Application of Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) in flood hazard analysis in the Rawa Pening Sub-Watershed, Indonesia

The watershed is one of the ecological functional units in a region. The Rawa Pening Sub-Watershed, located in Semarang Regency and its surroundings, is the upper region of the Tuntang Watershed, which experiences periodic floods. Flood hazard disasters occur due to several contributing factors. This phenomenon significantly affects the residents in the Rawa Pening Sub-Watershed, particularly in the agricultural sector, specifically in the local paddy fields. With the development of Geographic Information Systems (GIS) in disaster management, it is possible to provide preliminary information on predicting flood hazard zones, particularly in the Rawa Pening Sub-Watershed. This study aimed to determine the most influential parameters and areas highly prone to flood hazards in the Rawa Pening Sub-Watershed. Nine parameters, including elevation, slope, curvature, precipitation, land use, soil type, distance to the river, Normalized Difference Vegetation Index, and Topographic Wetness Index, were analyzed based on their respective roles. The Analytical Hierarchy Process (AHP) and Ordered Weighted Averaging (OWA) techniques were employed to obtain the flood hazard map. The nine parameters were weighted according to a Forum Group Discussion. The results yielded a flood hazard map divided into five zones: very low (3451,64 Ha / 13,16%), low (5024,29 Ha / 19,16%), moderate (6049,35 Ha / 23,07%), high (7949,67 Ha / 30,31%), and very high (3750,93 Ha / 14,30%).


Introduction
One of the disasters that occurs nearly every year in Indonesia is flooding.Floods are one of the disasters that can happen anywhere, across almost the entire land surface of this hemisphere [1].According to [2], a flood is when a river's flow overflows due to water volume surpassing the river's storage capacity, leading to water overflowing and inundating low-lying areas around it.According to statistical data from the website [3], regarding the distribution of disaster types and the number of casualties from 1815 to 2015, floods ranked first with 5,600 incidents and a death toll of less than 34,000 people.Furthermore, floods are also natural disasters with a frequency rate of occurrence of 34%, followed by strong winds.
Rawa Pening is a natural lake located in Central Java Province.Lake Rawa Pening is naturally formed through volcanic eruptions that carried basalt lava, blocking the flow of Kali Pening River in the Tuntang region.Consequently, the Kali Pening valley was submerged and transformed into a natural IOP Publishing doi:10.1088/1755-1315/1314/1/012114 2 reservoir with significant ecological importance [4].Rawa Pening transformed into a semi-natural lake after constructing the first dam on the upper Tuntang River between 1912 and 1916.This led to the rise of the water level in the marsh by utilizing the Tuntang River as the sole outlet.This increase in water level significantly impacted the ecosystem, including the degradation of tropical forests, the invasion of aquatic plants, the formation of floating islands, and the development of aquatic communities.The expansion of the lake was carried out in 1936, making the maximum water coverage reach around 2,667 hectares during the rainy season and around 1,650 hectares during the dry season [5].The Rawa Pening area covers four sub-districts with a total area of approximately 6,488.558hectares.These four sub-districts are Tuntang (2,163.478hectares), Bawen (483.900hectares), Ambarawa (961.840hectares), and Banyubiru (2,879.340hectares).The location map of the Rawa Pening sub-watershed can be seen in Figure 1.Based on a report by [6], hundreds of houses in Semarang Regency were submerged by flooding due to heavy rainfall on April 24, 2020.Krapah, Banyubiru, Demakan, Cerbonan, Rowoboni, Rowoganjar, and Candirejo were the most affected areas.This flooding occurred after four hours of heavy rainfall, causing Lake Rawa Pening to overflow and inundating the surrounding areas.Apart from residential areas, the floodwaters also covered ready-to-harvest rice fields.According to a [7] report, high rainfall on April 15, 2021, caused the flow of water into Lake Rawa Pening to increase, leading to its overflow onto agricultural fields in the Tuntang and Banyubiru areas.The affected paddy fields covered dozens of hectares, and the water level that inundated them reached 30-50 cm, making it unsuitable for planting rice.
The two news articles above show a similarity in the timing of the events, which is in April.The rainy season usually occurs between October and April, making these months suitable for researching flood hazards within that time range to ensure more accurate data results.Observing several flood phenomena in the sub-watershed of Rawa Pening, the author was motivated to conduct a study titled "Application of the Analytical Hierarchy Process (AHP) Method and Geographic Information System (GIS) in Flood Hazard Analysis in the Sub-Watershed of Rawa Pening."The goal of this study is to identify high-risk areas for flood hazards.This research utilizes a Geographic Information System (GIS) to combine, manage, and analyze data, ultimately producing outputs that can be used as a reference for decision-making in geography-related issues [8] [9].GIS can also be integrated with a Decision Support System (DSS), commonly referred to as a Spatial Decision Support System (SDSS) [10].SDSS can be modified and customized to match user requirements, enabling inexperienced users to utilize GIS in their decision-making activities [11] [12] [13].Furthermore, current technology allows collaboration among agencies for decision-making or management through web-based applications [14] [15] [16] [10] [17] or distributed services [18] [1] [10].Additionally, the Analytical Hierarchy Process (AHP) method, which assists decision-making through a hierarchical decision model using pairwise comparison matrices, is employed in this research to analyze suitable weights for determining flood-prone areas [19] [20] [21] [22].Digital technology has played and con-tinues to play a crucial role in the growth of virtual learning [23].This research aims to analyze parameters with the highest impact on flood disaster risk in the Rawapening Sub-Watershed using the Analytical Hierarchy Process (AHP) and identify areas with a high risk of flood disaster in the Rawapening Sub-Watershed.

