As the most prevalent and destructive natural disaster, floods negatively impact both human populations and built and natural ecosystems (Kia et al., 2012; Mehravar et al., 2023; Samanta et al., 2018). It causes a significant damage to human lives, agricultural lands, infrastructure, properties, and the overall economy (Charlton et al., 2006; Dang and Kumar, 2017; Scheuer et al., 2017). Alarming statistics revealed that floods result in over 2000 human casualties annually, and affecting over 75 million people worldwide either directly or indirectly (Calil et al., 2015; Gain et al., 2015). This catastrophe is the outcome of several correlated natural “climate driven irregular rainfall and excessive snowmelts” and human-caused events including rapid urbanization, deforestation excessive use of greenhouse gases (Rao, 2001; Scheuer et al., 2017; Xu et al., 2023).

As the risk of floods and associated damages have intensified in recent decades (Alfieri et al., 2015; Arnell and Gosling, 2016; Zhao et al., 2022), effective flood susceptibility assessment modeling is required to minimize the floods hazards (Jamali et al., 2022; Otieno, 2004). Although floods are incredibly difficult to prevent entirely, with well-planned strategies and implementation of appropriate mitigation measures, the inherent risks related to flooding can be significantly reduced. To address these risks and enhance flood management, researchers have developed different method to assess, map, and manage the flood susceptibility for an area (Das and Gupta, 2021; Dou et al., 2018; Khosravi et al., 2016a). Accurate flood susceptibility assessment needs a thorough understanding of an area’s topographical and climatic variables, as some parameters are detrimental in defining an area’s vulnerability to flood hazards.

For instant, elevation is a major factor affecting the water depth and flow direction (Gigović et al., 2017). Higher-elevation areas having steep slopes are less vulnerable to floods in comparison to lower-elevation and flat areas (Das, 2019; Kazakis et al., 2015; Liuzzo et al., 2019). Similarly, the slope measures the extent to which an object is inclined either steeply or obliquely in relation to a horizontal plane and is considered one of the crucial variables in flooding (Gigović et al., 2017). The duration and water flow rate are greatly impacted by the slope (Islam and Sado, 2000). Compared to steeper terrains, flat surfaces are more flood-prone because the water flow is slower and it accumulates there for longer periods of time (Gigović et al., 2017; Rimba et al., 2017). Precipitation is also an important factor, as intense and heavy rainfall over a short period could increase the chances of flooding in an area (Gao et al., 2023; Santato et al., 2013). Moreover, the rainfall-runoff process is determined directly by the soil properties and type of the area, like the thickness, infiltration rate, permeability, and wetness before the rainfall (Ouma and Tateishi, 2014; Zhiyu et al., 2013). Different soil types have varying resistance and absorption capacities of water (Ouma and Tateishi, 2014; Zhiyu et al., 2013). Consequently, the water-holding capacity and infiltration properties of different soils also influence flood susceptibility (Sugianto et al., 2022; Hill et al., 2010; Shen and Chui, 2023). Many flood hazard management experts highlighted the importance of land use-land cover (LULC) in determining the rainfall-runoff process (Fernández and Lutz, 2010). Another variable that is a key indicator of flood hazards and flood risk zones is the distance of a location from streams and river (Fernández and Lutz, 2010). Areas closer to waterbodies like rivers and streams are more prone to flooding compared to those located away from riverbeds. Similarly, normalized difference of vegetation index (NDVI) is one of the crucial variables to determine the flood susceptibility of an area (Ali et al., 2020). Dense vegetation can reduce the effect of floods (Ali et al., 2020). Finally, land surface temperature (LST) is a basic component of a regional climate and impacts ecosystems both locally and globally. LST is the measure and a good indicator of the thermal radiation emission from the land surface, which determine air flow temperature and its flow, consequentially effecting floods (Xue et al., 2018).

