Flood disasters have become the most frequent natural phenomenon due to climate change and environmental factors. There is a significant threat to human lives worldwide because most countries are susceptible to flood hazards, and it causes different types of damage, such as physical, social, and economic damage. Floods cause damage everywhere, especially in agricultural areas and infrastructure sectors near rivers. This phenomenon may be due to no proper mapping or preventive measures or different parameters, such as the drainage density and slope. Floods occur due to several reasons. Heavy rainfall is one of the significant reasons behind floods. Floods are caused when it rains heavily and discharge exceeds the capacity of rivers, dams, and canals [1
]. Flood risk modeling is an essential strategy for flood management and mitigation [4
]. Furthermore, some human factors affect recurrent floods, among which are changes in land use, channel manipulation, construction of bridges, barrages, agriculture practices in river beds, and deforestation [6
]. Therefore, initial measures should be taken to minimize flood hazard damage. Such susceptibility analysis needs to be done, and risk analysis should be performed as early as possible [9
Nowadays, susceptibility analysis has become famous in GIS (Esri Inc., Redlands, CA, USA) and remote sensing techniques [11
]. Remote sensing helps to gather information about features of topographic surfaces, the vegetation cover, the land, the effects of climate change, and many other relevant data of various regions. Simultaneously, GIS techniques help to prepare a spatial database by using remote sensing data for flood mapping. RS (Remote Sensing) and GIS (Geographic Information System) techniques have been useful for urban flood hazards throughout the world by using multi-criteria zoning decision analysis in the Argentinian province of Tucuman [13
]. For flood susceptibility, zonation images are gathered through remote sensing, such as Landsat 5 (Thematic Mapper), Landsat 7 (Enhanced Thematic Mapper Plus), Landsat 8 (Operational Land Imager), and Satellite Pour Observation Terre (SPOT) [14
]. The ANN (Artificial Neural Network) model for flood imitation with GIS was utilized in the Johor River Basin (Malaysia). The flood susceptibility mapping was predicted and validated using the frequency ratio model and GIS techniques in the Johor River Basin [16
]. In India, while analyzing Kosi river basin mapping of flood risk evaluation, [16
] used GIS and RS techniques. Furthermore, GIS is used for predicting spatial areas susceptible to flooding [12
]. It is also used for flood susceptibility analysis, as well as for its validations [13
]. In Iran, flood mapping using GIS-based frequency ratio models and flood susceptibility assessments was done in the province of Golestan [18
]. Therefore, numerous research works integrate GIS and RS techniques that were beneficial for mapping flood risk management.
The results of various GIS-based statistical analyses are more acceptable, accurate, and logical than only using a spatial database. The analytical hierarchy process (AHP) with GIS for the mapping of floods has become popular [20
]. The logistic regression [22
], Shannon’s entropy model [16
], decision tree [19
], ANN [18
], FRM [8
], fuzzy logic [19
], and AHP models were utilized for susceptibility mapping of floods. The AHP model is a widely used efficient technique that is easily utilized and understandable [18
]. Furthermore, for the bivariate statistical technique, frequency ratio (FR) is an important quantifiable method and acknowledged in the study of natural disasters [8
There have been sporadic disasters and emergencies in the last ten years due to flash floods, landslides, avalanches, and glacier lake outburst floods (GLOFs), resulting in the loss of human and animal lives, and partial or complete damage to infrastructure [28
]. Pakistan is exposed to various hazards, including waterlogging, riverbank erosion, floods, cyclones, earthquakes, drought, desertification, landslides, heatwaves, GLOFs, and water salinity. Pakistan has experienced 25 calamities from 2000–2015 in which floods, earthquakes, and landslides were the most common [20
]. During the last six years, there have been glacier outbursts in two major areas: upper Chitral Sonoghour and Booni. Recently, on 2 August 2013, unusually heavy rain in the upper pastures of Reshun village caused an unprecedented flash flood. Similarly, in July 2015, the heavy rainfall on the upper barren land of low altitude pasture above the village changed the rill into a heavy flash flood. Structures were destroyed, irrigation channels and linking roads were damaged, and gardens, crops, and orchards were washed away [2
Modeling flood susceptibility is one of the latest strategies used for dealing with flood disasters. The study area (Chitral District) experiences recurrent floods, which cause damage to infrastructure, standing crops, and earning sources, and even lead to human casualties. There is a lack of flood susceptibility assessment and mapping. Flood modeling could also be considered to develop routing of floodwater by considering the interaction of physical parameters. Flood risk modeling is essential in river management [33
]. It involves factors such as drainage density, slope, land use, elevation, rainfall deviation, lithology, and land use/cover. All these factors can be used with the help of Multi Criteria Analysis-Analytic Hierarchy Process (MCA-AHP) and FR to identify very high to very low susceptibility zones. This study was an attempt to model the risk susceptibility and zonation in the floodplain of Chitral District. With the integration of GIS, many hydraulic models have been used for flood hazard evaluation. Flood modeling is a central part of flood risk reduction that aims to reduce the weakness of components in danger. This study was pioneering research in its field since no such study has been carried out so far in Pakistan, especially in the study area. Hence, it will provide guidelines for policymakers dealing with flood hazards, particularly in Chitral District.
