Geohazard Risk Assessment: AI Applications, Challenges, and Future Directions

Geohazards, including landslides, earthquakes, tsunamis, and volcanic eruptions, pose serious risks to human life, infrastructure, and the environment. Over the past few decades, geohazard risk assessment has evolved significantly, integrating cutting-edge technologies such as artificial intelligence (AI) and machine learning (ML) to improve prediction accuracy and disaster mitigation strategies.

This article provides an in-depth analysis of geohazard risk assessment, the role of AI in its advancement, the challenges faced in data collection and processing, and future directions for improving risk assessment methodologies.

Essential Elements of Geohazard Risk Assessment: Understanding and Mitigating Geological Threats

Geohazard risk assessment is a critical process that helps identify, evaluate, and mitigate the risks associated with natural geological hazards such as landslides, earthquakes, tsunamis, volcanic eruptions, and floods. By systematically analyzing geological, environmental, and anthropogenic factors, geoscientists and policymakers can predict potential hazards and develop strategies to minimize their impact on communities, infrastructure, and ecosystems. This assessment involves several interrelated components that work together to provide a comprehensive understanding of hazard risks. These components include hazard identification, risk evaluation, impact analysis, and mitigation strategies. Each of these elements plays a crucial role in strengthening disaster resilience, ensuring safer land-use planning, and improving early warning systems. By integrating traditional methods with advanced technologies such as artificial intelligence (AI), remote sensing, and geographic information systems (GIS), geohazard risk assessment has become more precise, scalable, and effective in addressing the increasing challenges posed by natural disasters.

Hazard Identification

The first step in geohazard risk assessment is to recognize and classify potential geological hazards in a given area. This involves collecting data on historical occurrences, geological conditions, climate patterns, and land use.

Commonly identified geohazards include:

• Landslides – Slope instability due to rainfall, seismic activity, or human activities.
• Earthquakes – Ground shaking caused by tectonic movements, often leading to structural failures.
• Tsunamis – Large sea waves triggered by underwater seismic activity, posing a severe coastal threat.
• Volcanic Eruptions – The release of lava, ash, and gases, affecting air quality and land stability.
• Floods – Rapid accumulation of water due to heavy rainfall, dam failures, or sea-level rise.

Risk Evaluation

This stage involves assessing the probability of geohazard occurrence using historical records, environmental monitoring, and predictive models. Factors considered in risk evaluation include:

• Geological and Geomorphological Conditions – Rock formations, soil properties, and tectonic settings.
• Climatic Influences – Seasonal rainfall, temperature variations, and extreme weather patterns.
• Human-Induced Factors – Deforestation, urbanization, and infrastructure development that alter natural landscapes.
• Real-Time Monitoring Data – Seismic activity sensors, satellite imagery, and remote sensing technologies.

Advanced statistical models, geographic information systems (GIS), and artificial intelligence (AI)-driven machine learning (ML) approaches have enhanced the ability to forecast potential geohazard occurrences with greater precision.

Impact Analysis

Understanding the potential consequences of geohazards is essential for preparedness and mitigation planning. Impact analysis examines:

• Human Loss and Casualties – Estimating potential injuries and fatalities in the event of a disaster.
• Infrastructure Damage – Assessing vulnerabilities in transportation, energy networks, and buildings.
• Economic Losses – Evaluating the direct and indirect costs associated with geohazard events.
• Environmental Consequences – Analyzing long-term impacts on ecosystems, water sources, and biodiversity.

By integrating impact analysis with risk evaluation, policymakers and engineers can prioritize high-risk zones and develop targeted mitigation strategies.

Mitigation Strategies

Geohazard risk mitigation involves implementing structural and non-structural measures to reduce the adverse effects of geological hazards. These strategies include:

• Early Warning Systems – Deploying seismic, hydrological, and meteorological monitoring systems to provide timely alerts.
• Infrastructure Reinforcement – Designing resilient structures, such as earthquake-resistant buildings, flood barriers, and landslide stabilization projects.
• Land-Use Planning – Establishing zoning laws that restrict development in high-risk areas.
• Community Preparedness – Conducting public education programs, emergency drills, and evacuation planning.

The integration of advanced AI models has significantly improved the effectiveness of these mitigation strategies by providing real-time hazard predictions and automated decision-making frameworks.

