Abstract

Flash floods cause substantial harm to the social and economic aspects of the affected countries. This is a significant problem in urban areas where drainage systems are inadequate and unable to withstand severe flooding. Understanding the specific regions that are susceptible to flooding is essential to implement strategies aimed at mitigating the risk. Detecting floods in ungauged basins is challenging. The current work aims to establish a practical method for identifying and mapping floodplain areas. We can use several tools, including the FLO-2D integration tool, Flood Risk tool, Geomorphic Flood Area plugin, and Quantum Geographical Information System (QGIS) with Hydrologic Engineering Centre River Analysis System (HEC-RAS) to efficiently and cost-effectively detect flood hazard zones. The QGIS tool, the Geomorphic Flood Index (GFI), is the most valuable tool for identifying flood-prone areas in cases where the areas are extensive and lack sufficient data. This tool offers high data analysis and cost calculation precision while using few resources. 

1 Introduction

Floods are intricate phenomena, with variations in frequency and nature of occurrence. A flood is the result of a significant increase in the volume of water in rivers and streams, causing the water to exceed the boundaries of their natural and man-made banks (Rostvedt et al. 1972). India experiences significant flooding from monsoons, which are a widespread hazard around the world (Domeneghetti et al. 2015). Floods can originate from a variety of sources, typically caused by multiple influential elements in the valley and offshore regions. The impact they have on the ecosystem is determined by the size and frequency of the floods. The United Nations (UN) defines a flood as a significant disruption that causes economic, societal, material, and environmental damage that surpasses the available resources to cope with them (Schanze 2006). Based on historical data, floods can be classified into three categories: ‘impact events’ such as floods, earthquakes, tropical storms, and volcanic eruptions; 'slow-impact' events like drought and starvation; and 'epidemic illnesses' such as cholera, measles, and plague (Getahun and Gebre 2015). However, typically, approximately 15% to 20% of rainwater passes into surface runoff and flows into rivers. The residual water infiltrates into the soil and encounters groundwater or is released back into the atmosphere by transpiration and evaporation from vegetation. Elements such as climate, slope, soil, rock type, and vegetation influence the rainfall-runoff ratio, which varies from 2% to 25%. Persistent precipitation can saturate the ground and the air, sometimes resulting in floods, as the excess water runs down, encompassing the entirety of the rainfall. We take prompt action based on timely and precise information on the event's magnitude to mitigate the impact of flood disasters (Merz et al. 2010). A flood event occurs when a significant volume of water exceeds the capacity of natural streams, canals, or the sea, causing water to overflow into places where drainage is inadequate. A flood occurs when an abundance of precipitation occurs without prior notification. Consequently, this leads to the overflow of lakes, dams, and rivers, causing significant harm to humans, infrastructure, and other organisms.

Field (2012) suggests that an increase in the frequency and intensity of severe events like floods will be one of the most significant effects of climate change. The objective of flood risk management is to mitigate the consequences of floods. Quantifying and evaluating the consequences of floods is crucial for determining how to minimize flood damage and assess the relative advantages and costs of different intervention options (Albano et al. 2014). Within this framework, we created a freely available analysis toolbox, a component of the open-source geographic information system Quantum GIS, to assess flood consequences and assist authorities in gaining a deeper understanding of and effectively managing flood risk (Mancusi et al. 2016). We do not design these tools to be commercially viable software programs. However, other parties can use them for assessment and demonstration. The "Flood Risk" prototype software application can compute and display the impact of a flood scenario on the population and property damage in a selected area. For instance, if there is a requirement to implement measures to guarantee the safety of various hydraulic structures, such as dams or levees, which could potentially pose a risk downstream, it is crucial to determine the criteria for prioritizing the interventions. When evaluating the outcomes of failures or accidents, it is crucial to consider the implications as a key factor in determining the priority of solutions. This is particularly accurate considering the restricted financial resources that are accessible.

