1 Introduction

The frequency and severity of flood disasters have alarmingly increased over the past few decades, posing serious threats to ecosystems, economy, and communities worldwide. Floods are the most frequent and destructive natural catastrophes, affecting millions of people and resulting in significant economic losses annually, according to the United Nations Office for Disaster Risk Reduction [51]. A number of causes, such as deforestation, fast urbanisation, climate change, and inadequate land management techniques, are responsible for the rising frequency of floods [71]. Communities are more vulnerable to flood threats as a result of changing climate patterns and an increase in the frequency of extreme weather events. This emphasises the critical need for preventative actions to increase disaster resilience.

Improving the resilience of flood disasters is crucial, considering the significant effects these calamities have on the environment and society. Floods have long-term effects on livelihoods, food security, and public health in addition to immediate threats to infrastructure and human lives. Furthermore, the long-term consequences of floods, like biodiversity loss, water pollution, and soil erosion, can have a lasting impact on the impacted areas, making it more difficult for them to recover and rebuild [69]. A greater understanding of the significance of implementing innovative flood disaster management techniques that go beyond conventional reactive measures is emerging in light of these difficulties. The dataset shown in Fig. 1 shows the total number of flood incidents that were reported worldwide each year between 1990 and 2020. Every data point shows the total number of documented flood incidents for a specific year. Flood activity has fluctuated over the past three decades, with certain years showing considerable surges. The years 2005, 2015, and 2018 had a notable rise in the number of flood events, with peaks of 221, 227, and 232 flood occurrences, respectively. On the other hand, there have been times of comparatively low flood frequency, including 1990, 1998, and 2020. These are times of relative stability. Overall, the trendline captures both short-term fluctuations and long-term patterns in worldwide flood activity, showing the wider trajectory of flood occurrence across the studied time span. This dataset can guide future analysis and research focused at improving flood disaster resilience globally, as well as providing a basis for understanding the temporal dynamics of flood episodes.

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Global trends in flood occurrence (1990–2020)

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By combining developments in geospatial technology and computational approaches to analyse, simulate, and visualise flood dangers, geo-computational techniques have emerged as a viable approach to improving resilience to flood disasters. These methods include a wide range of approaches, such as machine learning, LiDAR, unmanned aerial vehicles (UAVs), remote sensing, geographic information systems (GIS), and geospatial big data analytics. Each of these approaches has specific advantages for assessing and mitigating flood risk [8, 22]. Through the integration of geospatial data with computational tools, stakeholders can evaluate vulnerability, obtain important insights into the dynamics of floods, and create focused policies to lessen the effects of floods on ecosystems and populations.

The potential of geo-computational tools in flood catastrophe management is underscored by a growing body of literature. For instance, the integration of high-resolution imaging and elevation data through remote sensing offers valuable insights for mapping flood extents and identifying vulnerable locations [9]. LiDAR technology delivers precise elevation data for flood modelling and simulation, while GIS enables spatial analysis tools for evaluating flood risk and planning evacuation routes [47]. After floods, UAVs with sensors and cameras can be used for quick damage assessment and situational awareness, allowing for prompt reaction and recovery operations [10]. Furthermore, by providing stakeholders with predictive skills for early warning systems and adaptive measures, machine learning algorithms and geospatial big data analytics improve communities’ resilience to future flood occurrences [28].

The incorporation of geo-computational methods into flood disaster management signifies a fundamental change in our strategy for enhancing resilience. These strategies allow decision-makers to make well-informed decisions and efficiently deploy resources in response to flood dangers by bridging the data-decision gap. Furthermore, communities' ability to adapt to shifting climatic conditions is strengthened by the iterative nature of geo-computational modelling, which enables constant development and refining of flood risk assessments [55]. As a result, there is a growing understanding of the significance of integrating geo-computational techniques into more comprehensive frameworks for climate adaptation and disaster risk reduction, as stated in international agreements like the Paris Climate Agreement and the Sendai Framework for Disaster Risk Reduction [24]. The goal of this study is to present a thorough analysis and assessment of the function of geo-computational methods in flood disaster mitigation. The uses of remote sensing, GIS, LiDAR, UAVs, machine learning, and geospatial big data analytics in flood catastrophe management, drawing on case studies, actual data, and already published research is inverstigated. The fundamental ideas behind each method, show how each contributes to flood risk assessment and mitigation, and pinpoint important findings, difficulties, and areas that need more investigation and application are looked. With the findings of this study, the transformative capacity of geo-computational methods in fostering resilient ecosystems and communities against escalating flood risks is demonstrated.

