Geographic Information System Framework Research Articles

Integrating Indicators in Agricultural Vulnerability Assessment to Climate Change

AbstractClimate change stands as one of the most pressing challenges confronting global ecosystems and human livelihoods. The agriculture sector of Anuradhapura district, Sri Lanka, well renowned for its pivotal role in the nation’s food production, faces an increasing threat from the changing climate. This study aims to incorporate the indicator system method which uses a set of indicators to assess the agricultural vulnerability (AV) to climate change in Anuradhapura district. The AV assessment used in this study involves three principal components exposure, sensitivity, and adaptive capacity. The indicators are normalized to allow spatial analysis and smooth integration within a geographic information system (GIS) framework. The AV of Anuradhapura district ranged from 0.32 to 0.67 and was divided among five levels very low, low, moderate, high, and very high. The findings suggest that Anuradhapura’s agricultural sector was significantly impacted by climate change as the majority of the total area was found to have very high, high, and moderate levels of AV with 25, 28, and 24%, respectively. The results obtained from this study are essential for formulating plans to improve Anuradhapura’s agricultural sector resilience and adaptability to ensure food security and livelihood sustainability considering the ever-changing climate.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

Application of GIS in land unit mapping and evaluation of land suitability for agriculture in Uong Bi district – Quang Ninh province

This study aims to apply Geographic Information Systems (GIS) in land unit mapping and the evaluation of land suitability for agriculture in Uong Bi District, Quang Ninh Province, Vietnam. Given the complex and spontaneous changes in land usage driven by market fluctuations and environmental challenges, assessing land quality becomes crucial for sustainable agricultural development. Integrating soil, topographic, and climatic data within a GIS framework led to the creation of land unit maps for 40 different units in Uong Bi district, classified based on soil type, slope, soil depth, soil fertility, and hydrological conditions. The assessment of land suitability for seven main crops, including Litchi, Citrus, Dragon fruit, Cinnamon, Vegetables, Pine, and Macca, was conducted using the FAO framework for land evaluation. The results indicate that Pine and Cinamon have the highest arable area across all crops (with 15,895.40 ha and 15,502.40 ha respectively) while Vegetables show the lowest total arable area (4,063 ha). The findings provide valuable insights into the suitability of land for specific crops, facilitating informed decision-making to optimize land use and improve agricultural production.

Implementing a vegetation-based risk index to support management actions in Mediterranean coastal dunes

Coastal dunes play a crucial role in mitigating sea-related impacts and safeguarding coastlines. However, increasing human influence and natural factors such as sea-level rise underscore the need for effective coastal risk assessment methodologies. This study introduces a comprehensive coastal risk index covering 24 km of the Italian coastline within the protected area of San Rossore Park (Tuscany, Italy). The study area, distinguished by its notable coastal dune ecosystems, holds naturalistic, cultural, and economic importance. Nevertheless, diverse uses, zoning, and human impact variables pose challenges.By incorporating geological, socioeconomic, cultural, and ecological parameters, the index integrates a range of data sources and field observations. This research focused on developing and applying a vegetation-based risk index (VRI) within a geographic information system (GIS) framework, recognizing the ecological importance of dune vegetation in mitigating coastal erosion. Analysisrevealed varying risk levels within the study area. Half of the San Rossore Park coastline exhibited low risk values, 37.5% had moderate risk values, and 12.5% had high risk values. The publicly accessible northernmost section displays excellent preservation of dune habitats but faces heightened risk due to anthropogenic impacts. Conversely, the central-southern portion, inaccessible to the public, registers high-risk levels linked to variables associated with coastal erosion.Furthermore, the results highlight areas with heightened cultural and ecological vulnerabilities aligned with elevated risk levels. The index facilitates clear and intuitive cartographic representations of coastal risk, identifying variable categories that substantially influence on risk determination. Tailored strategies, including mitigating human pressure in the northern sector and implementing erosion management in the central-southern region, are recommended.In summary, this study not only provides a practical tool for assessing and managing coastal areas and directing attention to specific threats but also supports stakeholders in informed decision-making. The VRI enhances global sustainable coastal conservation, deepening our understanding of coastal risks and providing valuable insights for effective management strategies.

Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.