The objective of this study is to conduct a comprehensive evaluation of the current literature about the utilization of the QGIS tool in the field of flood catastrophe management. The study examines the various categories of flooding, the factors that contribute to its occurrence, and the social and economic consequences. Additionally, it explores methods for reducing the risks and dangers associated with floods. The several forms of floods are listed below in detail:

1.1 River floods

The primary form of natural disaster is river flooding. Flooding occurs when rivers exceed their maximum capacity due to excessive water inflow. The excess water breaches the river banks and other protective barriers, flowing into lower-lying areas. River floods result in significant human casualties, damage to infrastructure, and economic losses for the nation (Jodhani et al. 2024a). Seasons do not limit river flooding; it can occur at any time. However, the most frequent occurrence of flooding occurs during periods of high rainfall in the monsoon season, as well as occasionally due to snowmelt. Rupinder (2008) defines a flood as a persistent and inevitable occurrence in rivers. Flooding occurs when high rainfall and precipitation, including melted snow, combined to inundate the river valley with water. Furthermore, intense rainfall from cyclones or tropical systems can also lead to river flooding (Jodhani et al. 2021).

1.2 Coastal floods

Coastal floods, which are natural phenomena, occur when the sea level rises significantly above the usual tidal level, flooding coastal regions. Storms, high tides, and tsunamis can trigger these occurrences. Coastal flooding can have severe implications, causing significant damage to both human populations and the ecosystem. Coastal floods pose a severe threat to infrastructure in densely populated coastal regions, such as cities or towns (Jodhani et al. 2024b; Jodhani et al. 2023a; Jodhani et al. 2023b). Residences, commercial establishments, and vital infrastructure may experience inundation, resulting in the relocation of inhabitants, financial setbacks, and interruptions to crucial services. Furthermore, coastal ecosystems, such as marshes and estuaries, are susceptible to the detrimental impacts of flooding. The incursion of saltwater into freshwater areas can have detrimental effects on local plant and animal life, causing disturbances to fragile ecological equilibriums. Climate change-induced sea level rise and alterations in weather patterns are expected to increase the frequency and severity of coastal floods. Increasing global temperatures cause polar ice caps to melt and saltwater to expand due to heat, which worsens the dangers of coastal flooding around the world. Therefore, it is essential to implement efficient coastal management methods, such as measures to protect shorelines, early warning systems, and sustainable land use planning, to reduce the effects of coastal floods and protect vulnerable coastal communities and ecosystems.

1.3 Urban floods

Urban floods occur when an abundance of rainfall surpasses the ability of urban drainage systems to handle it, resulting in waterlogging and flooding of city streets, buildings, and infrastructure (Rafiq et al. 2016). The floods are worsened by factors such as non-porous surfaces, insufficient drainage systems, increased urbanization, and intense weather events caused by climate change (Verma et al. 2024). Urban floods have a variety of complex and serious effects (Tinsanchali 2012). They can disturb transportation networks, resulting in traffic jams and delays, as well as causing harm to cars. Moreover, urban floods provide substantial hazards to public health, as the presence of polluted water can lead to the transmission of diseases and harmful substances. Urban flooding often leads to economic losses resulting from property damage, business interruptions, and infrastructure repairs. Urban flood mitigation efforts commonly entail a blend of infrastructure enhancements, such as the enhancement of drainage systems and the creation of retention ponds, as well as land-use planning measures, such as the preservation of natural water retention areas and the implementation of green infrastructure solutions like rain gardens and permeable pavements. Nevertheless, despite the implementation of these measures, the growing occurrence and intensity of urban floods emphasize the immediate need for all-encompassing, integrated strategies in urban planning, infrastructure development, and adaptation of climate change (Tandel et al. 2023). These strategies aim to construct more resilient cities that can withstand the difficulties presented by urban flooding.

