Artificial Intelligence-Driven Innovations in Hydrogen Safety

: This review explores recent advancements in hydrogen gas (H 2 ) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems.


Introduction
Hydrogen has been used safely for more than a century in a variety of industries, primarily in oil refineries, ammonia production, fertilizers, metallurgical applications, food industry, and the space program.Despite being nontoxic and lighter than air properties, it is seen hazardous due to historical reasons like the Hindenburg disaster.
However, in the last few decades, in the drive to reduce carbon emissions, hydrogen gas (H 2 ) is potentially considered an important energy carrier for the industry and transport sector.Hydrogen and fuel cells will play a greater role in the energy sector, thus the safety related to the use of H 2 as a fuel is now essential.Safety considerations are a prerequisite to establishing the confidence of stakeholders and the public in accepting H 2 as a replacement for conventional fuels.
Traditional hydrogen production methods are based on fossil fuel processing and electrolysis of water.Hydrogen produced by electrolysis of water powered by renewable energy sources is now considered green H 2 and is preferred compared to steam reforming of fossil fuels.Like other gaseous fuels, the possibility of leakage and subsequent hazards are the main risks through the entire H 2 value chain, i.e., from production, storage to delivery, and end-use.Being a light molecule H 2 has to be stored under high pressure and due to its high diffusivity and permeation properties, the risk of leakage is high.In the event of leaks, it is undetectable to the human senses, and due to rapid diffusion, low ignition energy, and wide flammability range, the risk of leaks may be associated with ignition.Therefore, rigorous safety measures to mitigate these risks are critical.
The global drive toward hydrogen technologies for decarbonization, emphasized by Ocko and Hamburg [1], highlights H 2 's pivotal role in mitigating climate change.However, The global drive toward hydrogen technologies for decarbonization, emphasized b Ocko and Hamburg [1], highlights H2's pivotal role in mitigating climate change.How ever, their study also brings attention to hydrogen's short-term warming effects, challeng ing its perceived neutrality and emphasizing the need for emissions reduction and proac tive safety measures.Recent investments aiming for the largest electrolyzer capacity b 2028 [2] underscore industry focus on decarbonization, demanding immediate safet standard development amidst rapid expansion.
Jahangiri et al. [3] highlighted the economic feasibility of hydrogen production vi renewable energy systems like wind and solar power, with sensitivity analyses emphasiz ing emission penalties' influence on generation costs and the efficiency benefits of fuel cel integration.Fahd Amjad et al. [4] identified sites for large-scale green hydrogen produc tion using solar energy, emphasizing proximity to national networks and water resources with implications for energy storage and transportation sectors.Furthermore, ensurin hydrogen safety in these production and utilization processes remains paramount.
There are international standards for H2 explosion protection IEC 60079 [5] and IEC 80079 [6], as well as specific standards (ISO 22734 [7] and ISO 19880 [8]) for H2 facilities Various sensors are commercially available, along with guidelines for selection installa tion and positioning, depending on the facility and the process.At the planning stage engineers also obtain information on understanding the field of view and blind spots a the facilities for choosing the correct instrumentation, such as layered gas and flame de tection sensors for specific potential hazards.
This paper undertakes a comprehensive exploration of recent advancements in hy drogen safety, particularly the application of AI methodologies in evaluating potentia safety scenarios, and the design of safety measures is considered.Against this backdrop the integration of AI methodologies emerges as a transformative paradigm in augmentin H2 safety measures.The H2 safety issues are leakage detection, explosion risk mitigation material compatibility assessment, optimization of storage systems, infrastructure devel opment planning, dispersion behavior prediction, combustion characteristic analysis public perception management, emergency response planning, and environmental im pact assessment, as shown in Figure 1.Leveraging ANNs, ML algorithms, CV, and data fusion techniques, as shown in Fig ure 2, AI promises to enhance detection capabilities, improve response times, and reduc false alarms across the H2 value chain.These methods facilitate efficient data analysis, pre dictive modeling, risk assessment, and decision support.This review critically evaluate the efficacy of AI-driven approaches in real-time monitoring, hazard detection, and safet protocol optimization, offering insights into key challenges, opportunities, and future di rections in the realm of hydrogen safety.Leveraging ANNs, ML algorithms, CV, and data fusion techniques, as shown in Figure 2, AI promises to enhance detection capabilities, improve response times, and reduce false alarms across the H 2 value chain.These methods facilitate efficient data analysis, predictive modeling, risk assessment, and decision support.This review critically evaluates the efficacy of AI-driven approaches in real-time monitoring, hazard detection, and safety protocol optimization, offering insights into key challenges, opportunities, and future directions in the realm of hydrogen safety.The need for a transition toward a sustainable hydrogen economy, the convergence of AI, and hydrogen safety represents a pivotal step toward realizing this vision.This fosters a safer, more resilient hydrogen ecosystem, paving the way for a cleaner, greener energy future.This work provides an understanding of the current landscape of hydrogen safety and identifies key opportunities and challenges for future research and development efforts in developing and implementing safety procedures in all areas of H2 technology.
This paper is structured into six main sections.Section 1 introduces hydrogen energy and its associated safety concerns.Section 2 provides an in-depth review of state-of-theart technologies in hydrogen safety, covering both conventional methods and advanced AI techniques such as ANN, ML, and CV.Section 3 presents various case studies and applications in hydrogen energy and safety.Section 4 offers a comparative analysis of these techniques for hydrogen safety research.Section 5 delves into safeguarding hydrogen through the integration of advances in materials, models, and storage techniques.Overall, Section 6 provides a discussion and conclusion, summarizing the key findings and outlining future research directions.