2 Background and context

For millennia, floods have shaped cultures, economics, and landscapes, leaving an enduring impression on human history. Floods are a frequent and destructive natural phenomenon that have affected civilizations all across the world throughout recorded history. Archaeological findings and historical documentation shed light on the frequency and severity of flood episodes in various locations. Frequent floods affected the social, economic, and cultural development of ancient civilizations, including those around the Tigris and Euphrates rivers in Mesopotamia and the Nile River in Egypt. Early civilizations flourished because of the rich soil that was made available for agriculture when river valleys were flooded. Floods, on the other hand, posed serious threats to settlements as well, resulting in extensive damage and fatalities. The effects of flooding on human populations have worsened in more recent times due to the industrial revolution and increased urbanisation. The number of individuals vulnerable to floods has increased dramatically over the past century [24] with urbanisation and population growth aggravating vulnerability in flood-prone areas. Additionally, research in the past has demonstrated how climate change affects flood frequency and intensity, with rising global temperatures causing more frequent and severe extreme weather events [18].

Communities are more susceptible to flood dangers due to a number of causes, including institutional, environmental, and socioeconomic ones. Floods are frequently made worse by socioeconomic inequality, which disproportionately affects marginalised populations in the wake of a disaster. Studies from the past highlight how poverty, substandard housing, and limited resource availability contribute to an increased risk of flooding. By changing natural hydrological processes and decreasing the landscape's ability to absorb and attenuate floodwaters, environmental degradation increases the danger of flooding. Communities are more vulnerable to flooding as a result of deforestation, soil erosion, and wetland loss, which reduce natural flood defences [66]. Communities' susceptibility to flood disasters is also influenced by institutional factors, such as poor infrastructure, incompetent land-use planning, and weak governance systems. Many studies emphasise how governance and institutional capabilities shape flood resilience and vulnerability. In many areas, weak flood defences, antiquated or laxly enforced building rules, and a lack of early warning systems increase vulnerability and impede efficient disaster response and recovery operations. Flood catastrophe management still faces several obstacles in spite of technological breakthroughs and enhancements in preparedness and response. One of the main issues is the absence of thorough risk mapping and assessment, which makes it more difficult for authorities to identify high-risk locations and set priorities for mitigation activities [57].

Another issue is inadequate flood defences and infrastructure, which is especially problematic in low-lying coastal areas and heavily populated urban centres. Communities are more vulnerable to catastrophic floods because ageing infrastructure may not be able to handle the number and intensity of flood events that are occurring more frequently. Furthermore, the length and intensity of flood catastrophes can be increased by gaps in early warning systems and communication networks that obstruct prompt evacuation and emergency response operations. Vulnerability is made worse by limited access to trustworthy information and resources, which makes community resilience-building initiatives more difficult, especially in rural and isolated locations [15]. Furthermore, efforts to address underlying vulnerabilities and effectively manage flood disasters can be hampered by sociopolitical variables such as bureaucratic inefficiency, corruption, and inadequate finance [65]. Disparate approaches to flood risk reduction and mitigation can frequently be the consequence of conflicting interests and a lack of cooperation amongst government agencies and stakeholders, which further complicates catastrophe response and recovery efforts. Therefore, a comprehensive strategy including risk assessment, infrastructure development, early warning systems, and community engagement is needed to solve the current issues in flood catastrophe management. Policymakers and practitioners can devise more efficacious methods for bolstering resilience and alleviating the effects of floods on susceptible communities by comprehending the historical patterns and variables that contribute to community susceptibility.

Our methodology was designed to ensure transparency, rigor, and comprehensiveness in the review process. Drawing on established protocols in the field [43], a methodical process was used to locate and compile research on the improvement of flood disaster resistance by using geo-computational methods. The search approach comprised a careful selection of databases, such as Scopus and Web of Science, that are well-known for their extensive coverage of academic literature. The evaluation focused on the link between flood catastrophe resilience and geo-computational approaches, therefore it was carefully selected a set of keywords to catch pertinent articles. The established inclusion criteria ensured that the studies that were chosen closely matched the study goals. Acceptable papers were those that used geo-computational techniques in the context of flood management, addressed research topics methodologically, and showed some relation to the overall theme of flood catastrophe resilience. Predefined criteria were used to evaluate each paper throughout the screening process, which included a detailed review of titles, abstracts, and full-text articles. In order to promote openness and dependability in the selection process, any disagreements or ambiguities that arose during screening were settled by consensus among the study team members.

To extract the most important insights from the chosen publications, a methodical process of data extraction and synthesis was followed. To promote methodological transparency and reproducibility, the procedure followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In order to augment openness, the paper features a PRISMA Flowchart (Fig. 2) that outlines the successive procedures entailed in the processes of data extraction and literature selection. This flowchart walks readers through the steps of the systematic review process, from article identification to final inclusion, by providing a visual depiction of our methodology. This deliberate and transparent approach aimed to provide an extensive overview of the literature on the application of geo-computational techniques to enhance flood disaster resilience. In order to ensure the authenticity and reliability of review results and further this vital field's understanding, process adhered to stringent criteria and processes.

