, the system’s early alerts contributed to a 45% drop in flood-related deaths and saved an estimated US$120 million in economic losses during the monsoon season.
4. United States (Wildfire Management in California)
AI platforms such as “FireMind” and “FireSentinel” are transforming California’s wildfire response. These systems integrate satellite heat detection, real-time drone footage, and climate modelling to anticipate wildfire spread. In 2025, FireMind enabled early evacuation orders for 30,000 residents, while FireSentinel’s mapping system helped contain fires 60% faster than traditional methods — with zero aerial suppression casualties reported.
5. Taiwan (Typhoon Forecasting)
Taiwan’s Central Weather Administration has enhanced its typhoon forecasting models by incorporating AI. During Tropical Storm Bebinca in 2024, AI increased the accuracy of three-day trajectory forecasts by 20%, allowing emergency agencies to pre-position aid and coordinate timely evacuations.
6. Vietnam (AI for Storm and Rainfall Prediction)
Vietnam’s National Centre for Hydro-Meteorological Forecasting has integrated AI into its storm and rainfall prediction efforts. The models improve speed and accuracy in delivering storm warnings, giving coastal and inland communities more time to prepare.
Malaysia and Sarawak: Stepping into the AI Era
Malaysia has begun adopting AI technologies to tackle its persistent flood risks, particularly during the monsoon season. While fully operational AI-based disaster warning systems are still in development, the country has made important strides in recent years.
National Initiatives and Collaborations:
- Huawei–UniKL Partnership: In 2024, Universiti Kuala Lumpur signed an MoU with Huawei to develop a national disaster management system powered by AI to deliver early flood and storm warnings.
- Smart Flood Mitigation Plans: The National Disaster Management Agency (NADMA) is integrating AI with weather and hydrological data to improve flood forecasts and evacuation strategies, especially in high-risk regions.
- MCMC’s Notification System: The Malaysian Communications and Multimedia Commission is finalising a next-generation disaster notification platform. Unlike traditional SMS alerts, this system uses geo-location and app-based technologies to deliver real-time, localised evacuation instructions.
- Proposals for Urban-Rural Expansion: The Malaysian IoT Association has proposed phased AI flood detection rollouts starting in urban flood hotspots and expanding into rural areas. The aim is to continuously enhance model accuracy while tailoring system design to local conditions.
In Sarawak, AI deployments are not yet well-documented. However, the region stands to benefit significantly from these national advances, given its vulnerability to monsoon flooding, especially in regions like Bintulu, Miri, and Kuching.
The integration of AI-driven early warning systems can enhance emergency preparedness, accelerate evacuation coordination, and reduce disaster-related losses. The groundwork laid by federal agencies, academic institutions, and tech partners offers a clear pathway for Sarawak to localise and scale these technologies.
Opportunities and Relevance for Sarawak
For Sarawak, where annual monsoon seasons routinely trigger floods and landslides, the potential of AI-based disaster warning systems is deeply practical. The state’s unique geography, dispersed population, and climate vulnerability make it an ideal candidate for AI-enhanced disaster preparedness and resilience.
1. Tackling Floods and Landslides with Timely Alerts
Sarawak regularly faces monsoon-induced flooding and rain-triggered landslides, particularly in low-lying coastal areas and hilly interiors. These events often lead to mass evacuations, infrastructure damage, and tragic loss of life.
AI-based warning systems can monitor rainfall intensity, river levels, and soil conditions in real time to generate earlier, more precise alerts. This critical lead time can empower residents and responders alike to act before disaster strikes, reducing harm and improving outcomes.
2. Smarter Preparedness and Emergency Coordination
AI tools offer a new level of insight and foresight for disaster management authorities. Through predictive analytics, real-time situation mapping, and automated resource planning, AI can improve coordination between agencies such as the Sarawak Disaster Management Committee (SDMC), police, fire and rescue, and civil defence units. The result: faster response times, clearer decision-making, and more efficient deployment of rescue and relief operations.
3. Localised Intelligence from Local Data
Sarawak’s diverse terrain demands disaster models that are tailored, not generic. AI can integrate data from local weather stations, IoT sensors, satellite feeds, and even community-level reports to create dynamic, localised risk assessments. These models can be fine-tuned over time to reflect Sarawak’s specific hydrological and geological patterns, significantly enhancing forecast precision.
4. Reaching Remote and Vulnerable Communities
In Sarawak, many rural and indigenous communities are located in areas with limited access to conventional media or infrastructure. AI-enabled platforms can deliver warnings through SMS, radio, or mobile apps even in remote areas and in multiple languages. This ensures that disaster alerts are inclusive, accessible, and culturally relevant, helping to narrow the digital preparedness gap.
5. Building Resilience for Sustainable Development
By identifying hazard-prone zones and informing land use decisions, AI-powered risk assessments can support smarter infrastructure planning and climate-adaptive development. From safer road construction to the placement of schools and clinics, AI insights can guide Sarawak toward a more disaster-resilient future, which is essential for sustainable progress amid growing environmental risks.
