Geographic Information System Techniques Research Articles

Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study in Tam Ky City, Quang Nam Province, Vietnam

Changes in land use/land cover (LULC) play a critical role in effective natural resource management, monitoring, and development, particularly within the realm of urban planning. In the examination of Tam Ky city, Quang Nam province, Vietnam, spanning from 2000 to 2020, remote sensing and Geographic Information System (GIS) techniques were employed. The Landsat satellite data (Landsat 7 ETM+ for 2000, Landsat 5 TM for 2010, and Landsat 8 OLI for 2022) underwent analysis using the supervised classification method in ArcGIS 10.8 software to identify and categorize six primary LULC classes: water bodies, agriculture, settlements, vegetation, construction, and bare soil/rocks. The reliability of the classification was evaluated through k values, revealing high accuracy with values of 0.951, 0.953, and 0.950 for the years 2000, 2010, and 2020, respectively. Notable shifts in LULC were observed during the period from 2000 to 2020. The areas covered by vegetation and settlements expanded by 53 and 1300 ha, respectively, while water bodies, agriculture, construction, and bare soil/rocks experienced reductions of 466, 48, 413, and 425 ha, respectively. To facilitate a rapid assessment, the study also incorporated the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The trends identified in this study are consistently aligned with the results of the supervised classification. The identified changes in LULC pose a substantial environmental threat, and the study's outcomes serve as a valuable asset for future land use planning and management in the area. The method's high accuracy enhances the dependability of the results, making them crucial for well-informed decision-making and sustainable development initiatives.

An Overview of the Remote Sensing and GIS Techniques Application to Detect Changes in the Surface Area of Water Bodies in Iraq

The use of Remote Sensing data in monitoring water bodies and reservoirs is a new and advanced approach that helps to study large areas covered by water in a short time and at a reasonable amount compared to traditional methods. Accordingly, the Remote Sensing technique integrated with Geographic Information Systems (GIS) has been used in several studies to monitor changes in water surface body area locally and regionally. In this study, several studies in Iraq were reviewed, using Remote Sensing techniques with the help of GIS to detect changes in the areas covered by the water bodies using satellite images captured in different periods. Natural and artificial lakes represent a large proportion of the water bodies in Iraq, including eight (8) lakes distributed in different regions of Iraq. An evaluation of the hydrological system of the studied water bodies showed that the changes in the area and size of natural and artificial lakes are affected by political, economic, and climatic conditions as the areas increase and decrease over years.

The macroecology of butyrate-producing bacteria via metagenomic assessment of butyrate production capacity.

Butyrate-producing bacteria are found in many outdoor ecosystems and host organisms, including humans, and are vital to ecosystem functionality and human health. These bacteria ferment organic matter, producing the short-chain fatty acid butyrate. However, the macroecological influences on their biogeographical distribution remain poorly resolved. Here we aimed to characterise their global distribution together with key explanatory climatic, geographical and physicochemical variables. We developed new normalised butyrate production capacity (BPC) indices derived from global metagenomic (n = 13,078) and Australia-wide soil 16S rRNA (n = 1331) data, using Geographic Information System (GIS) and modelling techniques to detail their ecological and biogeographical associations. The highest median BPC scores were found in anoxic and fermentative environments, including the human (BPC = 2.99) and non-human animal gut (BPC = 2.91), and in some plant-soil systems (BPC = 2.33). Within plant-soil systems, roots (BPC = 2.50) and rhizospheres (BPC = 2.34) had the highest median BPC scores. Among soil samples, geographical and climatic variables had the strongest overall effects on BPC scores (variable importance score range = 0.30-0.03), with human population density also making a notable contribution (variable importance score = 0.20). Higher BPC scores were in soils from seasonally productive sandy rangelands, temperate rural residential areas and sites with moderate-to-high soil iron concentrations. Abundances of butyrate-producing bacteria in outdoor soils followed complex ecological patterns influenced by geography, climate, soil chemistry and hydrological fluctuations. These new macroecological insights further our understanding of the ecological patterns of outdoor butyrate-producing bacteria, with implications for emerging microbially focused ecological and human health policies.

Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques

AbstractGroundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth’s surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study’s framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India.

InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia.