Traditional Approaches vs. AI-Powered Risk Assessment

Geohazard risk assessment has traditionally relied on physical models, historical records, and expert analysis to evaluate the likelihood and impact of geological hazards. These methods, while foundational, often struggle to handle the complexity of geohazard prediction due to the nonlinear relationships between environmental factors, the dynamic nature of geological processes, and the vast amount of data required for accurate assessments. 

Traditional approaches also heavily depend on expert judgment, which can introduce subjectivity and limit scalability. However, with the advent of artificial intelligence (AI) and machine learning (ML), geohazard risk assessment has undergone a significant transformation. AI-powered models can analyze large datasets, identify hidden patterns, and generate more precise predictions in real time. By integrating AI with geospatial analysis, remote sensing, and predictive modeling, researchers and policymakers can improve early warning systems, optimize disaster preparedness, and enhance mitigation strategies. This shift from conventional methodologies to AI-driven solutions represents a major advancement in the field, enabling more efficient, data-driven decision-making for geohazard risk management.

Traditional Geohazard Risk Assessment Approaches

Historically, geohazard risk assessment has relied on conventional methods, including:

• Field Surveys and Geological Mapping – Conducting manual investigations to identify hazard-prone areas.
• Empirical Models and Statistical Analysis – Using historical data to estimate the probability of hazard occurrence.
• Geotechnical and Hydrological Monitoring – Collecting soil stability, groundwater, and weather data to evaluate potential risks.
• Expert Judgments and Scenario-Based Assessments – Consulting specialists to assess and forecast disaster risks.

While these traditional methods have been effective to some extent, they have several limitations:

• Inability to Handle Complex, Non-Linear Relationships – Many geohazards are influenced by a combination of factors, making them difficult to model using conventional statistical techniques.
• Heavy Reliance on Expert Knowledge – The accuracy of assessments depends on the experience and judgment of specialists, introducing potential biases.
• Limited Data Processing Capability – Traditional approaches struggle to process large-scale, high-resolution datasets efficiently.
• Lack of Real-Time Monitoring Integration – Delayed hazard assessments can hinder timely response and mitigation efforts.

AI-Powered Geohazard Risk Assessment

The integration of AI and machine learning has revolutionized geohazard risk assessment by automating data analysis, identifying hidden patterns, and enhancing predictive accuracy. Key benefits of AI-driven geohazard assessments include:

Automated Data Processing

AI models can analyze vast amounts of geospatial, geological, and environmental data more efficiently than human experts. This includes processing remote sensing images, satellite data, and seismic readings in real-time.

Improved Predictive Accuracy

AI-powered models, such as deep learning (DL) and support vector machines (SVM), can detect patterns and relationships in large datasets that traditional statistical methods often miss. This leads to more accurate hazard susceptibility maps and risk assessments.

Real-Time Monitoring and Early Warning Systems

AI enables continuous monitoring of geohazards using sensor networks, drones, and satellite observations. Machine learning models can identify warning signs, such as ground deformations or abnormal seismic activity, and trigger alerts before disasters occur.

Integration with GIS and Remote Sensing Technologies

AI-based approaches enhance the capabilities of GIS by automating geospatial data interpretation. Deep learning models can classify terrain features, detect land-use changes, and assess flood-prone areas with greater precision.

Scenario-Based Risk Simulations

AI-driven simulations allow researchers and policymakers to model multiple disaster scenarios and assess potential outcomes under different environmental and climate conditions. These simulations help in designing better infrastructure and emergency response plans.

Overcoming Human Biases

AI-based systems rely on data-driven decision-making rather than subjective expert opinions. This reduces the risk of biases in hazard assessments and ensures more objective evaluations.

Challenges of AI in Geohazard Risk Assessment

Despite its advantages, AI-powered risk assessment faces several challenges:

• Data Availability and Quality – AI models require large, high-quality datasets, which may not always be accessible.
• Computational Requirements – Machine learning models, especially deep learning, demand significant computational power and resources.
• Model Interpretability – Some AI models function as “black boxes,” making it difficult to understand how they generate predictions.
• Integration with Physical Models – AI alone cannot fully replace traditional geophysical models; a hybrid approach combining AI and domain knowledge is necessary.