State-of-the-Art-Technologies in Hydrogen Safety
This section provides a comprehensive overview of the evolving landscape of hydrogen safety, encompassing both conventional methods and cutting-edge AI techniques.It delves into the traditional approaches like sensor-based detection systems, which have long been employed in hydrogen safety protocols.Additionally, it explores the integration of advanced AI methodologies, including ANNs, ML algorithms, and CV with pattern recognition, which offers the potential for real-time monitoring and proactive risk mitigation.By synthesizing insights from diverse sources, this section offers a holistic understanding of how traditional and innovative techniques converge to enhance safety measures in H2-related applications.Despite promising progress, challenges persist in the field of hydrogen safety, emphasizing the necessity for ongoing research and development efforts.It sets a solid foundation for future research endeavors, providing valuable insights for stakeholders aiming to harness AI techniques for the advancement of H2 leak detection systems, fostering a safer and more resilient energy landscape.The need for a transition toward a sustainable hydrogen economy, the convergence of AI, and hydrogen safety represents a pivotal step toward realizing this vision.This fosters a safer, more resilient hydrogen ecosystem, paving the way for a cleaner, greener energy future.This work provides an understanding of the current landscape of hydrogen safety and identifies key opportunities and challenges for future research and development efforts in developing and implementing safety procedures in all areas of H 2 technology.
This paper is structured into six main sections.Section 1 introduces hydrogen energy and its associated safety concerns.Section 2 provides an in-depth review of state-of-theart technologies in hydrogen safety, covering both conventional methods and advanced AI techniques such as ANN, ML, and CV.Section 3 presents various case studies and applications in hydrogen energy and safety.Section 4 offers a comparative analysis of these techniques for hydrogen safety research.Section 5 delves into safeguarding hydrogen through the integration of advances in materials, models, and storage techniques.Overall, Section 6 provides a discussion and conclusion, summarizing the key findings and outlining future research directions.

State-of-the-Art-Technologies in Hydrogen Safety
This section provides a comprehensive overview of the evolving landscape of hydrogen safety, encompassing both conventional methods and cutting-edge AI techniques.It delves into the traditional approaches like sensor-based detection systems, which have long been employed in hydrogen safety protocols.Additionally, it explores the integration of advanced AI methodologies, including ANNs, ML algorithms, and CV with pattern recognition, which offers the potential for real-time monitoring and proactive risk mitigation.By synthesizing insights from diverse sources, this section offers a holistic understanding of how traditional and innovative techniques converge to enhance safety measures in H 2 -related applications.Despite promising progress, challenges persist in the field of hydrogen safety, emphasizing the necessity for ongoing research and development efforts.It sets a solid foundation for future research endeavors, providing valuable insights for stakeholders aiming to harness AI techniques for the advancement of H 2 leak detection systems, fostering a safer and more resilient energy landscape.
Figure 3 summarizes the distribution of research papers reviewed in this study by their publication year.It highlights the trend and growing interest in AI for hydrogen safety over time.