1.4 Flash floods

A flash flood happens when there is unexpected and heavy rainfall. Floods are caused by high rainfall over a short period (Collier 2007). A variety of factors can cause flash floods, but heavy rainfall from thunderstorms is the main contributor. Flash floods may occur due to dam or levee ruptures. A variety of factors, such as the intensity, location, and distribution of rainfall, land use, topography, vegetation types and density, soil type, and soil water content, can determine flash flooding (Ozger 2017). Urban areas are susceptible to swift floods, primarily due to rainfall. Urban environments characterized by impermeable layers impede the rapid drainage of water, resulting in the swift movement of water toward lower-elevation locations (Jodhani et al. 2024c; 2024d). Flash flooding is a highly dangerous event characterized by the presence of a substantial volume of swiftly moving water.

1.5 Identification of Flood Hazard Areas

The identification of flood hazard zones is necessary to determine the specific locations that are at risk of flooding. Maps can be constructed that accurately pinpoint locations inundated by floodwaters by utilizing historical river data, information on previous flood volumes, and topographical data (Azharuddin et al. 2022). Flood hazard mapping is necessary for identifying locations prone to flooding and the elements that contribute to the risk, especially in the context of development plans. We implement various policies and guidelines to reduce and control hazards in specific locations. To develop a flood danger map, it is essential to have efficient and cost-effective administrative units that can accurately and swiftly prepare counteractive strategies. Obtaining aerial photographs and satellite images of the flood-affected area, both before and after the occurrence, is necessary to facilitate the process of adapting flood management strategies. These visual materials are essential for creating accurate and effective flood maps. We created a theoretical map based on this data, representing a flood with a return period of 10 years, a flood with a return period of 50 years, and a flood with a return period of 100 years. We also develop scale models to pinpoint locations vulnerable to flooding. Only synchronizing these models with flood occurrences promptly makes them effective. In response to the flood dangers in 1954, the Government of India implemented several measures and established multiple committees to address and oversee the issue of flooding in the country. To reduce the impact of floods, stakeholders and implementers have developed several guidelines to identify the most crucial areas affected. The National Disaster Management Authority (NDMA) is responsible for overseeing disaster management. We have used satellite images from IRS, LANDSAT, ERS, and RADARSAT for flood monitoring. Wide Field Sensor Images (WiFS) data from IRC-1C and 1D is suitable for improving flood monitoring accuracy, particularly on a geographical scale. We conduct the assessment of flood threats by integrating geographical, hydrological, and environmental data (Ozger 2017).

1.6 Importance of flood hazard assessment

An assessment of flood hazards is essential for comprehending and reducing the dangers posed by floods. The purpose of this tool is to identify regions that are susceptible to flooding, evaluate the potential consequences on communities, infrastructure, and the environment, and provide information for land use planning and disaster preparedness plans (UNDRR 2021). Assessments offer vital insights for designing successful flood risk management plans by analyzing elements such as rainfall patterns, geography, river flow, and infrastructure vulnerabilities. According to Sayers et al. (2013), they facilitate the implementation of early warning systems, evacuation strategies, and enhancements to infrastructure, which can effectively mitigate the loss of human lives and property damage caused by flood catastrophes. In addition, flood hazard assessments have a role in enhancing resilience to the impacts of climate change, particularly as shifting weather patterns increase flood hazards on a worldwide scale (IPCC 2021). Consequently, it is imperative to allocate resources toward thorough evaluations of flood hazards to promote sustainable development, mitigate disaster risks, and protect communities from the growing menace of floods.