Due to its river, Chitral’s floodplain is highly susceptible to recurrent flooding during the summer season. Almost every year, severe damage is caused to standing crops and infrastructure, as well as causing animal and human casualties. Population settlements are encroaching toward risky locations in the study area. A considerable number of villages are next to the river catchment. One main problem is that there are no known accurate flood zone methods in the Chitral municipality that provide information about floods to the municipal department, leaving Chitral more vulnerable to significant environmental, economic, and social damage [34
]. Historical data indicate that three extreme and seven moderate flood incidences occurred between 2010 and 2015 in Pakistan’s Chitral region, which destroyed natural resources and thousands of lives. To emphasize this problem, attempts were made to research certain factors to better identify and forecast areas that are more vulnerable to flooding. This study was conducted to reduce flood disasters in the Chitral river of Chitral District, as no such study has been conducted in this area before. This research also identified the spatial pattern of flash flood hazards through AHP and FR models [35
]. This research highlights the flood hazards and provides information for flood risk management policies to policymakers or the local government of Khyber Pakhtunkhwa province, Pakistan.
The key objective of this analysis was based on developing and applying quantitative analysis techniques with the integration of GIS for flood-susceptible mapping in the Chitral region of Khyber Pakhtunkhwa (KPK), Pakistan, and to estimate areas at risk. The GIS, FR, and AHP analysis results were further used for the detection and spatial mapping of flood risk areas for the Chitral region. FR and AHP models were used to evaluate the possible areas that were flood-prone. These results will be beneficial for planners, researchers, and the local government for impact assessment to predict the flood zones in the future and mitigate the risk of flood by developing different strategies. Therefore, the study used FR and MCA by utilizing AHP with GIS to generate flash flood hazard zonation to specify the high-risk areas and identify the most critical factor responsible for flash floods in the study area.
6. Concluding Remarks
Accurate flash flood susceptibility maps must be used in flash flood management studies by governing departments and decision makers for effective flash flood prevention and organized growth of the Chitral District. This flood susceptibility mapping analysis was performed to identify specific areas that are at risk of flooding. The flood susceptibility map design’s essential purpose was to raise awareness among the public, municipal authorities, and other organizations of the risk of flooding. In this analysis, we used computational approaches for FR and AHP learning to forecast the probabilities of flash flood events. In total, eight flash flood conditioning variables (river size, drainage density, slope, elevation, rainfall, land use, soil types, and geology) were taken into account in the preparation and testing of the proposed models. To determine the maximum and minimum weights, the AHP technique was performed on the selected factors that mainly cause floods.
Furthermore, a frequency ratio was performed to analyze the past flood occurrence incidents based on flood- and non-flood-based points. We conducted a comprehensive study using multi-source geospatial data in this research; many limitations remain in this data configuration study. We used the publicly available ALOS-PALSER DEM spatial resolution of 12.5 m; a higher DEM resolution will offer a more accurate flood map that could be more valuable for the practical application of flood mitigation strategies. It was observed from the study review that selected flood-inducing factor weight values were high for distance from the river (0.245), rainfall variance (0.315), land use/cover (0.256), and soil clay content (0.521), which suggested that these were the most critical factors causing floods in the Chitral area. The SFWV was 0.1235 for slope angle and 0.325 for elevation, which also played a part in flooding as contributing variables. The study demonstrates that, compared to the topographic factors (elevation and slope), the climate (rainfall) and local-based factors have a much more significant contribution since the Chitral area is flat near the river Chitral. The validation outcome based on flood position points showed that the prediction accuracy was 81% and the success rate was 84%.
To assess its practical use in diverse terrains and habitats, this analysis needs to be applied to other locations. Dynamic shifts that can be caused by human activity in the form of changes in land use, infrastructure growth, and climate change was also included in this study. These changes can affect the normal hydrological cycle and thus flood patterns, especially flash floods in populated areas that affect the affected communities’ lives and property. However, more research on the estimation, prediction, and mapping of flash floods by applying other variations of hybrid artificial intelligence models in various fields using high-resolution geo-spatial data for improved development of maps of vulnerability to flash floods has considerable potential. Along with public knowledge, the development plan often proves to be an obstacle. However, a high-risk area that shows a great response using a variety of strategies, such as flood-proofing steps, flood emergency preparation, flood shelter facility, and evacuation planning, which can be forecast and identified through the creation and use of practical methodologies for susceptibility analysis, can display significant flood preparedness. Therefore, multiple computational models based on multiple criteria may be implemented to reduce the flood risk load in future studies.