Conventional Techniques
In the realm of hydrogen safety, traditional experimental methods have long been employed to understand the complexities of H2 leakage scenarios.Zhou et al. [9] conducted experimental research investigating the effects of ignition height on explosion characteristics within a 27 m 3 hydrogen/air cloud.Their study revealed a fascinating interplay of factors, demonstrating how variations in ignition height impact flame propagation velocity, oscillation frequency, and overpressure metrics.Additionally, a novel parameter, "t," was introduced to quantify buoyancy effects, shedding light on the intricate dynamics of gas explosions.This research underscores the significance of understanding ignition height dynamics in mitigating explosion risks, highlighting the importance of experimental approaches in comprehending hydrogen safety phenomena.
To gain a comprehensive understanding of hydrogen safety, it is crucial to explore the broader landscape of H2 energy production and utilization.Tarhan and Çil [10] provided an insightful examination of H2 energy as a sustainable solution amid mounting environmental concerns and energy demands.Their work elucidated various aspects of H2 production, storage, and transportation, emphasizing its potential as a low-carbon energy source.Furthermore, the discussion extended to storage technologies like liquid organic hydrogen carriers (LOHCs) and solid-state systems, showcasing diverse applications in different sectors.By offering a comprehensive overview of H2 energy, this study lays a robust foundation for understanding the critical role of leak detection in ensuring the safety and viability of hydrogen-based technologies.
In the domain of sensor technologies for H2 leak detection, Baker [11] presented a hydrogen leak detection sensor database, offering technical specifications for various sensors, including the AppliedSensor Inc HLS-440.This resource provides essential details such as target gas concentration range, operating temperature, dimensions, and weight, facilitating informed decisions in sensor selection for hydrogen safety applications.Additionally, advancements in sensor technology have led to the development of innovative solutions like the H2 IntelliSense Slim Hydrogen Sensor, employing solid-state electrochemical sensor technology for precise H2 detection [12].With features such as a broad sensing range, rapid response time, and versatile applications across industries, this

