1 Introduction

The resilience of infrastructure in arid regions to extreme weather events, particularly unexpected and severe rainfall, is an increasingly critical concern [1, 2]. These events exacerbate the vulnerability of these areas, impacting community sustainability. The existing literature provides a substantial foundation on climate resilience, with major efforts to address the unique challenges faced by arid regions. Previously, Salimi and Al-Ghamdi [3] highlighted the significant impacts of climate change on urban infrastructure in the Middle East, emphasizing the need for urban resiliency strategies tailored to local environmental contexts. Similarly, Horn and Elagib [4] proposed socio-hydrological solutions for building resilient cities against flash floods in arid environments. These studies underscore the urgency of rethinking traditional infrastructure approaches in these areas. Furthermore, Norman et al. [5] and Piemontese et al. [6] discussed green infrastructure and the over-reliance on conventional water infrastructure systems, respectively, noting their inadequacy in the face of evolving climatic conditions. Kumar et al. [7] and Shakou et al. [1] contributed novel frameworks for assessing risk and enhancing resilience, incorporating technological advancements. Nakhaei et al. [8] explored smart frameworks for community resilience. Moftakhari and AghaKouchak [9] addressed the increasing exposure of energy infrastructure to compound hazards, such as wildfires and extreme rainfall, suggesting the need for integrated solutions that mitigate multiple risks simultaneously. Lebu et al. [10] examined the resilience of sanitation systems to extreme rainfall in Kenya, offering insights into the specific challenges faced by infrastructures in arid and semi-arid regions. Gnyawali et al. [11] proposed a zonation framework for infrastructure prone to rainfall-triggered landslides, highlighting the broader applicability of artificial intelligence (AI) in disaster prevention and response strategies. Additionally, Chirisa and Nel [12] reviewed infrastructure policies in rural areas affected by climate change, pointing out the discrepancies in policy frameworks that often neglect the specific needs of arid regions. Jobbins et al. [13] and Koskei [14] discussed resilience in semi-arid economies and areas, advocating for research agendas and resilience-building measures that are more inclusive of environmental and climatic particularities. Di Vaio et al. [15] suggested rethinking sustainable business models using AI in Agri-food systems. Despite the advancements in understanding these challenges, there remains a critical need for more efforts regarding the integration of advanced technologies, particularly AI, in enhancing infrastructure resilience in arid regions. While AI has been studied for utilization in various scenarios, including flood management strategies to improve disaster preparedness and response, its application and sustainability in the context of infrastructure resilience in arid regions require more investigation. Accordingly, this study focuses on enhancing infrastructure resilience in arid regions through the integration of AI. The unpredictable nature of extreme rainfall events, exacerbated by climate change, necessitates innovative engineering solutions. This research involves performing a comprehensive literature review of existing AI-based engineering strategies that address these climatic challenges, followed by a critical assessment of their sustainability benefits. Additionally, a strengths, weaknesses, opportunities, and threats (SWOT) analysis is performed to evaluate the viability and impact of these solutions. A key part of the study includes defining how these AI solutions align with the United Nations Sustainable Development Goals (SDGs) to ensure that the strategies contribute effectively to sustainable development. The study also proposes considerations and measures for the implementation of AI technologies that can significantly enhance the sustainability of infrastructure systems. This approach aims to promote the adoption of intelligent infrastructure practices and seeks to set new benchmarks for resilience, offering valuable insights for policymakers, engineers, and urban planners in making informed decisions that align with global sustainability targets.

2 Research methodology

In order to critically assess the sustainable benefits of using AI solutions for infrastructure resilience in arid regions when undergoing extreme rainfall events, this study first performs a literature review of existing studies on the adoption of AI-based engineering solutions for infrastructure resilience in arid regions against extreme rainfall events. Thereafter, it conducts a SWOT analysis to evaluate these solutions and conducts a critical assessment of their sustainability benefits. Eventually, it discusses how these AI solutions align with the United Nations SDGs and suggests considerations and measures for the implementation of these technologies. In general, the selection of the literature review and the theoretical and SWOT analyses as the methodology for this study is driven by the need for a detailed understanding of how AI can be utilized to enhance infrastructure resilience in arid regions faced with extreme rainfall events. The literature review serves as a fundamental approach to consolidate existing knowledge on AI-based engineering solutions, identifying how these technologies have been previously implemented and their effectiveness in similar environmental conditions. This methodological choice is rational because it allows for the evaluation of a broad range of studies and practices, providing a robust platform for assessing the sustainability and efficiency of AI applications in infrastructure systems. Moreover, conducting a SWOT analysis complements this review by offering a structured way to critically examine the strengths, weaknesses, opportunities, and threats associated with these AI solutions. This analysis is crucial in determining the practical viability and potential challenges of adopting AI technologies, ensuring that the proposed solutions are not only innovative but also applicable and sustainable in real-world settings. Additionally, by aligning the AI solutions with the United Nations SDGs, the methodology underscores the commitment to sustainable development, ensuring that the study’s outcomes are directed towards promoting long-term environmental, social, and economic benefits. This methodological framework is designed to provide comprehensive insights and strategic guidance to stakeholders, including policymakers, engineers, and urban planners, aiding them in making informed decisions that foster the resilience and sustainability of infrastructure in arid regions.