Data collection for each parameter.
The data mainly used originates from secondary sources collected from various institutions.The Watershed District Boundary data published by the Ministry of Environment and Forestry (KLHK) was utilized to determine the watershed boundary.Several index parameters, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, curvature, and Topographic Wetness Index (TWI), were acquired through remote sensing data downloads from Landsat 8 OLI/TIRS and ASTER DEM via the USGS's earth explorer website.Data on soil types were obtained from the Indonesian Center for Agricultural Land Resources Research and Development (ICALRD) publications.They were employed to establish the parameters concerning the soil type's ability to retain water.To determine the precipitation parameters based on the annual average rainfall (mm/year) as input for hydrological analysis, precipitation data were downloaded from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) website.Spatial data, including rivers, road networks, and other fundamental spatial data, were obtained from the Geospatial Information Agency (BIG) through the Ina Geoportal Indonesia website service.

Processing of parameter data
The secondary data is processed to create nine flood hazard analysis map parameters.To create this map, weighting is required using the Analytical Hierarchy Process (AHP) method.The weighting is guided by the journal of [24]: The nine research parameters involve weighting by a Forum Group Discussion (FGD).This weighting is adapted from [24], who conducted flood research in the Tuntang Watershed and the Sub-Watershed of Rawa Pening, part of the upper Tuntang Watershed.The outcome of this weighting involves dividing each parameter into 3-5 classes.The weighting is carried out to determine the composition of each parameter when Ordered Weighted Averaging (OWA) is applied.

Techniques for analyzing data
The method of data analysis in this research employs a complex regional approach.According to [25], the complex regional approach emphasizes that a region is not merely an isolated entity but also a part of a system where various regional components are believed to be interconnected, mutually influential, and interact.The consequence of these interactions is that if one or more components change, it might also result in changes to other components.The complex regional approach encompasses spatial and ecological approaches.The issue of flooding is well-suited to be addressed using the complex regional approach, as it involves spatial cases with causes and effects stemming from the environment.The complex regional approach is highly appropriate for addressing flood issues because it includes solutions in terms of spatial consequences or losses caused by floods and ecological aspects that trigger the occurrence of flooding itself.