Conventional Techniques
In the realm of hydrogen safety, traditional experimental methods have long been employed to understand the complexities of H 2 leakage scenarios.Zhou et al. [9] conducted experimental research investigating the effects of ignition height on explosion characteristics within a 27 m 3 hydrogen/air cloud.Their study revealed a fascinating interplay of factors, demonstrating how variations in ignition height impact flame propagation velocity, oscillation frequency, and overpressure metrics.Additionally, a novel parameter, "t", was introduced to quantify buoyancy effects, shedding light on the intricate dynamics of gas explosions.This research underscores the significance of understanding ignition height dynamics in mitigating explosion risks, highlighting the importance of experimental approaches in comprehending hydrogen safety phenomena.
To gain a comprehensive understanding of hydrogen safety, it is crucial to explore the broader landscape of H 2 energy production and utilization.Tarhan and Çil [10] provided an insightful examination of H 2 energy as a sustainable solution amid mounting environmental concerns and energy demands.Their work elucidated various aspects of H 2 production, storage, and transportation, emphasizing its potential as a low-carbon energy source.Furthermore, the discussion extended to storage technologies like liquid organic hydrogen carriers (LOHCs) and solid-state systems, showcasing diverse applications in different sectors.By offering a comprehensive overview of H 2 energy, this study lays a robust foundation for understanding the critical role of leak detection in ensuring the safety and viability of hydrogen-based technologies.
In the domain of sensor technologies for H 2 leak detection, Baker [11] presented a hydrogen leak detection sensor database, offering technical specifications for various sensors, including the AppliedSensor Inc HLS-440.This resource provides essential details such as target gas concentration range, operating temperature, dimensions, and weight, facilitating informed decisions in sensor selection for hydrogen safety applications.Additionally, advancements in sensor technology have led to the development of innovative solutions like the H 2 IntelliSense Slim Hydrogen Sensor, employing solid-state electrochemical sensor technology for precise H 2 detection [12].With features such as a broad sensing range, rapid response time, and versatile applications across industries, this sensor exemplifies the evolution of sensor technology in addressing hydrogen safety challenges.
Furthermore, addressing the challenging issue of pipeline leakage, Baroudi et al. [13] evaluated state-of-the-art leak detection systems (LDSs) and data fusion approaches to mitigate risks and protect the environment.Their comparative analysis underscored the importance of multiple LDSs for enhanced detection accuracy, emphasizing the need for rigorous data analysis and proper inputs.The study highlighted the role of historical data in predicting pipe deterioration, pointing towards future directions involving advanced technologies like ML and the Internet of Things (IoT) for improved pipeline safety.Additionally, Ramaiyan et al. [14] provided a comprehensive overview of recent developments in hydrogen sensor technology, emphasizing its crucial role in establishing a sustainable hydrogen economy.With a focus on applications such as leak detection and process monitoring throughout the supply chain, this review highlighted the significance of informed sensor selection for the effective implementation of H 2 energy solutions.
Table 1 summarizes relevant research investigating diverse techniques in hydrogen safety, displaying key findings and potential applications.ANNs have emerged as a cornerstone in the realm of hydrogen safety, offering a versatile framework for detecting and mitigating potential risks associated with hydrogen leaks.These sophisticated computational models, inspired by the human brain's neural structure, have been extensively explored and refined to address various challenges in hydrogen safety.Hamdalla [15] delved into the realm of hydrogen detection using long-period fiber Bragg grating (LPFBG) coupled with ANNs, demonstrating their prowess in accurately predicting transmission power based on H 2 concentration.This innovative approach not only highlighted superior fitting to experimental data but also offered advantages in terms of computational time and flexibility, laying a solid foundation for rapid and effective hydrogen detection.
Furthermore, Kopbayev et al. [16] introduced a novel neural network model tailored for early detection and classification of natural gas leaks, highlighting remarkable accuracy without the need for fine-tuning.However, challenges persist, particularly in optimizing these models for shorter datasets and real-world scenarios.Bi et al. [17] proposed a hybrid CEEMDAN-CNN-LSTM model aimed at precise H 2 leakage localization in refueling stations.This cutting-edge approach, combining advanced algorithms with neural networks, yielded exceptional prediction performance, highlighting its potential for ensuring station safety.Zhao et al. [18] ventured into H 2 leak localization using ML techniques, demonstrating promising accuracy in predicting leak locations.Despite these advancements, further research is warranted to enhance accuracy and robustness, especially when applied to complex environments such as full-size garages.Additionally, Suzuki et al. [19] addressed the challenge of H 2 leakage from pipelines using unsupervised ML, highlighting its effectiveness in distinguishing between leakage and nonleakage conditions.Yang et al. [20] introduced a hybrid WD-KNN-CNN model for predicting hydrogen leakage in refueling stations (HRS), achieving 99.14% accuracy for leak location and 97.42% for intensity.Utilizing wavelet denoising and Bayesian optimization, it captures temporal data for fast, reliable predictions, suggesting a need for 14 monitoring points for effective management.
Moreover, He [21] introduced a physics-informed surrogate model for predicting hydrogen leak consequences, leveraging the power of neural networks to offer real-time risk warnings.Lastly, the development of an AI-enabled optical sensor for detecting low levels of H 2 by Swedish and Dutch researchers [22] marks a significant breakthrough, emphasizing the transformative potential of AI in ensuring safety across various sectors.These collective efforts, as shown in Table 2, underscore the pivotal role of ANNs in revolutionizing hydrogen safety, paving the way for a cleaner and safer energy future.

Articles Main Focus Key Findings Applications
Hamdalla [15] H 2 detection using LPFBG and ANNs Superior fitting to experimental data and efficient prediction of transmission power based on hydrogen concentration.

Rapid and effective H 2 detection
Alibek Kopbayev et al. [16] Early detection and classification of gas leaks High accuracy in predicting gas leakage and classifying its size using simulated concentration profiles.
Early detection and classification of natural gas leaks

Detection of low levels of hydrogen for safety applications WD-KNN-CNN Model [20]
Predicting hydrogen leakage location and intensity Achieved high prediction accuracies of 99.14% for leak location and 97.42% for intensity level using wavelet denoising for data preprocessing and Bayesian optimization for hyperparameter optimization.Effectively captures temporal concentration information, enabling millisecond-level predictions, and addresses issues of delayed access to leak source information.
Provides a decision-making basis for on-site personnel to manage leakages in HRS.