Geohazard risk assessment is crucial for mitigating the devastating effects of geological disasters. While traditional methods have laid the foundation for understanding and managing risks, the integration of AI has brought significant improvements in hazard prediction, monitoring, and mitigation. By leveraging AI-powered geospatial analysis, machine learning algorithms, and real-time monitoring technologies, researchers and policymakers can enhance disaster preparedness and response strategies.

Future advancements should focus on addressing AI-related challenges, improving data-sharing frameworks, and integrating AI with physical hazard models. As AI technologies continue to evolve, they will play a pivotal role in enhancing global geohazard risk assessment and resilience-building efforts.

How FlyPix AI Supports Damage Detection and Classification

In geohazard risk assessment, accurately detecting and classifying damage is critical for understanding the impact of natural disasters and planning effective mitigation strategies. Traditional methods rely on field inspections, manual analysis of satellite images, and expert interpretation, which can be time-consuming and inconsistent. FlyPix AI enhances this process by utilizing artificial intelligence to automate damage detection and classification, significantly improving the speed and accuracy of geospatial analysis.

AI-Powered Damage Assessment and Classification

FlyPix AI applies deep learning and computer vision techniques to identify structural damage, terrain deformation, and infrastructure vulnerabilities in high-resolution aerial and satellite imagery. By processing large datasets in real time, the platform can detect and classify various types of damage, such as landslides, flood-induced erosion, and seismic fractures, with greater consistency than manual assessment methods.

Integration with Geospatial Data for Impact Analysis

By combining AI-driven damage detection with geospatial data layers, FlyPix AI provides a comprehensive view of disaster-affected regions. The platform integrates multispectral and hyperspectral imaging, enabling precise analysis of terrain stability, soil moisture variations, and vegetation changes—key indicators of geohazard risk. This allows researchers, policymakers, and emergency responders to assess the extent of damage, prioritize affected areas, and allocate resources effectively.

Real-Time Monitoring for Rapid Response

FlyPix AI enables real-time monitoring of post-disaster conditions, allowing authorities to make informed decisions during emergency response efforts. Through interactive mapping tools and automated alerts, the platform supports early detection of secondary hazards, such as aftershocks, progressive slope failures, and infrastructure collapses. By continuously analyzing updated geospatial data, FlyPix AI helps minimize response delays and enhances disaster resilience planning.

As climate change increases the frequency and intensity of natural disasters, AI-powered platforms like FlyPix AI are becoming essential for damage detection and classification. By automating geospatial analysis and improving risk assessment accuracy, FlyPix AI contributes to more effective disaster preparedness, mitigation, and recovery strategies.

The Role of Artificial Intelligence in Geohazard Risk Assessment

Artificial intelligence (AI) has become an essential tool in geohazard risk assessment, revolutionizing traditional methodologies by enhancing prediction accuracy, automating data processing, and enabling real-time hazard monitoring. The ability of AI to analyze vast and complex datasets has significantly improved the identification and forecasting of geological hazards such as landslides, earthquakes, tsunamis, volcanic eruptions, and floods. Unlike conventional models, which rely on historical records and expert interpretations, AI-driven approaches dynamically adapt to new data, making them more effective in real-world applications.

Key AI Algorithms Used in Geohazard Risk Assessment

Various AI algorithms have been developed and adapted for geohazard risk assessment, each serving a distinct function in analyzing and predicting geological threats. Deep learning (DL) techniques, particularly neural networks, are widely used to model complex relationships in geohazard datasets. By recognizing intricate patterns in seismic activity, soil composition, and hydrological data, DL models enhance the accuracy of landslide susceptibility mapping and earthquake forecasting.

Support vector machines (SVM) are another widely used machine learning (ML) approach that classifies hazard-prone areas based on environmental and geological variables. These models are particularly useful for landslide risk assessments, where they analyze topographical, climatic, and geological features to determine hazard probabilities. Similarly, decision trees (DT) and ensemble learning methods, such as random forests (RF), apply rule-based learning to classify geohazard risks. They are frequently used in combination to improve prediction accuracy by reducing overfitting and handling complex datasets more effectively.

Logistic regression (LR) plays a crucial role in probability-based hazard assessment. It is widely applied in flood and landslide prediction, where it estimates the likelihood of hazardous events occurring based on key influencing factors such as precipitation levels, slope stability, and land use. Extreme learning machines (ELM) provide another alternative, excelling in processing high-dimensional geospatial data at high speeds, making them suitable for real-time hazard detection applications.