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PRISMA Flowchart illustrating literature selection and data extraction process

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3 Overview of geo-computational techniques

Within the field of geospatial sciences, the application of computational methods has completely changed how we see, process, and organise spatial data. Leading the charge in this transformation is a group of approaches known as geo-computational techniques. These methods enable the extraction of useful insights from large and intricate geospatial datasets by combining computer science, geography, and environmental science. Remote sensing, which uses satellite and aerial sensors to gather precise information on the Earth's surface, is essential to this strategy. Researchers can map land cover, track changes in the environment, and identify natural dangers with previously unheard-of accuracy by using data from remote sensing [29]. The omnipresent Geographic Information Systems (GIS), which constitute the foundation for spatial data administration and analysis, are a valuable addition to remote sensing. GIS platforms enable users to perform geographic analysis, integrate various datasets, and visually represent geospatial data in an understandable and informative manner. By making it possible to investigate spatial relationships, patterns, and processes inside geographic datasets, spatial analysis techniques provide even more value to the analytical toolbox. These techniques, which range from proximity analysis to spatial statistics, make it easier to identify geographical trends and hotspots that are important for making decisions across a variety of industries. The capabilities of geo-computational approaches have been further extended by emerging technologies like machine learning and deep learning, which allow for automated feature extraction, categorization, and prediction from geographical data. These methods have great potential for solving difficult spatial issues as they develop and grow, from public health and disaster management to urban planning and environmental preservation [24]. Because of their computational power and interdisciplinary nature, geo-computational techniques have the potential to revolutionise the geospatial industry and spur innovation (Fig. 3).

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Applications of geo-computational techniques in flood disaster management

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3.1 Principles and applications of remote sensing for flood mapping and monitoring

A key component of contemporary geospatial analysis, remote sensing provides fast and thorough information on areas that are flooded, which is essential for flood monitoring and mapping. Fundamentally, remote sensing is the process of taking pictures of the Earth's surface using sensors on board satellites or aircraft. This allows for the identification and description of flood dynamics, impacts, and extent. Researchers and practitioners can monitor floods, evaluate their severity, and assist decision-making processes that aim to reduce risks and increase resilience by utilising a variety of remote sensing platforms and approaches. A synopsis of significant research on the application of remote sensing methods to flood mapping and monitoring is included in the Table 1. Information about the remote sensing platform used, the data resolution used, and the particular use of remote sensing for flood monitoring is included with each study (M [41]). A wide range of applications are covered by the studies, such as mapping the extent and dynamics of floods, identifying and characterising the effects of floods, estimating agricultural productivity in flooded areas, modelling flood dynamics for early warning systems and prediction, tracking long-term trends in flood patterns, and assessing the impact of climate change on flood risk. This thorough overview emphasises the various ways that remote sensing is used in flood management and emphasises how crucial it is to improving our knowledge of flood threats and assisting with well-informed decision-making. The identification of flood extent and inundation dynamics is one of the main uses of remote sensing in flood monitoring. In particular, synthetic aperture radar (SAR) imaging has proven to be an effective technique for mapping the extent of floods, particularly in regions that frequently experience cloud cover or at night. High-resolution, precise flood maps may be created thanks to SAR's capacity to see through clouds and collect data in all weather. Research have shown how useful SAR imagery is for emergency response and disaster management activities by demonstrating how well it maps flood extent during significant flooding occurrences [2]. Figure 4 shows rapid flood risk mapping schematic diagram, in which to prepare a flood risk mapping, various data such as L8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) model are called upon and are pre-processed on the Google Earth Engine (GEE) platform. Then, Landsat 8 satellite images are used to generate four indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil Texture (ST) ST, and Land Use (LU). Additionally, the SRTM DEM model is used to produce six indices Elevation (El), Slope (Sl), Slope Aspect (SA), River Distance (RD), Waterway and River Density (WRD) and Topographic Wetness Index (TWI). In addition, one-day precipitation data related to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) satellite are used. Therefore, in general, 11 risk indices are generated and are used to model flood risk. In the next step, the RF model is fed by the values of each risk index, which are obtained from historical floods, for the performance of the training stage. After evaluating the RF model by the testing data, a flood risk map is generated for the values of all of the pixels and then the importance of each index is determined.