Sea Level Rise, Land Subsidence, and Flood Disaster Vulnerability Assessment: A Case Study in Medan City, Indonesia

Global sea level rise (SLR) has emerged as a pressing concern because of its impacts, especially increased vulnerability of coastal urban areas flooding. This study addresses the pressing concern of SLR and flood vulnerability in the East Coast of North Sumatra (ECNS) and Medan City. We employ a data-driven approach integrating multicriteria analysis, analytical hierarchy process (AHP)-based weighting, and spatial modeling within a geographic information system framework. The analysis considers crucial factors such as slope, land use, soil type, SLR, and land deformation. The study expands the existing framework by incorporating SLR and land subsidence, acknowledging their significant roles in exacerbating flood vulnerability. Future flood-intensity scenarios are simulated based on SLR projections. Data for spatial analysis primarily originated from multisensor satellite imagery, secondary sources from published literature, and field surveys. We validated the consistency of the variable weightings assigned for vulnerability analysis using a consistency ratio threshold (<0.1). Finally, the established flood vulnerability model was validated by comparing its predictions with recorded flood events in the ECNS and Medan City. The ECNS and Medan City areas were classified as very high and highly vulnerable to flooding, respectively.

Estimating Nitrous Oxide (N2O) emissions from managed soils at higher spatial resolution in the Republic of Ireland

This paper aims to define a high spatial resolution model for estimating nitrous oxide (N2O) emissions from agricultural soils in Ireland. In 2020, N2O emissions from the management of agricultural soils represented 10% of the total national agricultural Greenhouse Gas (GHG) emissions. The high spatial resolution model employed here takes account of environmental factors that influence N2O emissions at a more disaggregated spatial scale than the Intergovernmental Panel on Climate Change (IPCC) national inventory-reporting framework. Activity data from the EU Farm Accountancy Data Network (for Ireland) is used in conjunction with biophysical and climatic data in a geographic information system (GIS) framework to estimate N2O emissions at a sub-national level. Results indicate that N2O emissions are 5% lower by applying this high spatial resolution modelling approach at the farm level compared to the baseline model (Tier 2). Nevertheless, 25% of the farms in the sample analysed had an overestimation in their N2O emissions of 20%, and another 25% of the farm sample had an average underestimation in their N2O emissions by 19%, a consequence of the variation of environmental factors among farms. Using a data panel regression, results confirm that the environmental factors examined are statistically significant within the model proposed, and with a simulation, we assessed the switching of 20% of CAN fertiliser for protected urea. According to the high spatial model, this mitigation measure can reduce N2O emissions from inorganic fertilisers by up to 15%. However, the reduction is conditioned by local factors and environmental conditions.

Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea

With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) models were used to prepare landslide susceptibility maps of the mountainous Pyeongchang-gun region (South Korea) within a geographic information system framework. To map landslide susceptibility, datasets of landslide occurrence areas, topography, land use, forest, and soil were collected and entered into spatial databases, and 18 factors were then selected from the databases and used as model inputs. The training and test datasets consisted of 1600 and 400 landslide locations, respectively. The test accuracies of the DNN and DNNK models were 98.19% and 97.53% and 94.11% and 92.22% for the area under the receiver operating characteristic curve and the average precision value of the precision-recall curve, respectively. The location of future landslides can now be quickly and efficiently predicted using remote sensing data at a lower cost and with less labor. The landslide susceptibility maps produced in this study can play a role in sustainability and serve as references for establishing policies for landslide prevention and mitigation.

Utilization of Augmented Reality Technique for Sewer Condition Visualization

Wastewater pipelines are largely buried underground, and techniques for assessing and visualizing their condition are critical for planning and rehabilitation. This paper introduces a framework for integrating Geographic Information System (GIS), 3D-creation platform, augmented reality (AR) techniques, and machine learning algorithms for the dynamic visualization of the condition of sewer networks. A sewer network in Ålesund City, Norway, was used as a case study, and the developed framework was implemented on an Android OS and Microsoft HoloLens. The results show the potential applications of the integrated framework of GIS, AR, and 3D models for sewer condition visualization. The positioning accuracy of the application for 2D objects is equivalent to that of well-designed GPS receivers (approximately 1–3 m), depending on the handheld device used. Loading and locating 3D objects will be limited by the performance of the devices used.