Another approach, k-nearest neighbors (KNN), is a non-parametric method that assesses localized hazard risks by comparing new data points to known hazard instances. Though computationally intensive, KNN is particularly useful for small-scale hazard assessments, such as identifying localized landslide-prone zones. Ensemble methods, which combine multiple models, provide a further advantage by integrating the strengths of different algorithms to improve prediction accuracy and generalization while minimizing errors.

Applications of AI in Geohazard Risk Assessment

AI has been successfully implemented in various geohazard scenarios, providing more accurate, scalable, and automated solutions for risk assessment and mitigation. One of the most prominent applications is landslide susceptibility mapping, where AI models analyze factors such as soil composition, rainfall intensity, vegetation cover, and slope gradients to identify areas prone to landslides. Traditional landslide hazard models often fail to capture the nonlinear interactions between these factors, whereas AI models—particularly deep learning and support vector machines—enhance prediction accuracy.

AI is also transforming earthquake detection and forecasting. Deep learning models analyze seismic wave patterns, identifying precursor signals that may indicate an impending earthquake. Unlike conventional seismic monitoring systems, which rely on historical records and physical simulations, AI-powered models process real-time data from seismic stations, allowing for faster and more precise predictions. These advancements have significantly improved early warning systems, reducing response times and enabling authorities to implement mitigation measures more effectively.

Another critical area where AI plays a crucial role is tsunami prediction. AI-driven models analyze underwater seismic activity, oceanographic data, and historical tsunami patterns to forecast potential tsunami threats. Machine learning algorithms help predict the height, speed, and impact of tsunamis, improving coastal evacuation strategies. This real-time analysis capability is particularly valuable for regions prone to sudden and high-impact tsunamis, such as the Pacific Ring of Fire.

AI is also increasingly used in volcanic activity monitoring. By processing multispectral satellite imagery, seismic activity records, and gas emission data, AI models detect early signs of volcanic eruptions. Traditional volcanic monitoring relies on direct measurements and visual observations, which can be challenging in remote or high-risk areas. AI enhances these efforts by continuously analyzing large datasets, identifying changes in temperature, gas concentrations, and seismic activity that may indicate an impending eruption.

In flood risk assessment, AI integrates hydrological models, rainfall data, topographic maps, and satellite imagery to predict flood-prone regions. Conventional flood prediction models often struggle to account for real-time changes in weather patterns, land use, and drainage systems. AI-powered approaches, particularly deep learning and ensemble methods, analyze dynamic datasets to improve the accuracy of flood forecasts, allowing for better preparedness and disaster response.

Advantages of AI in Geohazard Risk Assessment

One of the most significant advantages of AI in geohazard risk assessment is its improved accuracy. AI models detect subtle and nonlinear patterns in complex datasets, outperforming traditional statistical methods in hazard prediction. This enhanced predictive capability allows authorities to take proactive measures before disasters strike, reducing casualties and economic losses.

Another key benefit is automation. AI-driven models reduce the need for manual data processing, allowing for faster analysis of large-scale geospatial datasets. This automation enables real-time risk assessments, which are essential for early warning systems and emergency preparedness.

AI also provides scalability, making it suitable for analyzing data at various spatial scales, from local hazard assessments to regional and global risk evaluations. With advancements in remote sensing, satellite imaging, and cloud computing, AI can process vast amounts of geospatial data with high efficiency.

Furthermore, AI facilitates real-time analysis, which is particularly beneficial for monitoring geohazards that require immediate response, such as earthquakes, tsunamis, and flash floods. AI-powered early warning systems can analyze seismic signals, atmospheric conditions, and water levels within seconds, providing timely alerts to communities and disaster response teams.

Challenges in AI-Based Geohazard Risk Assessment

Despite its advantages, the application of AI in geohazard risk assessment faces several challenges. One of the primary issues is data availability. High-quality training datasets are crucial for AI models, yet comprehensive and standardized geohazard datasets are often lacking. Many regions lack extensive monitoring networks, making it difficult to obtain reliable input data for AI algorithms.

Another challenge is computational requirements. AI models, particularly deep learning networks, require substantial computing power and memory. The need for high-performance hardware, cloud computing resources, and energy-intensive training processes can be a barrier to widespread AI adoption, especially in developing countries with limited technological infrastructure.