3 Definition of key terminologies

Infrastructure resilience: This term refers to the capacity of infrastructure systems to anticipate, absorb, adapt to, and rapidly recover from disruptive events, such as natural disasters [16, 17]. In the context of this research, infrastructure resilience specifically addresses the ability of these systems to withstand and quickly bounce back from extreme rainfall events.


Extreme rainfall events: These are meteorological occurrences characterized by rainfall that significantly exceeds the average amount typically expected over a given period and geographical area [18].


Sustainable engineering: This concept involves designing and operating systems in a manner that uses energy and resources sustainably at a rate that does not compromise the natural environment or the ability of future generations to meet their own needs [19].


Sustainability benefits: These refer to the long-term advantages achieved through practices that support environmental, economic, and social viability [20, 21].


Smart engineering solutions: These refer to the application of intelligent design and technology to enhance the efficiency and effectiveness of engineering projects [22].


Artificial intelligence: In the context of this study, AI refers to systems or machines that display human-like intelligence by learning, reasoning, and self-correcting [23, 24].

4 Infrastructure challenges in arid regions with heavy rainfalls

Infrastructure development in arid regions, particularly those that experience heavy rainfall, presents unique challenges due to the inherent climatic and environmental conditions. Arid and semi-arid regions are characterized by low annual rainfall, high evaporation rates, and significant variability in precipitation patterns. This variability, coupled with occasional intense rain events, can lead to severe infrastructure challenges, as depicted in Fig. 1.

Fig. 1
figure 1

Overview of infrastructure challenges in arid regions with heavy rainfalls

Full size image

Understanding these challenges requires a comprehensive examination of the climatic conditions, hydrological responses, and the implications for infrastructure design and maintenance. Arid regions typically receive less than 250 mm of annual rainfall, and semi-arid regions receive between 250 and 500 mm. However, rainfall in these regions is often unpredictable, with long dry spells interrupted by short, intense rain events [25]. This nature of precipitation can exacerbate the difficulties in managing water resources and designing infrastructure capable of withstanding extreme weather conditions. In particular, the sudden influx of large volumes of water can lead to flash flooding, soil erosion, and sediment transport, all of which pose significant risks to infrastructure stability and longevity [26]. One of the most striking examples of rainfall variability in semi-arid regions is found in the Sahel, where rainfall intensification has been observed over recent decades. Panthou et al. [27] noted that the Sahel has experienced increased rainfall intensity, leading to more frequent and severe flooding events. This intensification is linked to changes in atmospheric circulation patterns and rising global temperatures, which enhance the moisture-holding capacity of the atmosphere. As a result, infrastructure in these regions must be designed to cope with both prolonged dry periods and sudden, intense rainfall events. Empirical studies of rainfall patterns in semi-arid environments further highlight the challenges posed by extreme precipitation. For instance, Camarasa-Belmonte and Soriano [28] conducted a study on extreme rainfall intensity at different time scales in a semi-arid environment. They found that short-duration, high-intensity rainfall events are common and can cause significant damage to infrastructure not designed to handle such conditions. This underscores the importance of incorporating robust stormwater management systems and resilient construction materials in infrastructure projects in these regions. The challenges in managing these events are compounded by the limited natural water storage capacity and the rapid runoff associated with arid landscapes. Effective flood management strategies, including the construction of retention basins and improved drainage systems, are essential to mitigate the impact of these extreme weather events on infrastructure. Rainfall-runoff modeling in arid and semi-arid regions presents additional difficulties due to the complex interactions between rainfall, soil properties, and topography. Pilgrim et al. [26] highlighted the challenges in accurately predicting runoff in these regions, where the hydrological response can be highly variable. Traditional models often fail to capture the rapid and often non-linear response of arid landscapes to intense rainfall, leading to underestimation of flood risks and inadequate infrastructure design. The sensitivity of extreme rainfall to temperature in semi-arid Mediterranean regions has also been explored, revealing important insights for infrastructure planning. Pumo et al. [29] found that rising temperatures are likely to increase the frequency and intensity of extreme rainfall events, further stressing the need for adaptive infrastructure designs that can accommodate these changes. This is especially relevant in regions where climate change is expected to exacerbate existing water management challenges. Advanced statistical techniques, such as copula functions, have been employed to analyze extreme precipitation events in arid and semi-arid regions. Pabaghi et al. [30] utilized bivariate analysis to better understand the joint behavior of extreme rainfall events, providing valuable information for designing resilient infrastructure. By capturing the dependence structure between different precipitation characteristics, this approach allows for more accurate risk assessments and improved planning for infrastructure resilience. In arid regions of Western Australia, the ecological impact of extreme rainfall on infrastructure has been studied, with a focus on the germination patterns of annual plant species. Mott [31] observed that intense rainfall events can trigger rapid germination and growth, which, while beneficial for vegetation, can also lead to increased erosion and sediment transport. These ecological responses must be considered in infrastructure planning to prevent damage to roads, bridges, and other critical structures. Long-term rainfall trends and change points in India's arid regions have been analyzed to assess the implications for infrastructure development. Machiwal et al. [32] identified significant trends in rainfall patterns, with notable changes in both the timing and intensity of precipitation. These findings highlight the need for infrastructure designs that are flexible and adaptive to changing climatic conditions, ensuring long-term sustainability and functionality. In Rajasthan, India, spatial and temporal trends of mean and extreme rainfall have been investigated to understand their impact on urban infrastructure. Pingale et al. [33] found that urban centers in this semi-arid state are increasingly vulnerable to extreme rainfall events, which can overwhelm existing drainage systems and cause widespread flooding. This underscores the importance of incorporating climate resilience into urban planning and infrastructure development. The impact of climate change on agriculture and infrastructure in arid regions has been empirically studied, revealing significant challenges for both sectors. Shakoor et al. [34] documented the adverse effects of changing precipitation patterns on agricultural productivity, which in turn affects the stability and sustainability of rural infrastructure. Adaptive strategies, such as the development of drought-resistant crops and improved irrigation systems, are crucial to mitigate these impacts. For ungauged watersheds in semi-arid regions, the estimation of curve numbers is essential for effective water resource management and infrastructure planning. Krisnayanti et al. [35] demonstrated the use of empirical methods to estimate curve numbers, providing a valuable tool for predicting runoff and designing appropriate flood control measures. Accurate curve number estimation is vital for developing infrastructure that can withstand extreme hydrological events. In Australia, a framework for understanding the ecology of arid environments has been proposed to inform infrastructure development. Smith and Morton [36] emphasized the need to consider ecological processes and interactions in the design and maintenance of infrastructure in arid regions. By integrating ecological knowledge into infrastructure planning, it is possible to create more sustainable and resilient systems that can adapt to the unique challenges of arid landscapes. Overall, the infrastructure challenges in arid regions with heavy rainfalls are multifaceted and require a holistic approach to address (see Table 1). The unpredictable and extreme nature of rainfall in these regions necessitates the development of adaptive and resilient infrastructure designs. By incorporating advanced hydrological modeling, ecological considerations, and climate resilience strategies, it is possible to mitigate the risks associated with extreme weather events and ensure the sustainability of infrastructure in arid and semi-arid regions.