Mapping the vulnerability of groundwater to saltwater intrusion from estuarine rivers under sea level rise

Surface water bodies connected to the ocean such as estuarine rivers can act as pathways for saltwater intrusion (SI, i.e., the displacement of fresh groundwater by saltwater in an aquifer) far inland of the coast, presenting one of the earliest risks associated with relative sea level rise (SLR). However, SI vulnerability mapping approaches have largely focused on SI from the coast and do not consider estuarine surface water bodies, except for GALDIT–SUSI (Kazakis et al., 2019); a weighted indexing approach designed for regional-scale mapping applications using a Geographic Information Systems (GIS) framework. However, GALDIT-SUSI is subjective in ranking the importance of factors that can contribute to SI and combining these into a vulnerability index. A less subjective approach to assessing SI vulnerability than weighted indexing methods involves the use of physically-based analytic solutions such as Strack (1976), but these have not been applied in a GIS framework or along estuaries and rivers previously. Here, these analytic solutions, surface water salinity, and surface water-groundwater freshwater head gradients are used in the development of a new SI vulnerability tier system. This new approach was applied in the low-lying coastal city of Ōtautahi Christchurch, Aotearoa New Zealand, along 70 km of coastal, estuary, and river margins under current sea level and SLR using GIS, then compared to GALDIT–SUSI. The main advantage of the SI vulnerability tiers method is that it considers surface water behaviour, e.g., increased saltwater encroachment up rivers under SLR, which exposed new areas of the aquifer to saltwater and increased SI vulnerability upstream. In contrast, GALDIT-SUSI does not consider surface water conditions and accounts for topography and groundwater level as the main SI vulnerability drivers in this application. The SI vulnerability tiers proposed here are more theoretically robust than GALDIT-SUSI and provide a physically-based, large-scale, relatively low-budget and rapid screening tool to highlight areas most vulnerable to SI under current and future conditions for further monitoring and management; a gap of national and international relevance.

Poster] Spatial modelling of midair collision risk using ADS-B data


 The airspace environment is a complex system that is only expected to continue increasing in complexity with the introduction of new types of air vehicles and operations, such as uncrewed aircraft, and the projected growth of air traffic volumes. This increase in complexity brings a need for investigating and developing new models of complex airspace environments to better understand their constituent parts. To address this need, this paper presents a methodology to create a geospatial model of complex airspace environments which can be used to study any geospatially distributed entity part of these environments. The methodology leverages Discrete Global Grid Systems (DGGS) for this purpose, a Geographic Information Systems framework previously utilized in geography and urban planning. The usefulness of the model is demonstrated on a case study geospatial entity which is the risk of midair collisions for many geospatially distributed points in an airspace region of interest. Since such a model needs to be able to work for any type of air vehicle and airspace region in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner, a rudimentary midair collision risk model was also developed which satisfies these requirements. Air traffic data from the OpenSky Network was collected and integrated in the geospatial model and the midair collision risk model was used to calculate the risk of collisions for many geospatially distributed points in the airspace for four scenarios of ascending airspace complexity. The results from these four scenarios showed that the proposed methodology can be used to study the risk of midair collisions for different points in the airspace for any type of air vehicle and airspace region of interest in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner.
 

Estimation of Runoff by SWAT Model in Noyyal River Basin

Accurate assessment of water balance components holds paramount importance for the sustainable management of water resources in river basins. This study specifically concentrates on the application of the Soil and Water Assessment Tool (SWAT) model to evaluate water balance elements within the Noyyal River basin, a tributary of the Kaveri River originating from the Vellingiri hills in Western Tamil Nadu grapples with escalating water stress brought about by both anthropogenic activities and climatic fluctuations.
 The methodology adopted encompasses the harmonious integration of diverse spatial and temporal datasets. These datasets are seamlessly incorporated within the confines of a Geographic Information System (GIS) framework. Through the process of watershed delineation 21 sub-basin was partitioned and which is further subdivided into 217 unique Hydrological Response Units (HRUs) are derived, each corresponding to a distinct hydrological reaction. The simulation employs 33 years of daily precipitation, coupled with minimum and maximum temperature records, enabling the continuous execution of the SWAT model for runoff prediction. The findings elucidate fluctuating runoff volumes over time, spanning from a minimum of 11.17 mm to a maximum of 610.32 mm. Notably, the annual average runoff stands at 206.7 mm, representing approximately 25.6% of the cumulative rainfall experienced within the Noyyal river basin.