Table 1 Seminal works on the theory and uses of remote sensing for mapping and monitoring flooding
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Fig. 4
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Rapid flood risk mapping schematic diagram regions [21]

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Apart from measuring the extent of floods, remote sensing aids in evaluating the intensity of floods and their effects on both the natural and constructed environments. Important information about how floods affect vegetation health, water quality, and land cover can be gleaned from multispectral and hyperspectral photography. Researchers may measure these alterations and evaluate the degree of flood damage to infrastructure, ecosystems, and agricultural fields using spectral analysis techniques. For instance, a study conducted in 2019 by Anderson et al. used multispectral imaging to evaluate how flooding affected agricultural output. This helped farmers and policymakers comprehend the financial effects of flood disasters and guide recovery efforts [1]. By combining pictures with hydrological models and Geographic Information Systems (GIS), remote sensing aids in the modelling and prediction of flood dynamics as well. Through the integration of remotely sensed data with rainfall records, topographical details, and hydrological parameters, scientists may replicate flood occurrences, predict their patterns, and pinpoint regions susceptible to flooding. The effectiveness of remote sensing-based flood modelling techniques in generating early warnings and guiding evacuation plans has been shown in some studies which lowers the possible loss of life and property during flood occurrences [39]. In addition, remote sensing makes it easier to track long-term trends and modifications in flood patterns, which advances our knowledge of the effects of climate change and hydrological variability. Through the examination of past satellite images and time-series data, scientists may discern patterns in the frequency, length, and intensity of floods. This allows them to evaluate the impact of climate change on flood risk and susceptibility. Therefore, remote sensing is a useful technique for mapping and monitoring floods, giving vital information for risk assessment, disaster response, and resilience-building initiatives. We can better comprehend flood threats and make educated decisions at local, regional, and global levels thanks to remote sensing's capacity to identify flood extent, evaluate severity, model dynamics, and track long-term trends. The use of remote sensing in flood management is anticipated to increase as data availability and remote sensing technology progress, providing new avenues for improving flood resilience and lowering disaster risks in a world growing more prone to disasters.

3.2 Spatial analysis and decision support for flood risk assessment using geographic information systems (GIS)

Geographic Information Systems (GIS) have become essential instruments for decision support and spatial analysis in the evaluation of flood risk. Researchers and practitioners may evaluate flood threats, vulnerabilities, and impacts with accuracy and efficiency because to GIS's integration of geographical data with analytical capabilities. Capturing, storing, manipulating, analysing, and visualising geographic data is the fundamental function of GIS, which offers a thorough framework for comprehending intricate spatial relationships and patterns. Several studies have shown how useful GIS is for assessing flood risk, highlighting its adaptability and suitability in a variety of geographic contexts and scales (Table 2). For example, a study simulated flood scenarios and assessed the possible effects on metropolitan areas using GIS-based hydrological models [14]. Policymakers were able to efficiently allocate resources and prioritise mitigation measures by identifying high-risk zones prone to floods through the use of spatial analytic tools including terrain modelling and proximity analysis. In order to create thorough flood risk maps, GIS also makes it easier to integrate other datasets, such as topographic maps, hydrological models, land use/cover data, and socioeconomic indicators. These maps aid in land use planning, emergency preparedness, and disaster response activities by offering important insights into flood-prone areas, exposure levels, and population risk. Other studies showed the value of spatial analysis in identifying coastal vulnerabilities and guiding adaptation strategies in the face of sea-level rise and climate change. These studies used GIS-based approaches to develop flood risk maps for coastal regions [21]. Along with mapping flood risk, GIS helps decision-making processes by giving stakeholders access to spatially explicit information and decision support tools. Built on GIS platforms, decision support systems (DSS) combine models, data, and analytical tools to help with risk assessment, scenario analysis, and ambiguous decision-making. With the use of these tools, stakeholders can investigate various approaches to flood control, assess their efficacy, and determine the best course of action based on risk reduction goals and cost–benefit analyses. The use of GIS-based DSS for flood risk management in river basins was shown in research by [37], demonstrating its potential to strengthen community resilience to floods and improve decision-making processes. Furthermore, land use changes, climatic variability, and urbanisation trends are examples of dynamic elements impacting flood risk that can be assessed with the help of GIS. Researchers can quantify the effects of these elements on flood susceptibility, exposure, and resilience through spatial analysis and modelling tools, offering important insights for long-term planning and policy development. GIS-based methods were used in studies to evaluate the impact of changes in land use on the risk of flooding in urban areas. The findings highlighted the necessity of proactive land management techniques to lessen the effects of flooding and improve urban resilience [56, 63, 70]. Therefore, geographic information systems (GIS) are essential for decision support and spatial analysis related to flood risk assessment. GIS's analytical powers allow geographical data, modelling tools, and decision support systems to be integrated for a thorough assessment of flood hazards, vulnerabilities, and impacts. Geographic Information System (GIS) enables stakeholders to prioritise resources, make well-informed decisions, and implement efficient flood management methods by offering spatially explicit information and decision support tools. It is anticipated that the use of GIS in flood risk assessment and management would expand as a result of climate change and urbanisation, underscoring the need for continued study and innovation in this area and contributing to the frequency and severity of floods.

