Integrating digital elevation models (DEMs) into environmental hazard mapping represents a pivotal development in geospatial sciences [1–4]. DEMs are essential tools for accurately assessing and managing environmental hazards such as floods [5,6], landslides [7–9], and coastal erosion [10–13]. They are widely used in environmental-related studies, particularly in hydrological and hydraulic models, where the high resolution of these models significantly affects the accuracy of hazard assessments [13–16]. Their importance is underscored by the increased frequency of these hazards, driven by climate change and urban expansion. DEMs are fundamental in determining landscapes and analyzing topography and are instrumental in enhancing the precision and utility of hazard maps, which are vital tools in disaster risk reduction [17–20]. DEMs provide fundamental elevation data that enable researchers to identify regions susceptible to environmental hazards by determining watershed boundaries, identifying flood plains, highlighting seismic affected areas, and recognizing steep slopes that can predispose areas to landslides [21–23]. Recent studies have emphasized the role of DEM resolution in the performance of machine learning models for predicting flood probabilities, indicating that finer resolutions can substantially improve prediction accuracy [24–27]. In addition, the technological advancements in DEMs have expanded their application in environmental management [28–30]. This includes the fusion of multiple DEM sources to enhance model accuracy, which has proven essential in refining the precision of hazard maps crucial for disaster risk planning [31,32]. In addition, integrating DEMs with geographic information systems (GIS) has revolutionized environmental planning and hazard management, allowing for more detailed and accurate mapping and significantly enhancing disaster preparedness and response strategies [9,33–35]. The use of LiDAR-derived DEMs has been highlighted for its effectiveness in flood applications, providing detailed and accurate topographical data that improve flood risk assessments [36,37]. Integrating UAV-derived DEMs demonstrates substantial potential, particularly in small-scale hazard mapping and monitoring geo-hazards in challenging terrains [38,39].

Although the utility of DEMs in hazard mapping is well-established, existing literature reveals several current problems. One significant issue is the gap in the simultaneous application across various types of environmental hazards. Most studies focus on a single type of hazard, leading to deficiency in comprehensive risk assessments that integrate high-resolution DEMs with other geospatial technologies across multiple hazard types. This lack of integration limits the ability to provide a holistic view of environmental risks, which is crucial for effective disaster preparedness. In addition, there is a gap in the application of machine learning techniques in enhancing DEM-based environmental hazard mapping. Machine learning methods can significantly improve the predictive accuracy of hazard maps by learning complex patterns from large and diverse datasets that traditional methods cannot capture. These techniques can refine the resolution and accuracy of DEMs, optimize hazard prediction models, and facilitate the development of more robust and comprehensive hazard assessment tools [40–42].

The potential for machine learning in this context is vast, as it can analyze the interactions between multiple hazard types and their environmental impacts more efficiently than conventional statistical methods. For instance, Su et al. [43] demonstrated the application of deep learning for assessing earthquake disaster chains, suggesting that similar methodologies can enhance multi-hazard models by integrating various data types and sources, including high-resolution DEMs. In addition, the integration of DEMs with other geospatial technologies, such as remote sensing and GIS, has been shown to effectively map hazards such as floods, landslides, and soil erosion, but the simultaneous use of these technologies across multiple hazard scenarios remains underexplored [44–47]. These gaps present significant opportunities for future research, where the development of integrated, multi-hazard assessment models can significantly increase the accuracy and efficiency of hazard maps, thus improving disaster risk management strategies. This study aims to review and critically assess the application of DEMs in mapping and managing specific environmental hazards, focusing on floods, landslides, and coastal erosion. The study seeks to enhance the accuracy and utility of hazard maps by employing high-resolution DEMs integrated with advanced GIS and innovative machine learning techniques. These improved maps are expected to provide more detailed and precise hazard assessments, crucial for effective disaster risk reduction and strategic environmental planning. In addition, the study aims to develop a robust framework that utilizes the combined capabilities of high-resolution DEMs, GIS, and machine learning to create a multi-hazard assessment tool, identifying potential hazard zones with greater precision and predicting the severity and probable impact of these hazards under various environmental scenarios. This approach intends to fill existing gaps in current hazard mapping practices, which often do not simultaneously consider multiple types of hazards or fully utilize the potential of modern technological advancements. The findings offer valuable insights into optimizing DEM technology for environmental management, contributing to safer and more resilient communities. This research addresses an important gap in the geospatial analysis of natural hazards and serves as a foundational reference for future advancements in the field, emphasizing its importance to academic researchers and practical stakeholders in environmental and disaster management.