Results and Discussion
From the process of data acquisition to data processing, the resulting outcome comprises nine parameters, namely: elevation of the land is a measurement of the height of a location above sea level.Elevation has an impact on the occurrence of floods.The lower an area is, the higher the potential for flooding; conversely, the higher it is, the safer it is from flood disasters [26].The elevation parameter is sourced from ASTER DEM data.Then, it is categorized into five levels through reclassification processing.These levels include very low (1 -252.58 m), low (252.58-623.96m), moderate (623.98 -1007.35m), high (1007.35-1630.33 m), and very high (1630.33-3056 m).
Slope or land inclination is the percentage ratio between the vertical distance (elevation of the land) and the horizontal distance (length of the flat land).The gentler the slope inclination, the higher the potential for flooding; conversely, the steeper the slope, the safer it is from flood disasters [26].The slope parameter is sourced from ASTER DEM data, and its categorization into five levels is based on the Van Zuidam (1985) classification.These levels encompass very low (0 -2%), low (3 -7%), moderate (8 -13%), high (14 -20%), and very high (21 -65.62%).
The soil type in an area greatly influences the process of water absorption, commonly known as infiltration.Infiltration is the vertical movement of water within the soil due to gravitational potential.Physically, several factors affect infiltration, including soil type, soil density, soil moisture, and vegetation cover.The infiltration rate in the soil decreases over time as soil moisture increases [27].The higher the water absorption or infiltration capacity, the lower the flood vulnerability level.Conversely, the lower the water absorption or infiltration capacity, the higher the potential flood vulnerability [28].The soil type parameter is sourced from data from the Center for Agricultural Land Resources Research and Development (ICALRD) and then categorized into three levels based on the study area.These levels include very low (others / (latosol)), moderate (cambisol, mediteran, gleysol), and very high (Podsolic, Andosol).
Rainfall is the amount of rainwater that falls in a specific area within a certain period.The required rainfall for flood control design is the average rainfall across the entire relevant area, not at a specific point, commonly referred to as regional rainfall.The higher the rainfall, the higher the potential for flooding; conversely, the lower the rainfall, the safer it is from flood disasters [26].The precipitation parameter is downloaded from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) website and then categorized into three levels.These levels include moderate (2500 -3000 mm/year), high (3000 -3500 mm/year), and very high (> 3500 mm/year).
Land use will impact the flood vulnerability of an area; it plays a role in the amount of runoff resulting from rainwater that exceeds the infiltration rate.If the form and method of land use do not disrupt the natural balance, the sustainability of land productivity will remain assured.Conversely, suppose the form and land use method are incorrect, meaning land use does not align with the land's capabilities.In that case, the natural balance becomes disrupted, referred to as hazardous land conditions [29].Land with abundant vegetation will facilitate rainwater infiltration, and it takes more time for runoff to reach rivers, reducing the likelihood of flooding compared to areas without vegetation [26].The LULC (Land Use Land Cover) parameter is sourced from Geospatial Information Agency (BIG) data and then categorized into seven land usage types: water, trees, flooded vegetation, crops, clouds, built area, and bare ground.
The Topographic Wetness Index (TWI) is a method for quantifying the topographic control of hydrological processes [31].The spatial distribution of hydrological conditions can be mapped using this method.Topographic Wetness Index (TWI) quantitatively assesses the local topographic effects on rainfall-runoff [32].The Topographic Wetness Index (TWI) value describes the tendency of water accumulation on a slope based on the gravitational force that controls water flow [31].Topographic Wetness Index (TWI) can be effectively applied to identify flood-prone areas by mapping regions that experience ponding [33].The assessment of Topographic Wetness Index (TWI) is currently implemented using Digital Elevation Models (DEMs).The Topographic Wetness Index (TWI) parameter is sourced from United States Geological Survey (USGS) data and then categorized into five levels, including very low (2,83 -6,45), low (6,45 -8,22), moderate (8,22 -10,38), high (10,38 -13,61), and very high (13,61 -22,47).The nine parameters can be seen in Figure 2  Based on the weighting using calculations from the AHP calculator as guided by the flood research journal in the Tuntang Watershed [24], the highest weighting is assigned to the precipitation parameter, with a weight value of 25.4%.This weight value is subsequently utilized in the subsequent step, which involves the process of Ordered Weighted Averaging (OWA).Based on the weighting results, the subsub watersheds can be ranked in terms of their vulnerability to flood hazards.The order, from most vulnerable to least vulnerable, is as follows: Sraten sub-sub watershed (1,018.71894hectares / 3.83389%), Panjang sub-sub watershed (712.98047hectares / 2.68326%), Parat sub-sub watershed (628.59548hectares / 2.36569%), Ringis sub-sub watershed (395.31549hectares / 1.48775%), Rengas sub-sub watershed (301.30185hectares / 1.13393%), Kedungringin sub-sub watershed (191.74333hectares / 0.72162%), Lagi sub-sub watershed (164.82868hectares / 0.62032%), Rawapening sub-sub watershed (147.04003hectares / 0.55338%), Torong sub-sub watershed (114.62660hectares / 0.41339%), and Galeh sub-sub watershed (75.83873 hectares / 0.28542%).This ranking provides valuable information regarding the flood vulnerability of these sub-sub watersheds, with Sraten being the most susceptible and Galeh being the least vulnerable to flood hazards.The map resulting from AHP and Ordered OWA methods can be seen in Figure 3 as follows:

Conclusions
Precipitation from the annual rainfall data of CHRIPS, based on AHP calculator calculations, ranks first with a weight of 25.4%.Therefore, this parameter has the most significant influence on flood hazards in the Rawapening Sub-Watershed.Precipitation holds the highest weight because the average rainfall in the Rawapening Sub-Watershed ranges from 2,500 mm/year to >3,500 mm/year.The flood vulnerability classes for the precipitation and rainfall parameters are very high for rainfall >3,500 mm/year, high for 3,000-3,500 mm/year, and moderate for 2,500-3,000 mm/year.

Figure 1 .
Figure 1.(a) The majority of the Rawa Pening sub-watershed is located in Semarang Regency.(b) The Rawa Pening sub-watershed consists of 10 subsub-watershed segments.

Figure 3 .
Figure 3. Map resulting from processing the Analytical Hierarchy Process (AHP) and Ordered Weighted Averaging (OWA) methods on the nine parameters, producing a map with five levels of flood disaster vulnerability.