Table 2 Geographic information systems (GIS) studies for flood risk evaluation
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3.3 High-resolution elevation data for flood modelling and simulation using LiDAR technology

Because LiDAR (Light Detection and Ranging) technology provides high-resolution elevation data necessary for accurately portraying topographical and hydrological aspects, it has revolutionised flood modelling and simulation. LiDAR makes it possible to create accurate Digital Elevation Models (DEMs) and hydrological models by capturing detailed three-dimensional information about the Earth’s surface. This allows researchers and practitioners to simulate flood events with previously unheard-of accuracy and dependability. Several studies have proven the effectiveness of LiDAR technology in flood modelling and simulation, highlighting its adaptability and suitability for a variety of flood situations and geographical locations (Table 3). Some conducted research that employed LiDAR-derived DEMs to enhance the precision of flood inundation mapping. This led to the development of more dependable flood risk assessments and emergency response plans. The study was able to simulate flood dynamics and evaluate the effects of several flood scenarios on infrastructure and populations by merging LiDAR data with hydrological models [47].

Table 3 Research on LiDAR technology for flood simulation and modelling
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Furthermore, through in-depth topography analysis and hydrological modelling, LiDAR technology makes it possible to identify and characterise locations that are vulnerable to flooding. Understanding topographic elements like slope, aspect, and drainage patterns is essential for comprehending surface runoff and flood paths. LiDAR-derived elevation data can help with this. According to other studies, high-resolution elevation data is crucial for precisely defining flood hazard zones. These studies used LiDAR-based hydrological models to identify floodplains and evaluate flood risk in coastal locations. LiDAR technology provides rapid and precise information about flood dynamics, which aids in the development of early warning systems and flood mitigation techniques in addition to flood modelling and risk assessment [47, 49, 56]. When combined with sophisticated data processing methods, real-time LiDAR data collection makes it possible to monitor flood occurrences' extents, water levels, and flow velocities. According to research, it is feasible to use LiDAR-based monitoring systems to track the advance of floods and notify at-risk areas in a timely manner, perhaps minimising the loss of life and property. Furthermore, by making it easier to analyse historical elevation data and hydrological changes over time, LiDAR technology advances our knowledge of long-term flood trends and repercussions. Researchers can distinguish patterns in urbanisation, changes in land cover, and alterations in river courses that affect flood resilience and susceptibility by contrasting LiDAR-derived DEMs from various time periods [17]. LiDAR data was used in studiesto evaluate the effects of land use changes on flood risk in urban settings. These studies emphasise the significance of temporal dynamics in flood modelling and management strategies. LiDAR technology is critical to flood modelling and simulation because it provides high-resolution elevation data that are necessary for precise terrain and hydrological feature modelling. LiDAR helps scientists and practitioners to better analyse flood risk, create early warning systems, and lessen the effects of floods on infrastructure and populations by capturing detailed three-dimensional information about the Earth's surface. LiDAR technology is predicted to become increasingly important in flood modelling and simulation as it develops and becomes more widely available. This will present new opportunities to improve flood resilience and lower the risk of disaster in areas that are already prone to flooding [14, 31].

3.4 Rapid deployment of unmanned aerial vehicles (UAVs) for post-disaster assessment and response

In post-disaster evaluation and response operations, unmanned aerial vehicles (UAVs) have shown to be indispensable instruments for prompt deployment. Unmanned Aerial Vehicles (UAVs) are crucial in supporting targeted response actions and decision-making processes by providing critical situational awareness through their rapid navigation of disaster-affected areas and high-resolution imagery gathering. They are significant resources in disaster management because of their adaptability and accessibility, which help responders prioritise interventions, assess damage, and identify risks quickly. Several research works have demonstrated the use of UAVs in post-disaster scenarios, demonstrating its efficacy in a range of disaster contexts and geographical locations. For instance, research showed how to employ UAVs for post-hurricane damage assessment, wherein the amount of infrastructure damage was evaluated, and restoration activities were prioritised using footage collected by the UAV [38]. In a similar vein, research demonstrated the use of UAVs in post-earthquake evaluations, where data from the drones helped quickly identify collapsed structures and evaluate access routes for rescue operations. Furthermore, UAVs are clearly superior to conventional evaluation techniques, especially when it comes to securely accessing dangerous or difficult-to-reach locations [33]. UAVs can provide real-time aerial footage and infrared imaging to support search and rescue efforts and assess the amount of damage to ecosystems and infrastructure in catastrophe situations like floods, landslides, or wildfires. The use of UAVs in flood-affected areas was emphasised by studies, where they were used to monitor floodwaters, identify submerged risks, and evaluate the impact on residents and crops. Furthermore, by lowering the time and resources needed for data gathering and processing, UAVs improve the effectiveness and cost-effectiveness of post-disaster assessments (Table 4). UAVs provide high-resolution imagery at a fraction of the expense of traditional approaches like manned aerial surveys or ground-based evaluations due to their rapid deployment capabilities. In post-disaster assessments, studies showed how time-saving UAVs might be. UAV-derived data facilitated quick decision-making and response coordination, which improved disaster response and recovery efforts [23].