A bibliometric assessment was conducted to evaluate the existing literature on the application of DEMs in environmental hazard mapping. A search was performed on the Web of Science platform, yielding a total of 2110 documents relevant to the study. The temporal distribution of publications from 2000 to 2023 is illustrated in Fig. 1, showing a steady increase in research activity over the years, with growth after 2010. The analysis of keyword co-occurrence within these publications reveals the most frequently used terms, including “hazard,” “GIS,” “model,” “LIDAR,” “remote sensing,” “uncertainty,” “risk,” “earthquake,” “vulnerability,” “climate change,” “deformation,” “landslide,” “evolution,” “susceptibility,” and “logistic regression,” as depicted in Fig. 2. These keywords highlight the multidisciplinary nature of the research and the integration of various technologies and methodologies in the field.

Additionally, the assessment identified the journals that published the most research on DEM applications in environmental hazard mapping. The prominent journals include Natural Hazards and Earth System Sciences, Natural Hazards, Geomorphology, Remote Sensing, Landslides, Environmental Earth Sciences, Geomatics Natural Hazards and Risk, and ISPRS International Journal of Geo-Information, as shown in Fig. 3. These journals are pivotal in disseminating advancements and findings related to DEM applications in hazard mapping. Citation analysis by country, as presented in Fig. 4, indicates that China, the USA, Italy, Germany, England, France, India, and Switzerland are the leading contributors to this research domain. This geographical distribution underscores the global interest and collaboration in utilizing DEMs for environmental hazard assessment and management. The bibliometric assessment demonstrates the evolving and expanding body of knowledge in the application of DEMs for environmental hazard mapping, emphasizing the importance of interdisciplinary approaches and international collaboration in advancing this critical field.

This section outlines the materials and methods employed in the study to evaluate the effectiveness of DEMs in environmental hazard mapping. The methodology is designed to explore the integration of high-resolution DEMs with advanced GIS and machine learning techniques to enhance the accuracy and utility of hazard maps. Fig. 5a illustrates the integration process of DEMs, GIS, and machine learning in creating a comprehensive multi-hazard assessment tool. The framework is divided into several components, each representing a step in the process from data acquisition to hazard mitigation and planning. However, Fig. 5b describes the general approach applied in this study. This includes a step-by-step depiction of the operational workflow, which helps in understanding the systematic application of integrated technologies and methodologies to address the complexities of environmental hazard mapping and provides a clear roadmap for achieving the research objectives.

This section presents a critical analysis to explore the applications, benefits, challenges, and practical implications of using DEMs in environmental hazard mapping, highlighting critical areas for improvement and future research. The case studies highlighted numerous applications of DEMs across different environmental hazards [21,63]. Each case study demonstrated the specific capabilities of DEMs in hazard mapping and revealed the improvements in hazard preparedness enabled by technological advancements. The effectiveness of DEMs can vary significantly based on the resolution, quality, and the specific environmental hazard being assessed [75]. Although high-resolution DEMs offer greater detail and accuracy, they require substantial computational resources and are more expensive. Lower-resolution DEMs, while less costly and computationally demanding, cannot provide sufficient detail for all types of hazard assessments, particularly in complex urban or densely vegetated areas. Table 4 categorizes strategic measures to enhance the application of DEMs in environmental hazard mapping. Each category addresses specific aspects, from technical challenges and operational needs to capacity building and policy support, ensuring a comprehensive strategy for implementing DEM technology in hazard assessments.

The critical role of DEMs in environmental hazard mapping carries significant implications for environmental management. Initially, the ability of DEMs to provide detailed topographical data aids decision-makers in planning and implementing effective mitigation strategies, such as flood barriers or landslide warning systems [76,77]. These tools are essential for reducing the potential impact of natural disasters on vulnerable communities and infrastructures. In addition, integrating DEMs with GIS and machine learning opens up new methods for predictive hazard modeling, allowing for more proactive management strategies. This approach helps in immediate hazard response and long-term planning for climate change impacts, such as sea-level rise and increased frequency of extreme weather events.