AI models also suffer from interpretability issues. Many advanced machine learning techniques, such as deep learning, function as “black box” models, meaning that their internal decision-making processes are difficult to understand and explain. This lack of transparency can make it challenging for scientists and policymakers to fully trust AI-generated predictions. Developing explainable AI (XAI) techniques is crucial to improving model transparency and gaining broader acceptance in geohazard applications.

Moreover, integration with physical models remains a significant limitation. AI models primarily rely on data-driven approaches, which may not always capture the underlying physical processes governing geohazards. Traditional physics-based models provide valuable insights into the mechanics of geological phenomena, but they often lack the ability to learn from real-time data. The future of geohazard risk assessment lies in the hybridization of AI with physics-based models, creating more robust and reliable prediction frameworks.

Global Research Trends in AI-Based Geohazard Risk Assessment

The application of artificial intelligence (AI) in geohazard risk assessment has gained significant traction over the past two decades, leading to an exponential increase in research output. AI-powered approaches have enhanced the accuracy, efficiency, and scalability of geohazard predictions, prompting widespread adoption in fields such as landslide susceptibility mapping, earthquake forecasting, flood risk analysis, and volcanic activity monitoring. A scientometric analysis of AI-based geohazard research reveals important trends in publication activity, key contributors, influential institutions, and emerging research hotspots.

Publication Trends

The volume of research on AI applications in geohazard risk assessment has grown dramatically, particularly since the early 2000s. This surge is attributed to advancements in machine learning (ML), deep learning (DL), and the increased availability of high-resolution geospatial datasets. China, the United States, and Italy are among the leading nations in AI-based geohazard research, contributing the highest number of publications and citations in the field.

• China has emerged as the most prolific country in AI-driven geohazard research, particularly in landslide susceptibility modeling, seismic hazard assessment, and flood prediction. The country’s investment in AI technologies, combined with its vulnerability to various geohazards, has led to significant research output.
• The United States follows closely, with a strong emphasis on earthquake detection and tsunami forecasting using AI-powered techniques. Research institutions such as the U.S. Geological Survey and the University of California, Berkeley have played a major role in developing AI-driven hazard monitoring systems.
• Italy has also made substantial contributions, particularly in integrating Geographic Information Systems (GIS) with AI for geospatial analysis of geohazard risks. The country’s research has focused on earthquake hazard assessments and climate-induced landslide susceptibility analysis.

A key characteristic of AI-based geohazard research is its interdisciplinary nature. Scientists from geophysics, remote sensing, data science, and engineering disciplines are collaborating to improve predictive models and risk mitigation strategies. The most cited research papers in the field primarily focus on landslide prediction, AI-driven seismic monitoring, and geospatial AI applications for hazard assessment.

Leading Researchers and Institutions

The rapid expansion of AI-based geohazard risk assessment has been driven by contributions from leading researchers and academic institutions. Some of the most influential figures in the field have developed novel AI methodologies, improved predictive modeling techniques, and facilitated the integration of AI with traditional geohazard assessment frameworks.

Notable Researchers in AI-Based Geohazard Risk Assessment

1. Biswajeet Pradhan (University of Technology Sydney, Australia) – A highly cited researcher specializing in landslide susceptibility mapping and geospatial AI applications. His work focuses on integrating ML algorithms such as Decision Trees, Support Vector Machines, and Deep Learning into geohazard assessments.
2. Dieu Tien Bui (University of South-Eastern Norway, Norway) – Known for his contributions to AI-based landslide hazard modeling, flood risk mapping, and earthquake forecasting. He has worked extensively with ensemble ML models and GIS-based hazard assessments.
3. Hamid Reza Pourghasemi (Shiraz University, Iran) – Recognized for his research on AI-driven geohazard prediction, particularly in evaluating landslide, flood, and earthquake risks. His work has contributed to the development of hybrid AI models combining machine learning with geospatial analysis.

Top Research Institutions Advancing AI-Based Geohazard Studies

Several institutions have established themselves as global leaders in AI-driven geohazard research. Their contributions range from theoretical advancements in AI models to practical applications for disaster risk reduction.