Flood management is crucial component in understanding flood and the formulation of risk-curtailing strategies to minimize the associated threats (Abbaszadeh, 2016; Dandapat and Panda, 2017). Numerous studies have highlighted the importance of identifying and mapping flood-risk areas to lower flood-related losses (Calil et al., 2015; Gain et al., 2015; Rao, 2001; Samanta et al., 2018; Zou et al., 2013). Flood hazard mapping is a pivotal tool, as it plays a critical role in planning, early warning systems, appropriate response efforts, and risk mitigation (Gain et al., 2015; Siahkamari et al., 2018). Advanced experimental and modeling analyses are crucial for the prediction and monitoring of natural variables and their impacts on flood susceptibility. For example, the use of UAV-acquired multispectral images has been shown to effectively assess the biomass of riparian plants and its influence on the hydrodynamics of vegetated streams (Crimaldi and Lama, 2021). Moreover, understanding the flow resistance of floodplain vegetation mixtures plays a significant role in modeling river flows, with vegetation cover directly affecting water movement and flood behavior (Box et al., 2021). Similarly, the validation of global flow resistance models in experimental drainage channels can improve predictions related to flow dynamics in vegetated waterways (Errico et al., 2019). However, the significance of flood hazard mapping cannot be overstated, as it encompasses crucial elements such as flood awareness, early warning systems, and the development of initiatives to reduce flood hazards (Dang and Kumar, 2017; Das and Gupta, 2021).

Innumerable attempts have been made to estimate and illustrate the areas susceptible to flooding (Hong et al., 2018; Shafapour Tehrany et al., 2017; Zhao et al., 2018). A variety of techniques and statistical models have been developed for a precise flood assessment (Abbaszadeh, 2016; Dandapat and Panda, 2017; Naulin et al., 2013). However, conventional flood hazard mapping techniques have various shortcomings (Guo et al., 2014). Traditional flood hazard mapping relies on historical flood records, simple hydrological models, or static inundation maps, which fail to account for dynamic factors like urbanization, deforestation, or climate change. While useful for baseline data, these methods lack the precision and spatial accuracy needed for detailed risk assessments, especially in rapidly changing environments. They are also less effective in predicting extreme weather or compound flood hazards, which require advanced, multi-criteria decision-making models. Moreover, in developing countries with limited resources, the traditional methods pose additional obstacles due to their resource-intensive nature, thus making them inappropriate in such regions.

Since traditional approaches may not provide an extensive understanding of river and flood dynamics, this emphasizes the need for alternative and innovative approaches (Mahmood and Ullah, 2016; Ozdemir and Altural, 2013). In recent years, researchers have developed various models and methods, often in combination with geospatial technologies, to identify and delineate flood hazard areas (Ali et al., 2020; Shafapour Tehrany et al., 2017; Yariyan et al., 2020). This study integrates global positioning system ArcGIS 10.5 (hereafter GIS), remote sensing, and multi-criteria decision analysis (MCDA) using the analytical hierarchy process (AHP) analysis to model flood susceptibility, due to the high accuracy, efficiency and convenience of this approach (Ajibade et al., 2021; Allafta and Opp, 2021; Aydin and Sevgi Birincioğlu, 2022; Das and Gupta, 2021; Karymbalis et al., 2021). Recently, this method has been used in many studies to track down and depict flood-prone zones (Ali et al., 2020; Hagos et al., 2022; Hong et al., 2018; Khosravi et al., 2016b; Mahmood and Rahman, 2019; Negese et al., 2022).

Pakistan has been impacted by floods on every front (Hussain et al., 2020), experiencing 21 major floods between 1950 and 2011, averaging one major flood every three years (Shah et al., 2017). These floods claimed 8,887 lives, caused an estimated economic loss of 19 billion US dollars, and destroyed more than 100,000 villages (Ali, 2013). Prior to 2022 flood, the country faced one of the biggest floods in 2010, which affected over 14–20 million people and caused 1,700 fatalities and caused economic damage worth US$9.7 billion by effecting almost every sector of the country (Floods, 2010). However, Pakistan was hit by an even more catastrophic event in 2022, often referred to as the “monster” flood, which simultaneously affected all four provinces (Nanditha et al., 2023). By October 11, 2022, a total of 94 districts of all four provinces of the country, were proclaimed flood afflicted. Alone in Khyber Pakhtunkhwa (KP) province, 17 districts were affected by the 2022 flood. It affected a population of around 4,350,490, resulting in 306 human casualties [males 149 (49%), females 41 (13%), and children 116 (38%)] and 21,328 livestock losses. Furthermore, 91,463 households (HHs) were fully or partially damaged due to the floods. Additionally, 107 bridges crumbled, and 1,275 kilometers (km) of roads were damaged. Crops, being no exception, as about 107,220 hectares of agricultural land was hit by the 2022 flood. Given KP’s vulnerability to recurring floods (Mahmood and Ullah, 2016; Ullah et al., 2018), this study focuses on assessing, categorizing, mapping, and quantifying the flood susceptibility of the province. The region’s complex topography and sensitive climate have aggravated flood susceptibility (Rahman et al., 2023; Ullah et al., 2018). Moreover, the study area is the first impact zone for most of the flood events in the country. This study aims to asses, categorize, map, and quantify the flood susceptibility of the Khyber Pakhtunkhwa province.