DEMs are crucial in hydrological modeling, particularly in flood simulation and management. They provide detailed information about land surface and riverbed elevations, essential for modeling water flow paths and accumulation zones [5,6]. High-resolution DEMs enable accurate delineation of floodplains and identification of flood-prone areas, facilitating effective emergency planning and community alerts. For example, integrating DEMs with GIS and real-time meteorological data can enhance predictive models, allowing for dynamic flood risk assessments that support timely and targeted responses [78]. The role of DEMs in landslide risk assessment is primarily related to their ability to provide detailed data on terrain slope, aspect, and elevation, all critical factors in determining landslide susceptibility. Scientists can predict potential landslide hotspots by analyzing these terrain parameters, especially in mountainous regions where natural disasters frequently occur. This predictive capability supports land use planning and infrastructure development, aiming to mitigate the impacts of landslides on human settlements and critical transport routes. In seismic hazard assessment, DEMs are employed to model ground shaking and to identify fault zones where earthquakes are likely to occur. Elevation data contribute to the understanding of tectonic settings and the mapping of seismic risk areas [69,79]. This information is vital for urban planning and the design of earthquake-resistant structures, which are critical in minimizing human and economic losses during seismic events. DEMs facilitate the monitoring and prediction of coastal erosion, providing essential data that helps understand how waves and tidal patterns affect shoreline changes [80,81]. This application is critical in the context of rising sea levels and increased storm activity associated with climate change. Coastal managers use DEMs to plan protective measures, such as sea walls and restored habitats, to prevent loss of land and protect coastal communities.

Despite their advantages, the application of DEMs in environmental hazard mapping is limited by several factors. The resolution and coverage of DEM data cannot adequately capture detailed terrain features crucial for accurate analyses. In addition, the static nature of DEMs means they can quickly become outdated as landscapes change. Low-resolution DEMs struggle to capture essential landform details that impact water flow, slope stability, or seismic wave propagation. High-resolution DEMs are often inaccessible in many parts of the world, particularly in developing countries, due to the prohibitive data acquisition and processing costs. Environmental conditions are inherently dynamic; thus, landscapes evolve due to natural processes and human activities, necessitating frequent DEM updates that are impossible due to logistical and financial constraints. Although DEMs are valuable tools, their effectiveness is enhanced when integrated with other geographic and environmental data [82]. This process can be complex and resource-intensive, requiring sophisticated GIS and computational modeling skills. In addition, managing and processing high-resolution DEM demands considerable computational power and technical expertise, which can be challenging, especially in resource-limited environments. However, as natural disasters become more frequent and intense, incorporating climate variables into DEM analyses is essential for accurate hazard assessments. Climate change affects temperature, precipitation, sea level rise, and extreme weather events, altering landscapes and natural hazards such as floods, landslides, and coastal erosion [83,84]. Integrating climate change projections with DEM data can assess future scenarios and regional impacts. Table 5 provides a comprehensive view of the advantages, challenges, opportunities for improvement, and potential risks associated with DEM applications in hazard mapping.

However, addressing these challenges involves several practical implications crucial for maximizing the accuracy and effectiveness of DEMs in environmental hazard mapping and ensuring they remain relevant over time. Enhanced data acquisition and sharing through advanced remote sensing and aerial survey technologies such as drones and satellite-based LiDAR are essential. These technologies provide more frequent updates and higher resolutions, and fostering global cooperation in data sharing can help overcome data availability limitations, especially in under-resourced regions. Developing adaptive models incorporating real-time data and feedback mechanisms can maintain the relevance of DEMs as landscapes change. Integrating DEMs with other environmental and social data through comprehensive GIS platforms can offer a more holistic view of hazard risks. Significant investment in technology and training is necessary to enhance the potential of DEMs. This includes building computational infrastructure and training local experts in advanced GIS and modeling techniques. In addition, continued research and development into new methodologies, including machine learning and artificial intelligence, can improve the accuracy and utility of DEMs, enhancing their predictive power in hazard management. Fig. 6 shows the interaction between the various factors influencing DEM application in environmental hazard mapping.