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Groundwater flow simulation for artificial recharge: a GIS, remote sensing, and MODFLOW integration

ABSTRACT Groundwater (GW) emerges as a sustainable resource to meet water demand, yet current GW reservoirs fall short of projected population needs. Artificial GW recharge, bolstered by Geographical Information System (GIS) and Remote Sensing (RS) technologies, stands out as a pivotal solution. Examining Aurangabad district in Maharashtra, India the study uncovers distinct challenges: Aurangabad faces escalating water demand due to population growth, leading to GW over exploitation. Leveraging hydrogeological parameters, rainfall analysis, and GW availability, the study proposes tailored GW recharge techniques. Utilizing MODFLOW simulation, the study explores diverse recharge rates and existing GW availability, showcasing promising results. Integration of stream augmentation, bladder pillow tank methods, and dug well recharge is examined via GIS and RS techniques. Simulation outcomes project significant GW level increases (11.55–23.70 m) with varying recharge rates, effectively meeting region-specific water demand projections. The findings underscore the imperative of sustainable water management practices and technological advancements in addressing water scarcity crises.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Spatial modeling of micro‐scale carbon dioxide sources and sinks in urban environments: A novel approach to quantify urban impacts on global warming

AbstractUrban environments play a significant role in global carbon emissions and sequestration, necessitating a comprehensive understanding of their spatial distribution. This study presents a micro‐scale spatial modeling framework to elucidate the complex interplay between CO2 sources and sinks within urban settings. Utilizing advanced geospatial analysis, remote sensing data, and geographically weighted regression (GWR) modeling techniques, we provide a detailed characterization of emission patterns and identify the spatial distribution of carbon dioxide sequestration. Employing the bottom‐up method and geographic information system techniques, we quantified carbon dioxide emissions in Isfahan City, Iran, attributing 81.68% to stationary combustion sources (residential, commercial, industrial, and power plant sectors) and 18.32% to mobile combustion sources (road‐rail transportation, and non‐road transportation [agricultural machinery]). To model carbon sequestration, we calculated tree biomass using allometric equations and estimated carbon sequestration per tree unit. Subsequently, we employed GWR to map the spatial distribution of carbon deposition across the city. The results revealed an annual carbon sequestration capacity of 7,704 tons, equivalent to storing 28,275 tons of CO2. Our findings highlight the substantial contribution of urban areas to greenhouse gas emissions and the potential of urban green spaces to mitigate these emissions. The spatial modeling framework developed in this study provides a valuable tool for urban planners to optimize carbon management strategies and promote sustainable urban development. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Science as exploration in a knowledge landscape: tracing hotspots or seeking opportunity?

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in Geographic Information System (GIS) techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists’ topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress.

Criticism of the Effect of Green Cover Change on Air Quality with i-Tree Canopy (Bursa-Osmangazi Region Sample)

Anything that reflects the ethnicity of a society that has aesthetic and visual textures transferred from past generations to the present day, that is universally accepted, that contains abstract and concrete concepts, and that is intended to be preserved and transferred to future generations is called "cultural heritage". Today's urbanism continues to develop around the settlements of ancient civilisations, which are accepted as cultural heritage. Today, migration from villages to cities has increased and population density in cities has shown a rapid upward trend. The destruction of green areas due to rapid urbanisation has increased visibly and has made smart cities with green areas obligatory. In smart urbanisation, practical methods are sought to quickly identify green areas and determine the benefits provided by these areas. The most widely used software for measuring tree canopy cover is i-Tree Canopy, which allows urban planners to quantify the ecosystem services and benefits of tree communities and forests at multiple scales, including pollution reduction, carbon sequestration and storage, and runoff reduction. Within the scope of the study, the changes on green areas such as urbanisation, industrialisation, increase in transportation networks brought about by population growth in the historical city centre of Bursa and the negative and positive effects of these changes were examined. In the study, the time-dependent changes of green areas in urban heritage and urban planning were determined with GIS (Geographical Information Systems) techniques, and suggestions were developed for existing and new green areas to be planned in the city.

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.