Table 1 Major aspects correspond to infrastructure challenges in arid regions with heavy rainfalls
Full size table

5 Role of AI in enhancing infrastructure resilience against heavy rainfalls

The past decade has seen a huge integration of artificial intelligence and other advanced technologies toward improved engineering solutions and enhanced infrastructure resilience [37,38,39,40,41,42]. Currently, AI technologies offer the capability to process vast amounts of data and generate predictive analytics [43,44,45,46,47,48]. Furthermore, they provided the ability to adopt supervised and unsupervised learning schemes [49,50,51,52,53]. As a result, they are increasingly being utilized in digital transformation strategies to improve infrastructure resilience [54,55,56]. In this regard, the integration of AI into flood management strategies represents a pivotal advancement in enhancing infrastructure resilience against flood events. The escalating frequency and severity of floods due to climate change necessitate innovative approaches for effective disaster preparedness and response. Hence, utilizing AI-based solutions that use historical and real-time data to forecast potential flood scenarios enables timely interventions and reduces the impact on infrastructure and communities. Previously, Saravi et al. [57] discussed the application of AI in enhancing resilience and preparedness against adverse flood events. Samaei [58] provided a case study of Tehran to demonstrate how AI can be employed to increase urban resilience. The study emphasizes the use of AI in managing flood risks through the analysis of urban water flows and the optimization of drainage systems. Singh and Goyal [59] explored the role of AI in enhancing climate resilience by helping businesses to better prepare for and mitigate the impacts of flood events to save their operations and assets. Samaei and Hassanabad [60] further elaborated on the management of surface and flood water collection in the Tehran Metropolis using AI. Cao [61] discussed the broader applications of AI and data science in smart emergency, crisis, and disaster resilience. Vamvakeridou-Lyroudia et al. [62] contributed by assessing and visualizing hazard impacts to enhance the resilience of critical infrastructures to urban flooding. Singh and Manoharan [63] examined the role of AI and machine learning in emergency decision-making for natural disasters, emphasizing the significance of AI in real-time data processing and situation assessment. These capabilities are vital for making informed decisions during flood events, ensuring a swift and coordinated response. Abid et al. [64] discussed the integrated disaster management approach by illustrating how AI can boost disaster management by providing comprehensive risk assessments and optimizing resource allocation during flood emergencies. Sun et al. [65] explored the various applications of AI for disaster management, including flood prediction and response. Their review highlights the effectiveness of AI in improving the accuracy of flood forecasts and enabling proactive measures to protect infrastructure. Goyal et al. [66] focused on post-flood management systems based on smart Internet of Things (IoT) devices using an AI approach, demonstrating how AI can aid in damage assessment and recovery planning following flood events. Munawar et al. [67] reviewed the use of cutting-edge technologies, including AI and unmanned aerial vehicles, for post-disaster flood management. Their findings emphasize the role of AI in enhancing the efficiency of recovery efforts and minimizing the long-term impacts of floods. Zabihi et al. [68] proposed a smart sustainable system for flood damage management using AI and multi-criteria decision-making computations, highlighting the potential of AI to improve the accuracy and effectiveness of flood damage assessments. Fotovatikhah et al. [69] investigated the use of computational intelligence in big flood management to identify key challenges and research directions. Their study underscores the need for advanced AI algorithms to handle the complexity of flood management and improve predictive accuracy. Al Qundus et al. [70] discussed the development of a wireless sensor network for AI-based flood disaster detection, illustrating how AI can enhance real-time monitoring and early warning capabilities. Rai Yereseme et al. [71] reviewed sustainable integrated urban flood management strategies and highlighted the role of AI in planning and designing smart cities that should be resilient to flood risks. Jones et al. [72] explored the applications of AI for climate impacts by focusing on flood risk management. Their research highlights the potential of AI to improve flood risk assessments and enhance the resilience of infrastructure to climate-induced flood events. Ye et al. [73] proposed an AI-driven framework for multi-scale urban flood resilience planning and design, emphasizing the importance of AI in creating robust flood management strategies that can adapt to changing climate conditions. Nakhaei et al. [8] discussed the enhancement of community resilience in arid regions through a smart framework for flash flood risk assessment. Their study demonstrated the potential of AI in improving the accuracy of flash flood forecasts and enabling proactive measures to protect vulnerable communities. Table 2 summarizes potential applications for AI technologies in infrastructure management against floods and extreme rainfalls.