Analysis of Correlation between Anthropization Phenomena and Landscape Values of the Territory: A GIS Framework Based on Spatial Statistics

The evaluation of anthropogenic impacts on the landscape is an issue that has traditionally been carried out from a descriptive or at least somewhat qualitative perspective. However, in recent years, the technological improvements provided by geographic information systems (GIS) and spatial statistics have led to more objective methodological frameworks for analysis based on quantitative approaches. This study proposes an innovative methodological framework for the evaluation of landscape impacts of the usual anthropization phenomena, using a retrospective spatiotemporal analysis based on geostatistical indicators. Various territorial indices have been used to assess the spatiotemporal evolution of fragmentation of the built-up urban fabric, the construction of roads or linear communication works and the changes in land use. These phenomena have been statistically correlated with objective indicators of the landscape’s intrinsic value. The analysis of said spatial statistical correlation has been applied to three different but neighboring environments in the region of Murcia, located in the southeast of Mediterranean Spain, providing interesting results on the objective impact of each of these phenomena on the landscape and depending on the boundary conditions.

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process.

Using multi criteria decision analysis in a geographical information system framework to assess drought risk

In this study we use the Mun river basin to demonstrate how a Multi Criteria Decision Analysis – Geographical Information Systems (MCDA-GIS) methodology can be used to assess drought risk. This paper not only provides a step forward in considering other elements such as land use change, climate within drought risk but also splits annual risk across three seasons (wet, cool and hot), previously not done. We also investigate how land use change, in the form of a/reforestation and changing crop varieties could potentially mitigate future risk.MCDA rankings from experts found that climatic factors such as rainfall, evapotranspiration and maximum temperature were the most significant. By splitting up the seasons we have been able to observe the temporal and spatial changes in drought risk at an increased detail, an important step in mitigating water security issue in the future. Results for cool months found an increased risk in the north and east (Surin, Si Sa Ket and Rio Et). With hot months finding increased risk in the east (Surin and Si Sa Ket especially) and west in Nakon Ratchasima. Whereas the wet season risk was greatest in the West (Nakon Ratchima, Khon Kean and Mara Sarakham). Differences in future land use scenarios compared to 2017 found that if current trends continued (BAU), the areas at risk from drought will increase. However, by changing land use in the form of a/reforestation (COB) or changing crop types (PRO), drought risk will decrease. Thus, the MCDA-GIS methodology serves as a great starting point, providing a high flexibility in data, meaning the methodology can readily applied to other case studies across the world.

A Decision Support GIS Framework for Establishing Zero-Emission Maritime Networks: The Case of the Greek Coastal Shipping Network

Sustainability of maritime operations is a topic widely considered in recent years, as the shipping industry attempts to limit its environmental impact and meet the decarbonization goals set by the International Maritime Organization (IMO). As alternative fuels and newer ship technologies are gaining interest, the shift to more environmentally friendly fleets is quickly becoming a reality. In this context, potential areas for such shifts need to be determined, to expedite decarbonization efforts and provide passengers with a more sustainable way of travel. Greece is an insular country, with a complex coastal shipping network connecting the mainland with the islands and being of paramount importance for their economic growth. Recognizing accessibility and decarbonization needs, this paper examines whether the Greek coastal shipping network (GCSN) can be restructured, by introducing zero-emission sub-networks operated by electric ferries. The aim is to propose a methodological framework for the spatial analysis and evaluation of coastal networks, with the implementation of exploratory spatial data analysis (ESDA) methods and determination of local indicators of spatial association (LISA) with the help of geographic information systems (GIS). The proposed framework provides insight on whether and where such a restructuring is possible, with the introduction of new transshipment port hubs in the islands from which electric ferries could operate, thus determining potential electrification areas with additionally high renewable resource potential. Final conclusions indicate that a potential electrification of certain parts of the GCSN could be possible, while results for GHG emissions reduced by the introduction of electric ferries are calculated.

Assessing the Impact of Land Use and Land Cover Changes on Aflaj Systems over a 36-Year Period