2 Quantum GIS tools

Quantum GIS (QGIS), an open-source geographic information system (GIS) program, allows users to view, organize, modify, examine, and share geographical data. This software offers a wide range of tools for a variety of GIS activities, from basic mapping to sophisticated spatial analysis. The core of QGIS tools is their user-friendly interface, which enables users to effortlessly access and employ a wide range of features. QGIS offers a user-friendly interface for dealing with spatial data, suitable for both experienced GIS professionals and beginners. QGIS provides a wide range of tools for data input, including the ability to work with many file types, such as shapefiles, GeoTIFFs, and GPS data. Users can import data from various sources, such as online services like OpenStreetMap and WMS/WFS servers. This allows for the smooth integration of external data into GIS applications. QGIS offers a wide range of geoprocessing tools that are highly useful for manipulating and analyzing data. These technologies allow users to carry out spatial operations such as buffering, overlay analysis, and spatial joins, which make complex geographical analysis workflows easier. Furthermore, QGIS provides robust support for sophisticated geostatistical analysis, allowing users to interpolate data, execute spatial regression analysis, and undertake terrain analysis. QGIS provides tools for map creation, arrangement, and visual design. Users can generate visually attractive maps by customizing the symbols, labels, and arrangements. In addition, QGIS has map composer capabilities, enabling users to create high-quality maps suitable for presentations and publications.

For complete analysis and planning, a variety of data formats and sources are required in flood hazard assessment. Digital elevation models (DEMs) provide critical topographic data for understanding land characteristics and water flow direction. Hydrological models replicate river flow behavior and predict flood situations using data such as rainfall, soil composition, and land surface characteristics (Jha et al. 2024). River gauge data provides up-to-date or historical readings of river levels, aiding in flood prediction and surveillance. Land use/land cover maps aid in assessing surface attributes that affect water flow and flooding vulnerability (Rimba et al. 2017). Precipitation data, which includes records of rainfall and snowmelt, are crucial for evaluating the magnitude and timing of precipitation events that have the potential to cause floods.

Geographic Information Systems (GIS) widely employ geospatial data formats such as shapefiles, GeoTIFFs, and raster datasets for the storage and analysis of various data types. In addition, databases such as PostgreSQL quickly store massive datasets, making it easier to do geographical queries and analytics. The integration of multiple formats and sources enables a thorough comprehension of flood dangers, facilitating the implementation of efficient risk management and mitigation measures in areas that are susceptible to such hazards.

The process of incorporating diverse data sources into QGIS plugins might vary in terms of difficulty, contingent upon the format and compatibility of the data sources. QGIS has extensive support for several data types, such as shapefiles, GeoTIFFs, CSVs, and databases like PostgreSQL and SQLite. QGIS provides developers with a powerful API and comprehensive documentation that facilitates the task of accessing and processing various data sources within plugins. In addition, QGIS plugins can utilize Python scripting, enabling developers to effortlessly incorporate APIs, online services, and real-time data feeds. Nevertheless, difficulties may develop when managing proprietary or less prevalent formats that necessitate supplementary libraries or customized treatment. In general, QGIS's adaptability and assistance from the community make it reasonably simple to include various data sources in plugins, hence improving its usefulness for a wide range of spatial analysis and mapping tasks (Jodhani et al. 2023c).

Additionally, users can modify parameters and models within QGIS plugins to accommodate specific local conditions or scenarios. QGIS plugins frequently offer configurable settings and choices in their interfaces, enabling users to adjust factors such as input data sources, spatial extents, analysis methods, and output formats. In addition, developers can design plugins that incorporate adaptable algorithms and models, allowing for customization to different geographic, environmental, or administrative conditions. Users can utilize this flexibility to customize studies and simulations according to their individual needs, thereby making QGIS plugins highly adaptable tools for processing geographical data and making informed decisions in many applications.

2.1 FLO-2D tool

FLO-2D is a highly efficient flood simulation tool that is cost-effective and user-friendly. This model is advantageous for modeling intense urban flooding because it includes comprehensive information, including storm drainage. FLO-2D provides data regarding the preservation of volume, velocity, and numerical stability. This plugin is highly beneficial for modeling floods caused by heavy rainfall.

2.2 Flood risk tool

The flood risk GIS tool is advantageous for creating risk maps and assessing both current and projected flood risk due to its versatile applicability (Albano et al. 2017; Dhiwar et al. 2022). This plugin employs a framework based on 2D inundation modeling. It considers various return periods as inputs and determines the extent to which structural and non-structural solutions can reduce the financial impact of floods on households (Kale 2003). 