1. Chinese Academy of Sciences (China) – The largest contributor to AI-based geohazard research, focusing on seismic hazard prediction, remote sensing applications, and climate-induced geohazard assessments.
2. University of California, Berkeley (United States) – A key player in earthquake risk assessment, utilizing AI for real-time seismic event detection and structural vulnerability analysis.
3. U.S. Geological Survey (United States) – A government-led institution at the forefront of AI-powered hazard monitoring, with research spanning landslides, earthquakes, and flood prediction.

These institutions have pioneered AI-driven methodologies that improve the accuracy of hazard forecasts and enhance disaster preparedness measures.

Hot Research Topics

Scientometric analysis has identified several emerging research clusters in AI-based geohazard risk assessment. These topics represent the most active areas of study and highlight the evolving role of AI in hazard prediction and mitigation.

1. Deep Learning (DL) for Landslide Prediction

Deep Learning has become a dominant approach in landslide susceptibility mapping due to its ability to capture complex spatial relationships and nonlinear interactions between terrain, climatic, and geological factors. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are widely used for landslide prediction, offering improved accuracy compared to traditional statistical models.

2. Geographic Information Systems (GIS) Integration with AI

The combination of AI and GIS has led to advanced geospatial modeling techniques for hazard assessment. Machine learning algorithms applied to GIS-based geohazard mapping have improved the spatial prediction of risk zones. GIS-integrated AI models are used in earthquake risk assessment, floodplain mapping, and volcanic hazard monitoring.

3. Seismic Hazard Analysis Using AI Models

AI-driven seismic risk assessment models have enhanced earthquake forecasting capabilities. By analyzing vast amounts of seismic wave data, AI algorithms can identify patterns indicative of foreshocks, mainshocks, and aftershocks. Machine learning models such as Support Vector Machines, Decision Trees, and Long Short-Term Memory (LSTM) networks have been successfully applied in seismic event classification.

4. Impact Assessments of Climate Change on Geohazards

With climate change altering precipitation patterns, sea levels, and geological processes, researchers are increasingly using AI to model the impacts of climate change on geohazard risks. AI-driven climate models integrate temperature trends, rainfall variability, and soil moisture data to predict shifts in hazard susceptibility over time. These assessments are crucial for developing adaptive risk mitigation strategies.

Future Directions in AI-Based Geohazard Research

While AI has already transformed geohazard risk assessment, there are still challenges and opportunities for future research. Key areas for continued exploration include:

• Development of Explainable AI (XAI) – To increase trust in AI-driven risk assessments, researchers are working on making AI models more interpretable and transparent.
• Integration of AI with Physics-Based Models – Hybrid models that combine AI with geophysical simulations can improve hazard predictions by incorporating both data-driven insights and fundamental geoscience principles.
• Real-Time AI for Early Warning Systems – The expansion of real-time AI-powered early warning systems for earthquakes, tsunamis, and landslides is a critical focus area, especially for high-risk regions.
• AI for Multi-Hazard Assessment – Future research aims to develop AI models that assess multiple hazards simultaneously, considering their interdependencies and cascading effects.

AI-based geohazard risk assessment has experienced rapid growth, driven by advances in machine learning, geospatial technologies, and the increasing need for accurate hazard predictions. Leading researchers and institutions have made significant contributions to AI-driven geohazard research, particularly in landslide prediction, seismic hazard analysis, and climate change impact assessments. Emerging research topics continue to shape the field, with deep learning, GIS integration, and real-time hazard monitoring taking center stage. As AI technologies evolve, future research will focus on improving model interpretability, integrating physics-based approaches, and expanding real-time early warning capabilities, ultimately strengthening disaster resilience worldwide.

Conclusion

Geohazards pose a significant threat to human life, infrastructure, and the environment. Over the past decades, geohazard risk assessment has evolved with the integration of artificial intelligence (AI) and machine learning (ML), enabling more precise predictions and improved disaster mitigation strategies. AI has proven its ability to analyze complex datasets, uncover hidden patterns, and provide accurate forecasts that traditional methods struggle to achieve.

However, challenges remain, including limited access to high-quality data, computational demands, and the interpretability of AI models. Future advancements in this field should focus on developing standardized benchmark databases, integrating AI with physical models, automating model selection (AutoML), and improving AI transparency through explainable AI (XAI). Addressing these challenges will enhance the reliability of AI-driven geohazard assessments, leading to better disaster preparedness and risk mitigation.

FAQ