Table 4 Unmanned aerial vehicles’ (UAVs’) function in post-disaster evaluation and response
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UAVs are being used more and more for long-term recovery and resilience-building projects, in addition to their function in the immediate aftermath of a disaster. UAVs assist with urban planning, infrastructure restoration, and hazard mitigation activities by offering comprehensive aerial surveys and 3D mapping of disaster-affected areas [67]. The use of UAVs in post-disaster reconstruction projects was demonstrated by studies, where data from UAVs was used to design resilient infrastructure and land-use zoning to lessen vulnerability to future disasters. UAVs are essential to post-disaster assessment and response operations because they offer high-resolution images, quick deployment capabilities, and reasonably priced data collecting for decision-making procedures. UAVs provide in better situational awareness, search and rescue operations, and long-term rehabilitation efforts in disaster-affected areas because of their adaptability and accessibility. UAVs are predicted to become more and more important in disaster management as technology develops and becomes more widely available. This will present new chances to improve disaster resilience and lessen the effects of calamities on infrastructure and communities in the future (Mohd [44]).

3.5 Predictive capabilities for early warning systems and adaptable strategies with machine learning and geospatial big data analytics

Geospatial big data analytics and machine learning (ML) have become potent instruments for improving early warning systems and adaptive plans in disaster management. Through the use of sophisticated algorithms and enormous volumes of geospatial data, machine learning techniques make it possible to predict disaster events with previously unheard-of precision and offer insightful information for proactive decision-making (Table 5). The potential for enhancing disaster resilience and response plans in a variety of scenarios is enormous due to the synergy between machine learning and geographic analytics. Numerous scholarly works demonstrate the efficacy of machine learning and geospatial big data analytics in diverse facets of catastrophe management. For example, a study showed how to use machine learning (ML) algorithms to forecast landslip susceptibility based on past landslip occurrences and terrain parameters. The study attained great accuracy in detecting landslide-prone locations by analysing geospatial data, including topography, land cover, and precipitation patterns. This allowed early warning systems to reduce the risk of landslides [3].

Table 5 Developments in disaster management using machine learning and geospatial big data analytics
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Furthermore, by examining meteorological data, hydrological models, and historical flood records, ML approaches have demonstrated promise in the fields of flood forecasting and early warning systems. ML algorithms were used in studies to create predictive models for flood inundation mapping. This allowed authorities to create evacuation plans and send timely warnings in areas that were susceptible to flooding. These models improve preparedness and response strategies by using geographical data, such as river discharge, rainfall intensity, and terrain elevation, to increase the accuracy and dependability of flood forecasts. Apart from their predictive powers, machine learning algorithms are essential for disaster response since they optimise resource allocation and adaptive tactics [13]. The effective delivery of supplies and manpower to affected areas was made, which illustrated the application of ML-based optimisation algorithms for route planning and resource allocation in disaster logistics. These algorithms increase the efficiency of disaster response operations by optimising response efforts and reducing response times by examining geographical data, such as road networks, population density, and infrastructure damage [20].

Furthermore, during disaster events, machine learning and geospatial analytics enable real-time monitoring and situational awareness, allowing authorities to analyse the evolving situation and modify response methods as necessary. Research demonstrated how ML algorithms may be used to track the development of wildfires and evaluate the effects on impacted communities by analysing data from social media and satellite imagery. Through the integration of real-time sensor data, social media feeds, and geospatial data, these systems enable prompt actions to safeguard property and people, giving decision-makers invaluable information. Additionally, the integration of diverse data sources and the extraction of useful information from sizable geographic datasets are made possible by ML approaches [32, 33]. In order to determine how vulnerable vital infrastructure is to natural disasters, research showed how machine learning (ML) algorithms may be used to analyse multi-source geospatial data, such as satellite imaging, aerial surveys, and ground-based sensor data. These analytics assist adaptive methods for enhancing resilience and lessening the impact of disasters on communities and infrastructure by detecting vulnerable assets and prioritising mitigation efforts. For early warning systems and disaster response, machine learning and geospatial big data analytics provide predictive capabilities and adaptive methods. Machine learning (ML) techniques provide precise catastrophic event prediction, optimal resource allocation, real-time monitoring, and integration of diverse data sources by utilising sophisticated algorithms and copious volumes of geographical data. Integration of ML and GIS analytics into disaster management systems has the potential to improve response to natural hazards and resilience as these fields continue to advance [48, 62].