2.3 Geomorphic flood area

The QGIS tool, Geomorphic Flood Index (GFI), is the most valuable tool for identifying flood-prone locations when dealing with broad areas that lack sufficient data. This program offers high levels of accuracy in data analysis and cost calculation while using few resources. We determine the index by dividing the water depth in the river basin nearest to the research regions (derived using hydraulic scaling) by the elevation difference between these two places.

3 Flood Hazard: Conceptual tool

3.1 General: Flood hazard—Framework for assessing flood consequences

The definition of "hazard" is broad and encompasses various aspects of social, economic, environmental, and safety concerns. Various fields and contexts have used the term "risk", potentially leading to a misinterpretation of the technical terms used in risk assessment. It is helpful to think about a basic conceptual model to comprehend the relationship between hazard and risk: for a risk to materialize, there must be a hazard, which is composed of a "source" or initiator event (such as heavy rainfall); a "receptor" (such as flood plain properties); and a receptor's vulnerability (Gouldby et al. 2005). Although the presence of a hazard does not guarantee negative consequences, it does indicate that harm may occur. The degree of exposure to the risk and the receptor's properties determines the actual injury. Risk can be simple, and can be understood as "probability times damage," describing the expected damage that can occur or will exceed a certain probability in a specific period. Awareness of the consequences often captures exposure and vulnerability (Merz et al. 2010).

Figure 1 depicts the conceptual framework for flood hazard assessment. This architecture integrates the proposed GIS tool, which focuses on evaluating the repercussions and facilitating the execution of the operations outlined in the final section of the diagram. Thus, "Flood Hazard" can facilitate the identification of individuals and resources that are vulnerable to flooding, the strategic planning and assessment of efficient flood prevention and management strategies, and the development of flood risk maps to enhance public awareness.

Figure 1 Framework for flood hazard assessment.

4 Methodology

The Geomorphic Flood Assessment (GFA) tool categorizes results into two groups: areas that are susceptible to flooding and those not susceptible to flooding (Samuels and Gouldby 2009). Equations 1 and 2 display the specified index.

(1)

Where:

hr	=	Water level in the nearest element of the river network, and
H	=	Elevation difference between these two points.

(2)

Where:

A	=	Area affected by flood, and
r	=	Radius.

The key input data for this method includes a Digital Elevation Model (DEM) used to generate the flood index and a specific flood risk map often obtained by the ordered weighted averaging method. An ordered weighted average (OWA) method is used to create a flood hazard map through multi-criteria evaluation. The outcomes are dependent on the weight allocation for a specific criterion. We assign the weights based on the relative significance of one choice over another. Correspondingly, the criteria's values are linked to changes in order weight. They allocate attribute values in a diminishing pattern, based on various situations. We have completed the re-ordering process to provide ordered values based on specific weighted attribute values. We assign the initial weight value based on each location's highest weighted attribute values and subsequent weight values in descending order to the next highest values. The raster data model assigns the same factor weight to each pixel. These factors have explicit parameters that validate the objectives.

For the ordered weighted averaging approach, it is necessary to assign a weight value to each layer that is considered. Multiply the input layer by this weight value. To generate the final output map, combine all layers and select the "SAGA geo algorithm" from the processing tools. Then, click on "Grid Analysis." This completes the process of assigning the output map name and implementing the ordered weighted averaging approach (Seejata et al. 2018). The tool utilizes data to analyze physical characteristics to calculate various intermediate variables for each basin area (each pixel of DEM). These variables include, i) the change in surface elevation, G; ii) the drainage network; iii) the hydrological paths; iv) the difference in elevation between a given location and the nearest point on the drainage network, H; and v) the areas near the drainage network, Ar. We use the outputs to estimate the GFI and then normalize the resulting values within the 1:1 range (Seejata et al. 2018). Flood maps are generated by applying a threshold value, τ, to the flood index. We use this parameter to validate and compare the flood map with standard flood maps. This evaluation assesses the given presentation by examining the rates of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). The threshold values serve to differentiate the flooded areas from the non-flooded areas within a certain basin. This value is used to precisely identify large basin areas that have been impacted by flooding.