Table 2 Potential applications of AI technologies in infrastructure management against heavy rainfalls
Full size table

Moreover, Table 3 lists the areas where AI technologies are applied in infrastructure management against heavy rainfalls. In general, these technologies span a wide array of applications to enhance the resilience and efficiency of infrastructure systems. In this regard, as seen in existing literature, machine learning can aid in predicting and managing flood events, while deep learning processes complex data for better infrastructure monitoring. Reinforcement learning can be utilized to improve resource allocation and decision-making during floods, while natural language processing can help analyze communications to enhance decision-making. Anomaly detection can be used to monitor for potential infrastructure failures, allowing for timely interventions. Generative adversarial networks and transfer learning can be used when developing robust predictive models, optimizing performance, and reducing preparation time for extreme weather scenarios.

Table 3 Areas of applications of various AI technologies in infrastructure management against heavy rainfalls
Full size table

6 Sustainability assessment of AI-based solutions for infrastructure resilience

Artificial intelligence is said to enhance the sustainability of infrastructure systems in arid regions facing extreme rainfall across environmental, economic, and social dimensions, as depicted in Fig. 2. Environmentally, AI can optimize resource management by reducing water and energy waste and improving efficiency during extreme weather events. Predictive maintenance capabilities of AI can enable early detection of infrastructure issues, preventing potential environmental harm. Additionally, AI can support the development of adaptive infrastructure through the design and implementation of flood-resistant structures and erosion control measures. Economically, AI can reduce the frequency and severity of infrastructure failures, thus lowering maintenance costs and enhancing the overall lifecycle of critical assets. It can optimize resource allocation during emergencies, improving the efficiency of emergency responses and safeguarding essential services and economic activities from the impacts of extreme weather. Socially, AI can improve disaster response by providing accurate forecasts and early warnings, which enhances community preparedness and reduces panic. It can promote equitable resource distribution, ensuring all community members have access to essential resources like water during emergencies. Furthermore, AI-driven enhancements in infrastructure resilience can contribute to community stability by ensuring the continuity of essential services such as healthcare and education, even under adverse conditions.