Machine Learning Algorithms
ML algorithms emerge as a transformative tool, offering innovative solutions to mitigate risks associated with hydrogen leakage and optimize storage systems.El-Amin's research [23] delves into the complexities of H 2 dispersion prediction by employing ML algorithms on synthetic datasets, highlighting the effectiveness of random forest models in forecasting nanoparticle concentration.Building upon this foundation, El-Amin et al. [24] explore the turbulent flow of hydrogen buoyant jets, integrating ML techniques to predict H 2 concentration in the air.Their findings underscore the pivotal role of ML in understanding and predicting hydrogen leakage scenarios, with the random forest method demonstrating superior performance.
Davoodi et al. [25] extend the application of ML to optimize H 2 storage systems, displaying the efficacy of least squares support vector machines (LSSVM) in predicting hydrogen uptake by porous carbon media.Meanwhile, Shi et al. [26] focus on developing advanced sensing technologies for hydrogen leakage detection, leveraging ML models to estimate H 2 detection response for various nanocomposites.Their study emphasizes the reliability of ML-based mathematical models in automating hydrogen sensing processes.
The importance of ML in enhancing safety measures is further exemplified by research addressing real-world safety concerns.One such study [27] utilizes advanced analytics and ML models to predict physical phenomena during indoor H 2 releases, thereby improving safety protocols.Additionally, El-Amin et al. [28] employ a random forest ML approach to analyze turbulent buoyant jets and accurately predict hydrogen concentration distribution, contributing to safety enhancement efforts.
Furthermore, ML finds practical applications in real-time gas pipeline leak detection systems [29], quantitative risk assessment of liquid H 2 leaks from offshore platforms [30], and prediction of natural gas leakage levels in urban environments [31].These studies underscore the versatility and efficacy of ML in addressing various safety challenges associated with hydrogen and natural gas handling.Overall, the integration of ML techniques represents a paradigm shift in hydrogen safety, offering unparalleled capabilities in predicting, detecting, and mitigating safety risks.
Table 3 provides a comprehensive overview of the ML techniques employed, along with key findings and their respective applications in enhancing safety and risk management in hydrogen-related scenarios.

Computer Vision and Pattern Recognition
Computer vision plays a critical role in enhancing safety measures across various industrial domains, including hydrogen safety.These techniques leverage image analysis and processing algorithms, such as convolutional neural networks (CNNs) and regionbased CNNs (R-CNNs), to interpret visual cues and patterns, enabling the detection of hazardous conditions such as gas leaks in real time.In the realm of hydrogen safety, CV techniques offer innovative solutions for early detection and monitoring, complementing traditional sensor-based approaches.This subsection delves into notable research endeavors harnessing computer vision and pattern recognition to mitigate risks associated with H 2 leaks and enhance safety protocols.
Jadin et al. [32] proposed a novel method utilizing infrared image analysis to detect gas leaks, offering a promising solution for identifying hazardous conditions in industrial environments.Their approach involves image filtering and segmentation to enhance and identify target regions of interest, contributing to the extraction and identification of leaky areas.Softweb Solutions' innovative approach [33] introduces an AI-powered leak detection system, leveraging CV techniques such as bidirectional CNNs, to enable real-time monitoring of pipeline integrity.
Additionally, Zhu et al. [34] addressed the challenges of subsea gas leak monitoring through a CV-based approach, demonstrating the potential for automatic and real-time detection of underwater gas leaks.Their study compares the performance of Faster R-CNN and YOLOv4 models with mathematical details supporting the efficacy of the faster R-CNN model in accurately classifying and locating gas plumes.Furthermore, Nooralishahi et al. [35] presented a drone-enabled gas leak detection technique, integrating video stabilization and optical flow analysis to enhance early detection capabilities.These research endeavors exemplify the diverse applications of computer vision and pattern recognition in augmenting hydrogen safety measures as given in Table 4, clearing the path for safer and more efficient hydrogen-related operations.