In this study, we used GIS-based MCDM and AHP analysis to map flood-prone areas across Khyber Pakhtunkhwa, the country’s first impact zone for floods. This method has been widely employed in previous studies to identify and map flood-susceptible regions (Ali et al., 2020; Hagos et al., 2022). Due to its efficacy and precision, this technique, along with other contributing factors, has been widely used in various studies to assess and map flood-vulnerable areas (Abdelkarim et al., 2020; Ali et al., 2020; Allafta and Opp, 2021; Aydin and Sevgi Birincioğlu, 2022; Das and Gupta, 2021; Karymbalis et al., 2021).

In our case this method proved its efficiency, reliability and accuracy, as the AUC value was 0.911, indicating that the model performance was excellent (Sharif et al., 2016; Wang et al., 2023). To assess our model’s performance, we compared its AUC value (0.911) with other commonly applied models, such as those by Hagos et al. (2022) and Mahmoud and Gan (2018), which reported AUC values of 0.85 and 0.88, respectively. The higher AUC value indicates that our model, incorporating AHP and expert validation, more accurately captures the complex flood dynamics of Khyber Pakhtunkhwa. Additionally, the model’s integration of local knowledge and adaptability to dynamic environmental factors further enhances its reliability in predicting flood susceptibility in this rapidly changing region. A complete understanding of the flood hazards susceptibility for the area is provided by the flood susceptibility model categorization that are derived from the AHP analysis. The resulting map and classifications would be helpful for timely decision-making and efficient flood risk management. Understanding the probability and extent of flood occurrences provides the basis for effective management of flood hazards (Binns, 2022). Flood-prone area maps are vital for managing and mitigating flood hazards, providing key information to residents and stakeholders (Abdelkarim et al., 2020; Rahmati et al., 2016). Recently, sensitivity analysis and natural resource monitoring have gained importance in flood risk mitigation, particularly for ecohydrological systems and vegetated waterways (Lama and Crimaldi, 2021). Remote sensing techniques like NDVI and dNBR are effective for monitoring environmental changes that influence flood susceptibility (Mohammad et al., 2023). Additionally, flood-mitigation reservoir designs, with varied configurations, can significantly reduce peak discharge during floods (Pirone et al., 2024). These advanced methods enhance flood hazard understanding and improve mitigation strategies.

According to AHP analysis, four factors—rivers/streams, precipitation, slope, and LST—contribute 82% to the flood susceptibility map. Precipitation is particularly critical as heavy rainfall causes river overflow, leading to floods. Similar results were registered by Shahabi et al. (2021). Precipitation is a key factor that triggers flood occurrences, as heavy or prolonged rainfalls leads to large volumes of runoff water entering streams and rivers, causing flood inundation (Allafta and Opp, 2021; Feng et al., 2023; Xu et al., 2023).

Like the volume of water, its velocity, which is greatly influenced by slope, also determines the flood susceptibility of an area. Generally, water flows more slowly in areas with lower slope, causing water accumulation in lowlands or flatlands, thereby increasing the risk of flooding. In contrast, steeper slopes mountainous regions prevent water from accumulating (Das and Gupta, 2021; Mujib et al., 2021; Wang et al., 2015; Zzaman et al., 2021). Our findings showed a clear pattern: the extreme northern edges of the area, which are characterized by steep mountain ranges, correspond with the safe zone identified in our flood susceptibility results. The results have clearly shown that plain areas with a minimum slope are more vulnerable to floods compared to areas with steeper slope (Shahabi et al., 2021). The findings of this criterion are consistent with the findings of Khosravi et al. (2016b).