6. Protecting the Economy by Reducing Losses
Natural disasters have a tangible economic impact, disrupting agriculture, damaging roads, and straining government budgets. By enabling proactive responses and minimising damage, AI-based warning systems can help protect critical sectors such as farming, transport, and tourism. Early action reduces recovery costs and preserves livelihoods, making AI not just a safety investment but a strategic economic one.
7. Aligning with National and Regional Initiatives
Sarawak stands to benefit from Malaysia’s broader push for AI-enabled disaster systems. National efforts, including the Huawei-UniKL partnership and the upcoming geo-located alert system by the Malaysian Communications and Multimedia Commission (MCMC) provide a ready-made framework for Sarawak to plug into. Integration at this level encourages cross-border data sharing, resource coordination, and rapid technology transfer.
Expert Insights: Tailoring AI Systems for Sarawak’s Disaster Landscape
While AI offers transformative potential for disaster warning systems, its success depends heavily on how well models are matched to local conditions. In Sarawak, not all AI approaches are equally suitable due to its complex topography and varied climate zones.
Fortunately, recent studies from Malaysian researchers and international disaster modelling experts offer clear guidance on effective tools for the region.
1. Decision Trees (DT) Enhanced with SMOTE
Among the most promising approaches for flood prediction in Malaysia is the Decision Tree (DT) model, particularly when combined with the SMOTE (Synthetic Minority Over-sampling Technique) method. This combination addresses class imbalance in flood datasets, a common issue where non-flood events vastly outnumber flood events in historical records.
In a benchmark study using Kelantan flood data, DT with SMOTE achieved an impressive 99.92% accuracy, outperforming many conventional machine learning models. Its advantages include ease of interpretation, ability to handle both categorical (e.g., land type) and numerical (e.g., rainfall, river height) variables, and suitability for government use cases that require clear, explainable decision logic.
2. Bayesian Networks (BN) for Uncertain Environments
Bayesian Networks are probabilistic models that represent relationships between multiple variables, making them ideal for environments where data may be incomplete, noisy, or uncertain; much like Sarawak’s diverse terrain and scattered population centres.
BNs are especially useful when real-time data streams are limited or inconsistent, allowing disaster managers to still infer flood risks based on known correlations between variables like rainfall, terrain slope, and previous flood history.
3. k-Nearest Neighbours (kNN) and Support Vector Machines (SVM)
Both kNN and SVM have shown solid performance in Malaysian flood modelling efforts. kNN is straightforward and effective for smaller, well-labelled datasets, making it useful in district-level or community-focused applications.
SVM, on the other hand, excels in high-dimensional, complex datasets, such as those incorporating weather patterns, satellite layers, and sensor feeds.
As with DTs, their accuracy improves when paired with balancing techniques like SMOTE, which helps address underrepresented flood cases in historical data.
4. Deep Learning and Spatial Mapping with CNNs
For tasks like flood extent mapping and real-time spatial analysis, deep learning models like the Convolutional Neural Networks (CNNs) are emerging as the go-to choice. CNNs are particularly adept at processing satellite imagery and environmental sensor grids, enabling highly detailed, visual predictions of flood progression.
In flood-prone areas of Sarawak, especially along coastal and riverine belts, CNNs can be used to create near-real-time flood maps that inform evacuation routes, resource deployment, and community alerts.
5. Emerging Models: Graph Neural Networks (GNNs) and Physics-Based AI
More advanced techniques such as Graph Neural Networks (GNNs) and physics-informed neural networks are beginning to show promise in generalising across diverse flood scenarios. These models can incorporate both data-driven learning and physical laws (e.g. water flow dynamics), offering greater robustness and adaptability to new or evolving flood conditions in Sarawak.
These models could be particularly valuable for areas where conventional hydrological models struggle to keep up with shifting climate impacts or land use changes.
6. Ensemble Methods for Robust Prediction
Rather than relying on a single model, ensemble methods combine multiple algorithms (e.g., DT + SVM + kNN) to capture a broader range of data patterns and improve predictive reliability.
For Sarawak, where different areas may have different environmental characteristics, ensemble systems offer the flexibility to deliver consistently strong performance across varied scenarios.
Conclusion: Toward a Smarter, Safer Sarawak
As natural disasters grow in frequency, scale, and unpredictability, the need for faster, smarter, and more inclusive response systems has never been clearer. AI-based disaster warning systems offer both a technological upgrade and a paradigm shift from reactive crisis response to proactive, data-driven resilience.
For Sarawak, the stakes are high. Seasonal floods, landslides, and shifting climate patterns continue to threaten lives, livelihoods, and long-term development. But the opportunity is equally significant. With AI, the state has the chance to enhance early warning capabilities, strengthen emergency coordination, and reach vulnerable communities in ways previously unachievable.
The path forward lies in strategic adoption. By combining proven models like Decision Trees and CNNs with localised data and community insight, Sarawak could build an AI ecosystem tailored to its geography, infrastructure, and people. Integration with national platforms and a willingness to pilot, refine, and scale solutions will be key to maximising impact.
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