The aflaj systems represent unique irrigation technologies that have been implemented in the Sultanate of Oman. This innovative system, referred to as “falaj” in the singular form, is composed of a sophisticated network of underground tunnels and open-air channels designed to access shallow subterranean water tables, thereby providing water for residential and agricultural use. The aflaj systems have played a significant role in supporting sustainable water resource management in arid and semiarid regions, making a notable contribution to the socioeconomic development of the country. The alteration of land use and land cover (LULC) in arid and semiarid regions can have significant consequences for hydrological systems, affecting the ability of local ecosystems to manage fresh surface and groundwater resources. These changes are often caused by both natural and anthropogenic factors. To investigate the impact of LULC changes on aflaj systems in the northern part of Oman, we utilized satellite imagery, aflaj data, and spatial analytical and image processing techniques within the framework of geographic information systems (GIS) and remote sensing. In the first part of the study, we quantified the changes in LULC and their impact on aflaj systems in seven cities in Oman due to urban expansion. In the second part, we evaluated the effect of LULC on groundwater for four major aflaj between 1985 and 2021. The study area was divided into four primary LULC classifications: vegetation, bodies of water, metropolitan areas, and bare soil. The classification maps demonstrated a high overall accuracy of 90% to 95%, indicating satisfactory performance. Our results revealed a significant reduction in vegetation areas between 1985 and 2021, primarily shifting from bare soil (BS) to urban areas (UAs) and from vegetation cover (VC) to BS, due to the reduction of groundwater resources. Over the four study periods (1985–1990, 1990–2000, 2000–2013, and 2013–2021), the percentages of the total area of Falaj Al-Muyasser, Falaj Daris, Falaj Al-Maliki, and Falaj Al-Khatmeen that transformed from agricultural lands to UAs were 40%, 39%, 32%, and 8%, respectively. Our study highlights the need for appropriate land management and planning to ensure the most effective solutions are utilized to meet social and economic sustainability requirements. In conclusion, our study presents a comprehensive analysis of LULC changes and their impact on aflaj systems over a 36-year period, providing new insights into the potential effects of LULC changes on groundwater resources and offering a basis for informed decision making on land management in arid and semiarid areas.

Integrated GIS and Fuzzy-AHP Framework for Suitability Analysis of Hybrid Renewable Energy Systems: A Case in Southern Philippines

This study proposes an integrated framework for assessing the suitability of renewable energy systems, including wind, solar, hydro and hybrid wind–solar and hydro–solar, in the southern Philippines. The framework employs a combination of the Fuzzy-Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) techniques to evaluate various socio-environmental and techno-economic factors. Several suitability indices were developed and used in the analysis. The criteria used in the analysis are based on a comprehensive literature review and input from experts in renewable energy and micro-grid technology. The results indicate that energy production is the most important factor and the total suitable areas for hybrid wind–solar and hybrid hydro–solar systems are 126.60 and 629.02 square kilometers, respectively. This research provides valuable insights for decision-makers and potential investors in the renewable energy sector in the study area.

Seismic Risk Assessment of Nagpur City using the Geographic Information System

Earthquakes are the major threat to mankind accounting for almost half of the fatalities brought about by all of the natural hazards and desert their traces in societies for years. It is well known that seismic risk cannot be eliminated but minimized. Risk studies lay down the roadmap to prepare and minimize the aftermath of any disastrous event. Geographic Information System (GIS) has been proved as an effectual tool for seismic risk assessment study. This study presents the seismic risk assessment of Nagpur city using the GIS framework. Albeit per Indian Standard (IS) 1893-2016 [1] seismic zonation of Nagpur city comes under zone II (relatively low seismic hazard zone), the seismic risk being a combination of hazard, elements at risk and, vulnerability cannot be denied. Similar to other Indian cities the city of Nagpur had a heterogeneous development pattern. Even though Nagpur city is municipalized in well distributed wards, many intra wards distributions show housing clusters of different socioeconomic status. Therefore, using the satellite imagery different socioeconomic housing clusters were identified and mapped in the ArcGIS platform. Six socioeconomic clusters were identified using satellite imagery for Nagpur. From the field survey of randomly selected typical clusters, 14 prevailing Model Building Types (MBTs) were identified. Census data were used to find the population density and its distribution throughout the city. Seismic hazard has been estimated from IS 1893-2016 [1] as well as using the probabilistic hazard map of NDMA [2]. Selected MBTs were assessed for their seismic vulnerability, seismic risk in terms of casualties and damage is calculated. Finally, thematic maps showing damages arrived from the various approach are shown and discussed.

Flood Susceptibility Mapping Using an Analytic Hierarchy Process Model Based on Remote Sensing and GIS Approaches in Akre District, Kurdistan Region, Iraq

In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final flood susceptibility map was reclassified into five different classes of susceptibility to flooding: very low (16.64% of the study area); low (19.53%); moderate (38.92%); high (17.83%); and very high (7.08%). The area under curve values for the predicted rate and success rate of the AHP model were 0.956 and 0.971, respectively. Therefore, the results were accurate and reliable. The AHP model is a powerful method for fundamental for susceptibility mapping to mitigate the serious impacts of flooding and assist scholars, local governments and policymakers in future master planning.