Fig. 2
figure 2

Potential sustainability benefits of AI-based solutions

Full size image

6.1 Potential environmental benefits

The integration of AI into infrastructure systems in arid regions presents numerous potential environmental benefits, particularly in the context of extreme rainfall events. AI technologies offer critical solutions to the unique challenges posed by such events, which are becoming increasingly frequent and severe due to climate change. One significant advantage of AI is its ability to optimize resource management. In regions prone to sudden and intense rainfall, AI can analyze large datasets to forecast water demand and manage water resources efficiently. By predicting rainfall patterns and water usage, AI ensures that water distribution systems operate optimally, reducing water waste and the energy required for water treatment and distribution. This capability is vital in arid regions where water scarcity is a constant issue, and efficient water management becomes even more critical during extreme weather events. AI-driven predictive maintenance is another crucial environmental benefit. Traditional infrastructure maintenance often relies on scheduled inspections that may not detect early signs of deterioration, leading to unexpected failures that can cause significant environmental harm. AI systems, however, continuously monitor infrastructure health using sensors and data analytics to detect anomalies that indicate potential issues. For example, during extreme rainfall, AI can identify early signs of stress or damage in critical infrastructure such as dams, bridges, and drainage systems, allowing for timely interventions. This proactive approach prevents catastrophic failures that could result in widespread environmental damage, such as water contamination from breached dams or soil erosion due to collapsed flood defenses. The role of AI in enhancing infrastructure resilience to extreme weather events is particularly important in arid regions, where infrastructure is typically not designed to handle heavy rainfall. These regions often face challenges such as flash floods, soil erosion, and sediment transport, which can severely impact both natural and built environments. AI systems can analyze historical climate data and real-time weather information to predict extreme rainfall events with high accuracy. This predictive capability enables the design and implementation of adaptive infrastructure solutions that can withstand and respond to such events, thereby minimizing environmental degradation. For example, AI can inform the construction of flood-resistant structures, optimized drainage systems, and erosion control measures, all of which help protect the environment from the adverse effects of heavy rainfall. Moreover, AI technologies contribute to environmental sustainability by promoting the efficient use of energy during extreme rainfall events. Smart grids powered by AI can optimize energy distribution based on real-time demand and supply conditions, reducing energy waste and lowering greenhouse gas emissions. During extreme weather, AI can manage the integration of renewable energy sources, ensuring stable and efficient energy use despite the challenges posed by the weather. This not only enhances the sustainability of energy systems but also supports the transition to a low-carbon economy, which is essential for mitigating climate change impacts that exacerbate extreme rainfall events. AI also facilitates the implementation of green infrastructure solutions that work in harmony with the natural environment, especially during extreme weather conditions. For instance, AI can be utilized to design and manage green roofs, permeable pavements, and urban green spaces that absorb rainwater, reduce runoff, and mitigate the urban heat island effect. These solutions are particularly beneficial during heavy rainfall, as they help manage excess water and prevent urban flooding. By integrating AI with green infrastructure, cities in arid regions can become more resilient to extreme weather events while also promoting sustainable urban development. In the agricultural sector, which is critical in many arid regions, AI technologies can optimize irrigation practices to conserve water and protect soil health during extreme rainfall. AI systems can monitor soil moisture levels, weather conditions, and crop health to determine the optimal amount of water needed for irrigation. This precision agriculture approach reduces water use, prevents soil degradation, and enhances crop yields, contributing to both environmental sustainability and food security. During extreme rainfall events, AI can adjust irrigation schedules to prevent waterlogging and soil erosion, protecting crops and maintaining soil health.

6.2 Potential economic benefits

The economic benefits of integrating AI into infrastructure systems, on the other hand, potentially include the cost savings achieved through predictive maintenance and early detection of infrastructure issues. Traditional maintenance approaches often rely on fixed schedules, which may not align with the actual condition of the infrastructure, leading to either unnecessary maintenance or unexpected failures. AI systems, however, continuously monitor the health of infrastructure using sensors and data analytics to detect early signs of wear and tear. This allows for timely maintenance interventions, preventing costly repairs and catastrophic failures. When reducing the frequency and severity of infrastructure failures, AI helps lower maintenance costs and extends the lifespan of critical assets. Another potential economic benefit is the optimization of resource allocation during extreme rainfall events. AI can analyze real-time data on weather patterns, water levels, and infrastructure conditions to prioritize resource deployment more effectively. This ensures that resources are directed to the areas more in need, enhancing the efficiency of emergency response efforts and reducing the economic impact of extreme weather events. For instance, AI can help manage the distribution of sandbags, emergency personnel, and equipment to areas at highest risk of flooding, minimizing damage and associated costs. AI also plays a crucial role in enhancing the resilience of infrastructure, which has long-term economic benefits. Resilient infrastructure systems are less likely to fail during extreme weather events, reducing the economic losses associated with infrastructure damage, business interruptions, and recovery efforts. By incorporating AI into the design and management of infrastructure, arid regions can develop systems that are better equipped to withstand extreme rainfall, thereby safeguarding economic activities and maintaining community functionality during and after such events. Furthermore, AI-driven optimization of water and energy use contributes to significant cost savings. In arid regions, where water and energy resources are often scarce and expensive, AI can enhance the efficiency of these resources, reducing operational costs. For example, AI can predict water demand and optimize irrigation schedules, ensuring that water is used efficiently and wastage is minimized. Similarly, AI can manage the integration of renewable energy sources into the power grid, optimizing energy distribution and reducing reliance on costly fossil fuels. These efficiencies not only lower operational costs but also contribute to the overall economic stability of the region. The adoption of AI technologies can also stimulate economic growth by fostering innovation and creating new business opportunities. The development and implementation of AI solutions require skilled labor, which can lead to job creation and the growth of high-tech industries. Additionally, AI-driven advancements in infrastructure resilience can attract investments and encourage the development of new technologies and services. This innovation ecosystem can drive economic development, enhance competitiveness, and position arid regions as leaders in sustainable and resilient infrastructure solutions. Moreover, AI can help mitigate the economic risks associated with climate change. Extreme rainfall events and other climate-related hazards pose significant threats to economic stability. By improving the ability to predict, prepare for, and respond to these events, AI reduces the economic vulnerability of communities and businesses. This enhanced resilience can lead to lower insurance premiums and reduced financial risks, further strengthening the economic benefits of AI integration. AI's role in facilitating data-driven decision-making also enhances economic efficiency. By providing accurate and timely information, AI enables policymakers, engineers, and business leaders to make informed decisions that optimize the use of resources and maximize economic returns. For instance, AI can help urban planners design infrastructure that is more adaptable to changing climatic conditions, ensuring that investments in infrastructure yield long-term economic benefits.