Accordingly, DEMs have limitations that affect hazard mapping despite their significant utility. The quality and availability of high-resolution DEMs are often restricted by costs and logistical challenges, compromising accuracy in data-scarce regions. In addition, DEMs are static, necessitating frequent updates to capture recent changes due to natural events or human activities. Integrating DEMs with other geospatial data is complex and resource-intensive, requiring advanced computational resources and specialized technical expertise. To overcome these limitations, future work should focus on improving data acquisition through technologies such as drones and satellite-based LiDAR and developing adaptive models that integrate real-time data. Enhancing machine learning applications can also increase predictive accuracy.

Soft computing encompasses a variety of methodologies, including fuzzy logic, neural networks, genetic algorithms, and machine learning, which are particularly effective in enhancing the precision of hazard management [85,86]. These techniques integrate diverse data types such as experimental, remote sensing, topographic maps, and socio-economic datasets [24,87]. Such integration is essential for comprehensive hazard analysis, facilitating the management of the inherent uncertainty and variability of hazards due to changing climatic conditions and geographical transformations [88,89]. Fig. 7 highlights the benefits of integrating soft computing techniques into hazard analysis. These methods improve the accuracy and efficiency of solutions across various hazard mapping applications. Fig. 8 illustrates two problem-solving strategies in computer science and artificial intelligence: hard and soft computing techniques. Each approach has benefits and usages, frequently combined to achieve the best results. Hard computing relies on mathematical logic and accurate and deterministic techniques, which need well-defined issues and yield exact and reliable solutions. However, soft computing offers a more adaptable method that assumes the nuances of imprecision and uncertainty. This adaptability makes it well-suited for addressing complex issues in real-world scenarios.

Explainability in artificial intelligence (AI) models is crucial for their acceptance and effectiveness in multi-hazard mapping. Techniques such as shapley additive explanations (SHAP) and LIME (local interpretable model-agnostic explanations) provide insights into the variables influencing model predictions, enhancing trust and decision-making in hazard mitigation [85,90,91].

Previous studies have developed various methodologies for integrating machine learning in the process of multi-hazard mapping, such as the one given in Fig. 9 by Nachappa et al. [92]. One of the main benefits comes from the adaptability of neural networks and machine learning models that allows for continual learning from new data, improving hazard prediction models [93,94]. As a result, using soft computing in multi-hazard mapping improves the accuracy and reliability of hazard assessments and significantly contributes to developing informed and effective disaster risk reduction strategies. Previously, Kariminejad et al. [95–97] emphasized the role of geo-environmental modeling and deep learning methods in improving hazard mapping and management. Ullah et al. [98–100] further underscored the potential of machine learning techniques in multi-hazard susceptibility mapping, presenting substantial advancements in the field. Rahmati et al. [24] and Nachappa et al. [92] highlighted using machine learning in multi-hazard exposure mapping, providing a practical framework for assessing risks associated with various natural hazards. Despite these advantages, soft computing faces challenges such as computational complexity, the necessity for extensive training datasets, and difficulties in interpreting complex model outputs. However, future advancements in computational power and algorithm efficiency, along with improved data collection methods, are expected to enhance the applicability of soft computing in hazard management.

This paper attempts to review and critically analyze existing literature on applying DEMs in multi-hazard assessments. It highlights the need for broader application of DEMs in multi-hazard assessments by integrating high-resolution DEMs with advanced methods such as machine learning techniques to enhance the precision and utility of hazard maps, which are essential for effective disaster risk reduction and strategic environmental planning. Based on the statements above, the following conclusions are drawn:

Integrating DEMs across multiple hazard types, including floods, landslides, and coastal erosion, enables more comprehensive environmental risk assessments.

The review outlines several limitations in the current applications of DEMs, such as the challenges related to data resolution, update frequency, and the accessibility of high-resolution models, particularly in resource-limited areas. In order to address these limitations, it is recommended that DEMs be enhanced in resolution and accessibility to ensure they provide accurate and current data for hazard assessments. In addition, it recommends developing innovative methods to integrate real-time environmental data into DEMs to capture dynamic changes more effectively and improve the models’ predictive accuracy. Further exploration into expanding machine learning algorithms in conjunction with DEMs is also advised to bolster multi-hazard assessment capabilities. This critical review lays the foundation for future advancements in the field and underscores the importance of bridging these gaps to enhance DEM applications in environmental hazard mapping.