4.1 Analysis methods

The study employed a remote sensing technique utilizing the Quantum GIS program (Figure 2). QGIS utilizes data from the Shuttle Radar Topography Mission (SRTM) and the Digital Elevation Model (DEM) for the study. This data was carefully examined and modified to meet the mapping projection's requirements, using the UTM WGS 1984 coordinate system (Ariyani 2023).

Figure 2 Methodology.

4.2 Digital Elevation Model (DEM)

A DEM serves as a foundation for determining various topographical characteristics of watersheds, including catchment area, slope, river flow, and land elevation (Janizadeh et al. 2021; Elmoustafa 2012). Every watershed possesses distinct topographic features that additionally impact the gradient, river discharge, and altitude.

Slope factor

Slope is a significant factor contributing to flooding. Surface inclination can enhance the accuracy of threat indicators for flash flooding in susceptible regions. According to Elmoustafa (2012), the slope of the land affects how sensitive a watershed is to the velocity of water flow.

River discharge

Using the buffer method, the river flow analysis determines the appropriate distance from the river. Lee et al. (2021) employs the buffer method to control the river's dispersion within the designated right and left buffer zones. The implementation of water safety distance can be viewed as a qualitative method that enhances access to open space. This approach benefits different groups of humans and ecosystems by effectively managing flood dangers (Münch et al. 2016).

Elevation factor

Land elevation refers to the vertical distance observed from sea level and is often measured in meters or feet. Five distinct classes divide the elevation categorization. Areas with lower elevations have a higher susceptibility to flooding, while sites situated at higher elevations offer greater safety from flood disasters. This occurs because the water moves about the lower elevation areas. Low-lying locations are more prone to flooding due to their geographical position and higher altitude, as stated by Seejata et al. (2018).

4.3 Landsat 8

NASA launched Landsat 8 in 2013, which plays a crucial role in the field of Earth observation. NASA and the US Geological Survey (USGS) jointly manage this satellite, which plays a crucial role in monitoring and comprehending the constantly changing surface of our planet. Landsat 8 utilizes the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) to capture high-resolution multispectral images with exceptional precision and level of detail. The extensive data provided by this resource enables a wide range of applications, such as agriculture, forestry, urban planning, and environmental monitoring. The 30-meter resolution imagery of Landsat 8, together with its worldwide coverage and consistent data collection over time, allows scientists, governments, and industries to observe changes in land use, monitor natural disasters, and evaluate the effects of climate change. Landsat 8 plays a vital role in Earth observation, helping us get a better understanding of the planet and make important decisions for its sustainable management (Tian et al. 2015; Deng et al. 2019; Breinl et al. 2021).  

4.4 CHIRPS

The CHIRPS database is a comprehensive compilation of infrared rainfall data that combines three different sources of rainfall information: global climatology, satellite rainfall predictions, and in-situ rainfall observations (Sahu et al. 2024; Sahu et al. 2022). This study utilizes the CHIRPS map to visualize the millimeter-measured precipitation levels in the watershed. The magnitude of precipitation is crucial in determining the subsequent flow of water during a flood, both in practical and theoretical terms (Breinl et al. 2021).

4.5 Digital Soil Map World (DSMW)

To assess the various soil types in the Bangko and Masjid watersheds and their susceptibility to rainwater penetration. The properties of soil, such as its type, texture, and permeability, dictate the extent to which rainfall can infiltrate before reaching its capacity and contribute to flood vulnerability. Certain soil types can produce runoff rapidly, even in arid conditions, without allowing water to penetrate the soil. Clay exhibits greater velocity and coverage of runoff compared to sand when subjected to intense rainfall (Butt et al. 2015; Elkhrachy 2015).