6.3 Potential social benefits

AI-enhanced infrastructure can significantly improve disaster response and management. By leveraging predictive analytics, AI can forecast extreme weather events with high accuracy, providing communities with earlier warnings and more preparation time. Increased preparedness helps reduce panic, allows for orderly evacuations if necessary, and, overall, reduces the potential for chaos and harm during crises. Moreover, AI systems can facilitate efficient resource allocation during disasters, ensuring that emergency services are directed where they are more needed, thus saving lives and minimizing injury. This is particularly important during extreme rainfall events when the risk of water contamination or infrastructure damage could disrupt supply to certain areas. AI systems, if thoughtfully trained and overseen, can maintain a balanced distribution, which not only fosters a sense of fairness but also prevents conflicts and dissatisfaction among community members. Furthermore, AI could contribute to the long-term sustainability of communities by enhancing infrastructure resilience. Roads, bridges, and public buildings that are more resilient to extreme weather conditions ensure that daily life can continue with minimal disruption during and after rainfall events. This stability is crucial for the social fabric of communities, as it maintains access to essential services such as healthcare, education, and commerce, even in adverse conditions. By reducing the frequency and impact of service disruptions, AI helps maintain normalcy in the lives of residents, which is vital for mental and emotional well-being. AI also aids in job creation and economic stability. The deployment of AI technologies in infrastructure projects often requires skilled labor, creating new job opportunities in areas such as system maintenance, data analysis, and infrastructure monitoring. These jobs not only provide economic benefits but also encourage the development of local expertise and skills, which are essential for the sustained growth and technological advancement of the community. In addition, AI-driven systems improve the overall living environment. By optimizing energy use and reducing emissions through smart grid technologies, AI contributes to cleaner air and a healthier environment. This is particularly beneficial in urban areas of arid regions, which might otherwise suffer from poor air quality due to dust and industrial pollutants. Cleaner air leads to lower rates of respiratory diseases and overall better public health, enhancing the quality of life for all residents. Moreover, AI's impact extends to enhancing educational and communication tools. During extreme weather events, AI can be used to maintain communication channels, ensuring that critical information reaches all segments of the community. This connectivity is essential for managing community response and for continuous education on disaster preparedness, helping build a culture of resilience.

6.4 Potential risks and costs

Although AI-based solutions offer significant advantages in enhancing the resilience of infrastructure in arid regions, it is critical to acknowledge the substantial risks and costs associated with their implementation. While AI can offer a cost-effective alternative to conventional approaches, the adoption of AI entails more than just the upfront expenses of acquiring and deploying the technology. Moreover, the long-term costs include continuous investments in system maintenance, regular updates, and the need for highly specialized personnel to manage these systems. For regions with limited financial resources, these ongoing expenditures can pose a significant barrier to successful integration. Furthermore, AI systems are highly dependent on the quality and availability of data. In arid regions, where data might be scarce, biased, or outdated, the performance of AI models can be severely compromised. Poor data quality can lead to inaccurate predictions or decisions that exacerbate vulnerabilities instead of mitigating them, disproportionately affecting marginalized or underserved communities. Additionally, there is a growing concern about the false predictions of AI systems. Such issues can mislead engineers and policymakers, resulting in inappropriate infrastructure decisions, particularly in high-stakes contexts like disaster management and climate adaptation. In addition, the use of AI technologies comes with the risk of unintended consequences, as models trained to address specific technical issues may overlook broader social, economic, or environmental factors. This narrow focus can result in decisions that, while optimizing for certain aspects, inadvertently introduce new risks or exacerbate existing problems, especially in communities that are already vulnerable. From a public policy perspective, these concerns underline the need for a balanced approach to AI adoption in infrastructure. Policymakers must go beyond the focus on technological benefits and consider the financial, ethical, and social implications of AI deployment. Clear regulatory frameworks, along with strong governance structures, are essential to ensure that AI technologies are used responsibly and inclusively. Collaboration between policymakers, engineers, and local communities is critical to creating AI-driven solutions that are not only innovative but also equitable and adaptable to long-term sustainability challenges. This comprehensive approach will help mitigate the risks associated with AI, ensuring that its benefits can be fully realized without compromising the social, economic, and environmental resilience of infrastructure systems in arid regions.