In addition to rainfall and slope, the occurrence, density, length, and proximity of streams and rivers also play an important role in influencing flooding. The flood vulnerability is higher in the areas that are near the streams or with a higher density of the streams than the areas situated away from the rivers and streams since heavy rainfall results in overbank flows, which cause flooding of adjoining lowland areas (Mahmoud and Gan, 2018; Termeh et al., 2018). Moreover, usually areas away from the rivers are at the higher elevation, which also adds to low flood risks (Kia et al., 2012; Lee and Rezaie, 2022; Zzaman et al., 2021). Our study’s results align with these findings, showing that areas nearer to rivers and streams indicate a greater susceptibility to flood hazards (Abdelkarim et al., 2020; Das and Gupta, 2021; Lee and Rezaie, 2022; Mahmoud and Gan, 2018; Zzaman et al., 2021).

Studies have also revealed an association between LST and flooding. The global warming and climate change are the reasons for an increase in the LST, causing ice caps in the Himalayan region to melt rapidly, leading rivers to flood (Jain et al., 2023). The relationship between rise in LST and flooding is indirect, as many studies have shown that LST varies considerably with the LULC and vegetation cover. With an increase in urban cover and barren land the LST values also increases while it decreases in areas with higher vegetation (Achmad and Muftiadi, 2019; Jamali et al., 2022; Rahaman and Shermin, 2022). Many studies have established a correlation between higher NDVI and lower flood vulnerability (Das, 2018; Tehrany et al., 2013). Vegetation cover affects climate and land surface fluxes both regionally and globally, while climate also plays a significant role in determining vegetation cover (Hussain et al., 2023). A positive relationship between the La Niña events and temperature has been established by depicting higher land surface temperatures with an increase in La Niña events (Eboy and Kemarau, 2023; Yan et al., 2020).

The intense monsoon rains in Pakistan were linked to strong La Niña events (Ju and Slingo, 1995; Safdar et al., 2019). Abnormally high temperatures during the spring and summer of 2022 enhanced a deep depression from the Arabian Sea, resulting in heavy rainfall across the country (Mallapaty, 2022; Otto et al., 2023). Temperatures rose to more than 50°C in few parts of the country in the months of March and April 2022, which is an atypical time of year to witness severe heat. The rise in temperature speeds up the melting of Pakistan’s around 7,000 glaciers in the northern parts, that feed the country’s rivers. Recent studies have highlighted that climate induced fluctuations in temperature and rainfall are the driving forces of repeated floods (Mahmood and Ullah, 2016; Qi et al., 2024; Ullah et al., 2018). Simultaneously, changes in land use pattern and excessive urbanization leads to an increase in impervious surfaces and a consequent increase in flow velocity (Sugianto et al., 2022) triggers flood disasters within a specific locale (Charlton et al., 2006). Pakistan is located in the south Asian monsoon region (Ahmed et al., 2019). Northern parts of Pakistan often receive heavy rainfall from June to September and approximately, about half a million people are affected by flooding every year (Baqir et al., 2012).

This study used raster layers with consistent spatial resolution and projection, a best practice for geospatial modeling (Hong et al., 2018; Khosravi et al., 2016a), ensuring spatial consistency and minimizing errors in flood susceptibility modeling (Zhao et al., 2022). The integration of GIS-based MCDM and AHP provides a robust framework for assigning weights to variables, which is widely recognized as reliable for flood assessments (Aydin and Sevgi Birincioğlu, 2022; Hagos et al., 2022). If these considerations—consistent data resolution, appropriate projection, and the use of MCDM with AHP—had not been addressed, model accuracy would have been compromised, undermining the results’ reliability in capturing flood risk dynamics in Khyber Pakhtunkhwa (Ali et al., 2020; Aydin and Sevgi Birincioğlu, 2022).

This study assessed, quantify, and map flood prone areas in the Khyber Pakhtunkhwa (KP), Pakistan, which is one of the most vulnerable and first impact points of floods in the country. The study concluded that 9% of the KP province fell into the very high-risk area, spanning over an area of 923,257 hectares while 14% (1,419,480 hectares) was categorized as high-risk area. These high-risk zones are predominantly concentrated in the lower northern, and central densely populated districts of the study area. Based on our results, we recommend strengthening the flood risk mitigation policies, focusing primarily on the high-risk zones. Additionally, an early warning system is needed to be established across the study area, which will help minimize economic loss, injuries, and deaths due to the floods. The findings of this study will be valuable to policymakers, concern departments, and local communities in implementing safety measures and developing effective flood risk mitigation strategies.