5 Current challenges, possible solutions, and future approach

The main objective of this study is to develop a streamlined approach or method that can generate accurate flood susceptibility maps in locations with limited data and for large-scale applications. The geomorphic flood plug-in in QGIS is a valuable tool for identifying areas affected by floods in environments with little data. This plug-in, predicated on the DEM, requires a flood danger map to comprehend the places that have been impacted by a flood event. The correctness of this method is entirely contingent upon the quality and resolution of the digital elevation model utilized. To accurately determine the extent of a flood plain, it is necessary to have a high-resolution digital elevation model and a precise flood risk map for validation purposes. This approach is highly advantageous in situations where data is scarce, and one needs to utilize freely accessible DEMs that have a reasonable level of resolution. In data-scarce environments where flood hazard maps are lacking, one can utilize the ordered weighted averaging approach in QGIS, together with the GFA tool, to create a flood hazard map. This map will effectively identify locations that are prone to flooding.

6 Conclusions

QGIS tools are highly valuable for conducting a preliminary and efficient delineation process that is cost-effective, requires minimal computational time, and has straightforward data requirements. Interlinking conventional maps might be utilized to bridge the gap. It can also be utilized in unmeasured basins, and GFI tools can be advantageous. The GFA tool was used to identify and recognize flood-prone areas. It has multiple applications in geomorphology and hydrology. Using this tool is very beneficial for assessing danger areas over a wide expanse. The identification of flood hazard areas provides critical information to aid and implement effective measures to mitigate the effects of floods on human lives, infrastructure, and the district's economy. However, an advantage of the plugin is its intuitive interface and adaptable features, enabling users to efficiently modify settings and models to suit certain local situations or scenarios. The adaptability of this tool makes it useful in different geographic and environmental situations, helping to make well-informed decisions and prepare for disasters. Furthermore, the plugin's capacity to process several data types, such as Digital Elevation Models (DEMs), hydrological models, and land use maps, guarantees a thorough examination of flood vulnerability and consequences. It helps in the development of proactive measures for reducing flood risk and planning responses by enabling the integration of real-time data and offering visualization tools.

6.1 Future scope of the study

The future potential of the work on "Detection of Flood Hazard Based on QGIS" is highly promising in multiple domains. First, future research can prioritize improving the precision and dependability of flood hazard detection algorithms within the QGIS platform. This could entail the utilization of sophisticated machine learning methodologies or the integration of live data streams to enhance the accuracy of forecasts. Moreover, there is significant potential for extending the range of applications for QGIS-based flood danger detection beyond its current limitations. This may entail modifying the process to suit diverse geographical places with differing environmental conditions and infrastructural configurations. In addition, investigating the incorporation of QGIS with other developing technologies, including remote sensing, the Internet of Things (IoT), and artificial intelligence, has the potential to enhance flood monitoring and management systems by making them more comprehensive and efficient. Furthermore, the integration of community participation and participatory mapping methodologies in the study should enhance comprehension and reduce flood threats at the local level. Finally, the implementation of user-friendly interfaces and decision support tools in QGIS has the potential to empower stakeholders, policymakers, and disaster management agencies to make well-informed decisions and take proactive measures to effectively reduce flood risks.

6.2 Limitations of the study

Several restrictions can apply to the "Detection of Flood Hazard Based on QGIS" study. The accuracy of flood hazard mapping is highly dependent on the quality and resolution of input data, such as terrain elevation models and hydrological data, which might vary in availability and accuracy. Moreover, the accessibility of past flood data for verification and adjustment can influence the efficacy of the methodology. Moreover, the geographical and climatic circumstances of the studied location may limit the study's relevance, as flood patterns may differ greatly between regions. The computational resources required in resource-constrained contexts may restrict the processing of huge datasets and the running of complex models. Finally, the study's findings may be questionable because of the assumptions and simplifications made throughout the analysis. This emphasizes the need for strong sensitivity analysis and methodologies to quantify uncertainty.

Acknowledgments

The authors express their sincere gratitude towards the National Institute of Technology Raipur (C.G.) throughout the study.

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