6.5 Alignment with United Nations sustainable development goals

As mentioned before, using AI-based systems for enhancing infrastructure resilience in arid regions during extreme rainfall events aligns closely with several key SDGs, including SDG 6, SDG 9, SDG 11, and SDG 13, as shown in Table 4 and Fig. 3.

Table 4 Analysis of various AI benefits and their related to the United Nations SGDs
Full size table
Fig. 3
figure 3

Alignment of AI-based solutions with the SDG when achieving resilient infrastructures during heavy rainfall events

Full size image

SDG 6: clean water and sanitation focuses on ensuring the availability and sustainable management of water for all. AI-enhanced infrastructure can contribute significantly to this goal in arid regions by optimizing water use and improving water distribution systems. Through predictive analytics and real-time data monitoring, AI could ensure that water resources are managed more efficiently, minimizing waste and ensuring that clean water is available even during and after extreme weather events.

SDG 9: industry, innovation, and infrastructure is about building resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation. AI can play a pivotal role in meeting this goal by driving the development of smart, resilient infrastructure that can withstand the challenges posed by extreme rainfall. AI's ability to predict weather impacts and optimize maintenance schedules for infrastructure not only enhances durability but also supports sustainable industrialization through the integration of innovative technologies that reduce environmental impact and improve safety.

SDG 11: sustainable cities and communities aims to make cities and human settlements inclusive, safe, resilient, and sustainable. AI contributes to this goal by enhancing urban planning and management. Smart AI systems can help design cities that are better equipped to handle extreme weather events, ensuring that urban areas remain functional and safe during such conditions. Furthermore, AI can enhance the quality of life by maintaining critical infrastructure, thus ensuring continuous access to essential services, which is key to the resilience and sustainability of urban communities.

SDG 13: climate action calls for urgent action to combat climate change and its impacts. AI aids in achieving this goal by enabling better prediction and management of the effects of climate change, such as extreme rainfall in arid regions. By integrating AI into environmental monitoring systems, cities can adapt more proactively to climate-related challenges, implement more effective disaster response strategies, and contribute to global efforts to mitigate the effects of climate change through innovative technology solutions.

6.6 SWOT analysis

The SWOT analysis of AI-based solutions for enhancing infrastructure resilience in arid regions against extreme rainfall events highlights several key factors (Table 5). These solutions exhibit significant strengths, such as their high efficiency and accuracy in data analysis and decision-making, which enable faster and more informed responses to extreme weather challenges. They also have the capacity to predict and mitigate the impacts of extreme weather, contributing to sustainability by reducing damage and long-term infrastructure repair costs. AI's ability to integrate seamlessly with existing infrastructure systems enhances functionality without requiring complete overhauls, while optimized processes and predictive maintenance help reduce resource consumption and carbon footprints and prolong the lifespan of infrastructure. However, these technologies are not without weaknesses. One of the primary challenges is the high initial implementation cost, which includes expenses related to hardware, software, and the skilled labor necessary for deployment and integration. Ongoing operational costs, such as those for maintenance, updates, and scaling, also require continuous investment. Another concern is the heavy reliance on data quality and availability, as poor data management can result in biased outputs and inaccurate predictions. Traditional infrastructure sectors may also resist adopting AI solutions due to entrenched practices. Additionally, the complexity of managing AI systems and the risk of "hallucinations" when AI models produce inaccurate results pose further challenges, potentially leading to harmful unintended consequences. On the opportunities side, the continuous technological advancements in AI present a promising area for developing more sophisticated tools to manage extreme weather events. The global emphasis on sustainability and climate resilience, aligned with SDGs, provides a supportive context for the adoption of AI technologies. AI can also drive new policy frameworks and set higher standards for sustainable infrastructure, which can, in turn, strengthen economic resilience and stability in communities. Beyond these direct benefits, AI-enhanced infrastructure could improve social outcomes, including safety and community resilience. However, there are significant threats to the successful implementation of AI-based solutions. Cybersecurity risks and concerns over data privacy remain critical issues. Additionally, the rapid pace of climate change could outstrip the development and adaptation of AI technologies, rendering some solutions obsolete. Economic and political instability may also disrupt funding and support for AI initiatives, while the long-term sustainability and adaptability of AI systems are uncertain. Lastly, if not properly managed, biased training data or AI hallucinations could lead to faulty decisions, exacerbating existing vulnerabilities and causing negative socio-economic impacts. By addressing the identified weaknesses and threats and capitalizing on the strengths and opportunities, AI technologies can play a pivotal role in creating more sustainable and resilient infrastructure systems.