Exploring GIS Techniques in Sea Level Change Studies: A Comprehensive Review

Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Modeling on comprehensive evaluation of groundwater quality status using Geographic Information System (GIS) and Water Quality Index (WQI): a case study of Bahir Dar City, Amhara, Ethiopia

Abstract Groundwater is the most important natural resource, and many people throughout the world rely on it for drinking, particularly in rural areas. The present study was carried out to assess the status of groundwater quality and to check its suitability for domestic purposes in urban centres of Bahir Dar City, Ethiopia. Twelve shallow wells were selected for sampling. The sampled water was taken during the dry and summer seasons of the year 2019/2020. pH, turbidity, EC, TDS, chloride (Cl−), nitrate (NO3−), phosphate (PO4−3), total hardness, and Escherichia coli were measured for the suitability analysis. Comparison of measured results with those of WHO and Ethiopian drinking water quality standards was done. Moreover, Geographic Information System (GIS) and Water Quality Index (WQI) data analysis techniques were applied in order to investigate the groundwater quality. The spatial distribution map showed that the city's core area had the poorest groundwater quality status. The WQI result obtained from the analysis showed that 41.67, 33.33, and 25% of the sampled groundwater has low, extremely poor, and unsafe quality for drinking purposes, respectively. The present study revealed that anthropogenic activities have a great impact on the quality of groundwater in the area, necessitating immediate mitigating actions.

Flood risk assessment using Neutrosophic Analytical Hierarchy Process (N-AHP) and GIS techniques in the Melet Basin (Türkiye)

Today, knowing the spatial distribution of flood risk using GIS (Geographic Information Systems)-based MCDM (Multi-Criteria Decision Making) approaches has been a topic addressed by many researchers. In this context, the current study focuses on the spatial distribution of flood risk using the N-AHP (Neutrosophic Analytical Hierarchy Process)-based GIS approach. The Melet Basin (Türkiye) is a case study for the suggested methodology. Four decision-makers used linguistic phrases to compare and assess the flood criteria during the method's application phase. The opinions of the decision makers were combined with the N-AHP, and the criteria were weighted. The results determined that precipitation, distance from the river, drainage density, land use, and slope were the most important factors affecting the flood and contributed approximately 74%. Apart from this, it has been observed that 10% of the basin is in the high and very high flood risk classes, and these risky areas overlap with the flood points of past periods. The proposed approach and findings are anticipated to have theoretical and practical implications.

Geospatial quality assessment of locally available ice for heavy metals and metalloids and their potential risks for human health in Karachi, Pakistan

Extreme hot conditions during summers, high poverty rate and continuous electricity load shedding affect commercial manufacturing and sale of ice in many countries. The vendors prepared ice using untreated piped water, tanker water and ground water. These waters may contain hazardous pollutants and ice made from them will pose a potential human health risk. Thus, it is important to regularly monitor the chemical composition of water sources and the quality of the manufactured ice. A contemporary examination was carried out to evaluate the physico-chemical properties and heavy metals and metalloids in the ice sold in all the districts of Karachi, Pakistan. This pioneering study was an innovative effort to assess the ice quality in relation to potential pollutant hazards to human health; with concomitant geospatial information. The geospatial distribution of ice quality and major constituents were among the measured parameters; carefully associated with further geospatial information, determined using GIS (Geographic Information Systems) and PCA (Principal Component Analysis) techniques. Interestingly, the physico-chemical analyses revealed that the ice quality was marginally adequate and the total mean metal-metalloid contents were in the sequence of Pb > Ni > Zn > Fe > Cr > As. The concentrations of these metals were above the upper allowable limits with reference to the recommended WHO guidelines. We observed that 57.1% and 35.7% ice samples had good physico-chemical properties assessed using the Ice Quality Index (IQI). Conversely, the IQI for metals showed that the ice was unsafe for human consumption. In terms of health risk assessment, the overall mean CDI (Chronic Daily Intake) and HQ (Hazard Quotient) values were in the order of Pb () > Ni (3.2) > Zn (2.3) > Fe (2.1) > Cr (1.6) > As (0.5) and Pb (7.4) > As (1.7) > Cr (0.5) > Ni (0.4 > Zn (0.008) > Fe (0.003), respectively. This study highlighted that routine monitoring of the water supplies available for making ice is required to protect public health.