4 Methodologies and approaches

With the introduction of geo-computational technology, disaster management methodologies and approaches have changed dramatically, providing creative ways to lessen the effects of flood disasters. Case studies demonstrate how these methods are applied in real-world settings and demonstrate how well they work to improve reaction plans, risk assessment, and early warning systems (Table 6). For instance, research emphasises the use of remote sensing and Geographic Information Systems (GIS) for flood mapping and vulnerability assessment in metropolitan settings. Authorities were able to prioritise mitigation measures and enhance community resilience by using the study's significant insights into flood-prone areas, which were obtained through the integration of satellite imagery and spatial analysis tools. A comparative examination of methods shows the wide variety of strategies used in flood catastrophe management and their applicability in various situations. Some research compares the efficacy of machine learning algorithms, hydraulic models, and hydrological models when it comes to flood modelling [34]. In order to help decision-makers choose the best approach for their particular requirements, the comparison analysis evaluates elements including accuracy, computational efficiency, and data requirements. Additionally, comparison analyses point out the advantages and disadvantages of each strategy, directing further study and development in the field of flood disaster management. It is clear that there are obstacles and restrictions related to the use of geo-computing approaches in flood disaster management. These range from problems with data availability and quality to demands for technical competence and computational limitations [9]. Data interoperability is a major obstacle to the integration of heterogeneous datasets from various sources, including socioeconomic indicators, weather data, and satellite images. The scalability of geo-computing methodologies is also limited by computational limitations, especially when processing large-scale datasets and doing real-time analysis during crisis situations. Furthermore, widespread adoption is hampered by the technical know-how needed to develop and understand complicated modelling techniques, especially in environments with limited resources. It will take multidisciplinary cooperation and creativity in a variety of fields, such as computer science, disaster management, and geographic science, to meet these difficulties. Studies from the past highlights the value of capacity building and training initiatives to improve practitioners' and decision-makers' technical proficiency in applying geo-computational methods [27]. Moreover, developments in big data analytics and cloud computing present viable ways to get over computational limitations and increase the scalability of geo-computational models. Cloud-based systems facilitate real-time analysis of large-scale geospatial datasets by utilising distributed computing resources and parallel processing techniques. This allows for more robust and rapid decision-making in flood catastrophe management. With the use of geo-computational tools, flood disaster management methodologies and approaches have advanced significantly. Case studies show how these methods are applied in real-world settings and show how well they work to improve reaction plans, risk assessment, and early warning systems. A comparative examination of methodologies helps decision-makers choose the best methodology for their particular requirements by illuminating the advantages and disadvantages of various strategies. But problems like data interoperability, computational limitations, and the need for technical skills still exist, and their effective resolution calls for interdisciplinary cooperation and creativity. Geo-computational tools have the ability to transform flood disaster management and enhance community resilience against more frequent and severe flood disasters by surmounting these obstacles [33].

Table 6 Geo-computational techniques integrated into flood disaster management
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5 Consequences for disaster resiliency in flooding

The key insights and conclusions drawn from the analysis underscore the transformative possibilities offered by geo-computational methods in fostering flood-resistant communities. Through comprehensive scrutiny of case studies, comparative analyses, and challenges, this examination unveils several significant implications for enhancing flood disaster resilience. The use of geo-computational methodologies facilitates a more precise and prompt evaluation of flood risk, thus augmenting readiness and reaction endeavours. Research shows how machine learning algorithms, GIS, and remote sensing may be used to identify flood-prone locations and increase the accuracy of flood forecasts. Decision-makers can reduce the impact of floods on vulnerable areas by using predictive modelling and advanced spatial analysis to prioritise mitigation measures and distribute resources more effectively. Geo-computational techniques also make it easier to create reliable early warning systems, which means that preventive actions can be done before flood catastrophes happen [54]. The integration of UAVs for quick flood damage assessment allows for real-time aerial imagery to improve situational awareness and guide emergency response activities. Authorities can more efficiently coordinate rescue and relief efforts, detect vital infrastructure damage, and rapidly assess the extent and severity of flooding by utilising unmanned aerial vehicles (UAVs) and remote sensing capabilities. The analysis also emphasises how crucial interdisciplinary cooperation and knowledge exchange are to increasing flood disaster resistance [12]. The necessity of technical training programmes and capacity building activities to improve practitioners’ and decision-makers' abilities to use geo-computational approaches. Collaborating among researchers, policymakers, and practitioners can yield useful insights and facilitate the sharing of best practices to address shared concerns and enhance flood disaster management techniques. Furthermore, geo-computational methodologies have the capacity to significantly impact long-term resilience building and adaptation tactics in addition to urgent reaction efforts [50]. Comparative studies demonstrate the efficacy of various flood modelling techniques in a range of geographical situations, offering planners and policymakers insightful information to help them choose the best course of action for their areas. Through the integration of hydrological modelling, machine learning algorithms, and spatial analysis, communities may create comprehensive strategies for managing flood risks that take into consideration evolving environmental circumstances and climate projections. In conclusion, stakeholders may successfully lessen the effects of flood disasters and create more resilient communities by putting these suggestions into practice and utilising the transformative potential of geo-computational tools [40]. The combination of sophisticated geographical analysis and predictive modelling can open the door to a more resilient future in the face of growing dangers associated with climate change through cooperative efforts and creative solutions.