Table 5 SWOT analysis of the proposed AI-enhanced framework
Full size table

7 Considerations and measures for implementation of AI solutions to achieve enhanced sustainability benefits

The introduction of AI into infrastructure systems is not just a technological upgrade but a transformative process that enhances sustainability. In general, it is essential to begin with a clear understanding of the goals AI aims to achieve, chiefly enhancing the resilience and sustainability of infrastructure. Table 6 lists the main considerations and measures for the implementation of AI solutions to achieve enhanced sustainability benefits.

Table 6 Considerations and measures for implementation of AI solutions in arid regions
Full size table

The compatibility of AI solutions with existing infrastructure and their ability to scale with increasing data and usage are critical. To that end, conducting thorough cost–benefit analyses should help assess the financial viability of AI implementations, balancing long-term savings against initial investments. Diverse funding options, including public–private partnerships and government grants, will have to be explored. Engaging stakeholders in the planning and implementation process should better ensure that the concerns of the community are addressed and trust is built. Making AI solutions accessible to all community members is imperative to bridge the digital divide and promote inclusive development. Environmental considerations are also paramount. Implementing energy-efficient AI technologies reduces the carbon footprint of infrastructure operations. Protecting data privacy and securing systems against cyber threats is fundamental to maintaining the integrity of AI systems. Navigating the regulatory and policy landscape is essential for compliance and advocacy. In addition to these factors, it is essential to address the potential risks and costs of AI solutions. As highlighted in Table 6, AI systems are vulnerable to biases, inaccuracies, and hallucinations when trained on poor-quality or outdated data, which can result in faulty decisions and unintended consequences. Regular validation and updates of AI models are necessary to prevent these issues. Policymakers and engineers must implement rigorous validation processes to detect biases, avoid hallucinations, and ensure the accuracy of AI recommendations. By addressing these risks, AI solutions can be deployed more responsibly, minimizing harmful impacts and ensuring long-term relevance in the face of dynamic environmental conditions. Finally, establishing clear metrics for monitoring and evaluating the performance and impact of AI solutions is essential for continuous improvement. Creating feedback mechanisms to incorporate stakeholder insights ensures that AI solutions remain effective and relevant to community needs.

8 Conclusion

This study aims to review existing studies on the utilization of AI solutions for infrastructure resilience in arid regions against extreme rainfalls. Moreover, it performs a critical assessment on the sustainability benefits of integrating AI into infrastructure systems to enhance their resilience, sustainability, durability, and responsiveness to extreme rainfall events in arid regions. This research contributes to the sustainable benefits of AI-enhanced infrastructure resilience in arid regions under the strain of unusual and severe rainfall events. Within the study context, a SWOT analysis was performed, the alignment of these technologies with the United Nations SDGs was analyzed, and considerations and recommendations for implementing these technologies to enhance the sustainable benefits were suggested. Accordingly, this study offers critical insights and recommendations for policymakers and engineers. Based on the aforementioned statements, the following conclusions are drawn:

  • AI technologies can significantly improve infrastructure resilience by enabling predictive maintenance, optimizing resource management, and facilitating adaptive infrastructure designs that can withstand extreme weather events.

  • AI can optimize water and energy usage, reduce environmental harm through early detection of potential infrastructure failures, and support the development of flood-resistant structures and erosion control measures.

  • AI can reduce maintenance costs and extend the lifespan of infrastructure assets through predictive analytics. It also potentially enhances resource allocation during emergencies, saves economic activities, and reduces the financial impact of extreme weather events.

  • AI can enhance disaster response and community preparedness, ensure equitable resource distribution, and maintain the continuity of essential services such as healthcare and education, thus supporting community stability.

  • AI-based solutions align closely with Clean Water and Sanitation (SDG 6), Industry, Innovation, and Infrastructure (SDG 9), Sustainable Cities and Communities (SDG 11), and Climate Action (SDG 13), promoting inclusive, resilient, and sustainable development in arid regions.

  • The high costs associated with the development, deployment, and continuous maintenance and operation of AI systems can be prohibitive, particularly in economically constrained regions. These costs include not only the initial setup but also ongoing investments in updates, skilled labor, and infrastructure.

  • There is a significant risk that AI systems, when trained on biased or insufficient data, may produce biased or "hallucinating" predictions, generating inaccurate or false predictions due to poorly trained models. These errors can lead to unintended harmful consequences, particularly in infrastructure planning and disaster response, where accuracy is paramount.

Accordingly, future research should focus on developing cost-effective AI technologies, enhancing data collection and management practices, and fostering collaborations between policymakers, engineers, and communities to ensure the successful integration of AI in infrastructure systems. Additionally, exploring innovative funding mechanisms and advocating for supportive regulatory frameworks will be crucial for overcoming these challenges and achieving sustainable infrastructure resilience in arid regions against extreme rainfall events. Future studies should also examine the long-term adaptability of AI solutions and their ability to evolve with climate change.