Flood hazard mapping using a GIS-based morphometric analysis approach in arid regions, a case study in the Red Sea Region, Egypt

Flash floods are a major threat to life and properties in arid regions. In recent decades, Egypt has experienced severe flash floods that have caused significant damage across the country, including the Red Sea region. The aim of this study is to map the flood hazards in flood-prone areas along the Red Sea region using a Geographic Information System (GIS)-based morphometric analysis approach. To evaluate the flood hazard degree, the adopted methodology considers various morphometric parameters such as basin area, slope, sinuosity index, shape factor, drainage intensity, circularity ratio, and curve number. GIS techniques were employed to delineate the watershed and the drainage network. The delineated watershed was used together with the digitized maps of soil and land use types to estimate the curve number and the morphometric parameters for each subbasin. The flood hazard degrees are calculated based on the considered morphometric parameters and distinguished based on a five-degree scale ranging from very low to very high. Results indicate that 47% of the study area has a very high flood hazard degree. Furthermore, morphometric analysis results align with the runoff results simulated by a hydrological model, where, for example, basins with a high to very high hazard degree exhibited high runoff. This suggests the influence of physical characteristics on the hydrological behavior of the watershed and further validates the morphometric analysis presented in this work. The results presented here can help policy planners and decision-makers develop appropriate measures to mitigate flash floods and achieve sustainable development in arid regions.

Application of the fuzzy analytic hierarchy process for water resources in the Wadi AlHasa catchment, Jordan

This article introduces a groundwater vulnerability assessment model that utilises the fuzzy analytic hierarchy process (FAHP) in the Wadi AlHasa catchment, Jordan. The assessment takes into account both geomorphological and hydrogeological variables, employing a comprehensive methodology that integrates various parameters. To evaluate the catchment, the study employs remote sensing and Geographic Information System (GIS) techniques. The analysis of the digital elevation model enables the creation of a map illustrating the diverse geomorphology of the catchment. This geomorphology significantly influences drainage density, direction, and the spatial distribution and intensity of flash flood events. Moreover, the study develops and maps a fuzzy FAHP DRASTIC vulnerability index, which proves to be a valuable tool for assessing the susceptibility of groundwater resources to contamination. The unique feature of the index is its ability to incorporate uncertain or subjective data, providing a means to evaluate the significance of various influencing factors. This information serves as critical support for decision-making and management efforts geared towards safeguarding and enhancing groundwater resources. Within the study area, the DRASTIC vulnerability index values span from 0.08325 to 0.28409, with 18% of the site exhibiting a high vulnerability rate. Additionally, the article implements a managed aquifer recharge model (MAR), with 31% of the area falling into MAR classes. Among these, 22.1% are classified as a high MAR class, while 0.7% belong to a very high MAR class. These findings underscore the feasibility of MAR projects in regions with limited water resources.

SELECTION OF SUITABLE LANDFILL SITE FOR WASTE DISPOSAL IN ILORIN METROPOLIS USING GEOGRAPHICAL INFORMATION SYSTEM

Rapid urbanization globally has aggravated and worsen the management of municipal solid waste (MSW) in cities, especially the developing countries. This article underscores the pressing need for effective waste management solutions from the unruly contamination of soil and groundwater, or improper waste disposal and focuses on the selection of suitable landfill sites using Geographic Information Systems (GIS) techniques. With a global increase in MSW generation, especially in Sub-Saharan Africa, the environmental and health consequences of open dumping is hazardous. The study employs Multi-Criteria Evaluation (MCE) and Analytical Hierarchy Process (AHP) within the GIS environment, considering criteria such as elevation, slope, aspect, soil properties, proximity to roads, water bodies, and settlements. The analysis integrates diverse data sources, including remote sensing and soil databases, offering a comprehensive evaluation of topographical factors, soil characteristics, and accessibility. Results indicated that approximately 29.73%, falls under the category of “Restricted Area,” totaling 18,887.18 hectares. High Suitable areas cover 15.85% of the region, comprising 10,070.28 hectares, while “Very High Suitable” areas, though minimal in coverage at 0.02%, represent nearly perfect sites totaling approximately 12.35 hectares. The results offer valuable insights into landfill suitability, providing a foundation for informed decision-making and sustainable waste management strategies.