Case Studies and Applications
This section delves into practical applications of hydrogen safety technologies, showcasing their significance across diverse environments.These ensure safe hydrogen utilization across various domains such as industry, space, power, and transport sectors.Cutting-edge hydrogen leak detection systems offer rapid, multipoint monitoring and real-time leak data in order to safeguard critical environments.
Breakthroughs in low-cost distributed gas sensors offer cost-effective solutions for industrial settings and wireless sensor networks, spanning from fuel cells to environmental testing.Integrating gas leakage and fire detection into centralized networks enhances safety in buildings.
Advanced models utilizing CNNs enhance long-distance pipeline safety, while collaborative efforts enhance reliability and safety across hydrogen infrastructure.AI-based risk analysis optimizes transportation logistics, ensuring safety throughout the hydrogen value chain.With AI-enabled IoT solutions predicting the consequences of hazardous substance transportation and Arduino-based systems ensuring prompt mitigation of gas leaks, proactive measures are taken to safeguard lives and property in diverse environments.Table 5 gives the practical application details for hydrogen safety technologies.

Comparative Analysis
Table 6 presents a comprehensive analysis across different aspects of hydrogen safety research for leakage issues, providing insights into the research focus, approach, challenges, research gaps, and opportunities for further exploration and development in each area.

Real-world implementations
Demonstration of practical applications, including hydrogen leak detection systems, IoT solutions, and integration with fire detection systems for real-world safety.
-Integration and interoperability challenges when deploying safety systems in complex environments.
-Scalability and cost considerations for widespread deployment.
-User acceptance and adoption of AI-driven safety solutions.
-Development of interoperability standards and protocols for seamless integration of safety systems.
-Cost reduction strategies for making safety technologies more accessible and affordable.
-Deployment of AI-driven safety systems in smart cities and industrial environments for comprehensive risk mitigation.
-Collaboration with regulatory bodies and industry partners to establish safety standards and guidelines.
-Continuous monitoring and evaluation of safety solutions for performance optimization

Safeguarding Hydrogen: Integrating Advances in Materials, Models, and Storage for Enhanced Safety
In the pursuit of hydrogen safety, a multidisciplinary approach is paramount, integrating advancements in materials, models, and storage methods to mitigate potential risks and ensure the safe utilization of this versatile energy carrier.Recent research efforts underscore the significance of comprehensive analyses in understanding hydrogen's role as a future fuel source.
Through meticulous examinations of hydrogen production methods, including the classification of hydrogen by color codes and evaluations based on cost, environmental impact, and technological maturity [49], researchers are delineating pathways toward safer and more sustainable hydrogen utilization.Additionally, the development of digital replicas (DRs) for proton exchange membrane electrolyzers (PEMELs) [50] represents a crucial advancement, enabling the simulation and validation of electrolysis processes, thereby enhancing efficiency and safety.Furthermore, the exploration of advanced materials in hydrogen production, as in [51], illuminates key considerations such as durability, stability, and scalability, essential for ensuring the integration of hydrogen into large-scale systems while mitigating potential hazards.As the hydrogen energy landscape continues to evolve, a proactive approach to safety, supported by innovative research and innovation, remains paramount for realizing its full potential as a sustainable energy solution.

Discussion and Conclusions
In conclusion, this comprehensive review underscores the transformative potential of integrating cutting-edge technologies into hydrogen safety measures for leakage issues.From traditional experimental methods to state-of-the-art AI techniques, the research landscape is vast and evolving, with each approach offering unique insights and solutions to address the complex challenges of hydrogen safety.

Figure 2 .
Figure 2. Digital techniques in hydrogen safety.

Figure 2 .
Figure 2. Digital techniques in hydrogen safety.

Figure 3
Figure 3 summarizes the distribution of research papers reviewed in this study by their publication year.It highlights the trend and growing interest in AI for hydrogen safety over time.

Figure 3 .
Figure 3. Articles considered over the year.

Figure 3 .
Figure 3. Articles considered over the year.

Table 1 .
Overview of Techniques and Applications.

Table 2 .
Overview of Techniques and Applications.

Table 3 .
Various ML Techniques and Applications.

Table 4 .
Various CV Techniques and Applications.

Table 5 .
Real-world applications for hydrogen safety technologies.

Table 6 .
Comprehensive analysis framework for hydrogen research.