6 Discussion and future directions

The field of flood catastrophe resilience is changing quickly thanks to the ongoing development of geo-computational methods. Although our evaluation has offered a thorough analysis of the available literature, more investigation and synthesis of the results are necessary to completely clarify the consequences and provide possible future paths in this important area. The application of geo-computational technologies for flood catastrophe resistance is highlighted by our examination of new advances and trends in this field. We acknowledge that the significance of our results warrants more investigation beyond simple listing. For example, we may provide concrete instances of the effectiveness of geo-computational approaches in improving flood resilience by examining particular case studies where they have been applied successfully [4]. Furthermore, filling in the gaps in the literature and suggesting new lines of inquiry can enhance our conversation, point scholars in new areas, and encourage creativity in flood catastrophe management.

In flood risk management, it is critical to close the gap between theoretical developments and real-world implementation. We may help the smooth incorporation of geo-computational approaches into present practices by clarifying the practical implications of our findings for practitioners, policymakers, and community stakeholders. Giving stakeholders specific examples of how these results might influence decision-making procedures will enable them to take proactive steps to reduce the danger of flooding and improve their preparation for emergencies [45]. The intricacy of flood catastrophe resilience demands interdisciplinary and cross-sector collaboration. Stakeholders may work together to overcome issues like limited computing power and data interoperability by collaborating across disciplines. Additionally, promoting standardisation of risk assessment protocols and modelling techniques would encourage knowledge exchange and interoperability among stakeholders, enabling more effective and efficient flood disaster management. Encouraging practitioners and decision-makers to apply geo-computational approaches with greater technical competency requires investments in training programmes and capacity building activities. Prioritising professional development and education will enable people and organisations to use innovative ideas and state-of-the-art technology in flood resilience initiatives [52].

New developments and trends in geo-computational methods for flood catastrophe resistance portend a bright future for disaster management. Novel applications of geo-computational techniques are being investigated to improve flood resistance at different scales as technology advances. The use of machine learning (ML) and artificial intelligence (AI) algorithms into models for assessing flood risk is one such trend. Predictive models and early warning systems can now analyse large, complicated datasets and find minute patterns in the dynamics of floods thanks to these sophisticated methodologies. Furthermore, during flood disasters, there are never-before-seen possibilities for real-time monitoring and decision-making thanks to the development of cloud computing and big data analytics. Through the utilisation of scalable data processing systems and distributed computing resources, stakeholders can optimise response plans in near real-time and obtain vital insights into flood dynamics. Furthermore, new opportunities for data gathering and monitoring in flood-prone areas are presented by the development of cutting-edge sensor technologies and unmanned aerial vehicles (UAVs) [54]. Accurate flood modelling and risk assessment are made possible by the deep insights into terrain form and flood extent provided by high-resolution elevation data acquired by LiDAR technology and UAV-based images. Furthermore, the incorporation of citizen science projects and crowdsourced data enables local people to actively engage in flood monitoring and response operations, promoting resilience at the local level. In spite of tremendous progress, more study and cooperation are still desperately needed in the area of flood disaster resistance [64]. Researchers, legislators, practitioners, and community stakeholders must work together to address complicated issues like data interoperability, computational limitations, and technological skills. Developing comprehensive and context-specific solutions to lessen the effects of flood disasters requires interdisciplinary cooperation that span the gaps between academic institutions, governmental bodies, non-profit organisations, and local communities. Standardisation of modelling approaches, risk assessment procedures, and data protocols is also necessary in order to promote knowledge sharing and interoperability among various industries and geographical areas. Stakeholders can expedite data sharing and collaboration, leading to more efficient decision-making and resource allocation in flood catastrophe management, by instituting standard frameworks and best practices. Additionally, training programmes and capacity development activities are crucial for improving the technical proficiency and knowledge base of practitioners and decision-makers in the efficient application of geo-computational methodologies. By making investments in professional development and education, people and organisations will be better equipped to prepare for and implement flood resilience using cutting-edge technologies and creative solutions.

7 Conclusion

Mitigating the effects of flood disasters and constructing future-ready communities require proactive efforts. Through increased risk assessment, early warning systems, and adaptive methods, the integration of geo-computational approaches presents hitherto unseen prospects to improve flood resistance. Stakeholders can create more sustainable and effective solutions to the intricate problems presented by flood disasters by utilising new technological trends and developments. However, cooperation and coordinated efforts from a variety of stakeholders are needed to make significant progress. Everyone may contribute to increasing flood catastrophe resilience from academics and politicians to practitioners and community members. We can create a future that is more robust to flood disasters and more capable of thriving in the midst of hardship by cooperating, exchanging information and resources, and embracing innovation.