Geographic Information System Techniques Research Articles

Impact of land-use/cover change on water quality in the Mindu Dam drainage, Tanzania

Abstract Water pollution caused by land-use/cover change is one of the most pressing problems facing both industrialized and developing countries. Freshwater quality deterioration suggests the collective outcomes of natural courses and alterations in land-use/cover. Understanding the correlation among land-use/cover and water quality parameters is vital for future water quality management. In this work, land-use/cover pattern and its effect on water quality in the Mindu Dam drainage area were analysed using Remote Sensing (RS) and Geographical Information System (GIS) techniques together with cellular automata–Markov model. The land-use/cover images between 1990 and 2020 were used to assess historical and spatial change of land-use/cover change, and projected for 2030 and 2040. We discovered that the dynamics of land use and land cover during the study period were significant. A strong correlation was revealed between changes in the land-use/cover and water quality parameters. Furthermore, a strong correlation exists between cultivated land and measured nutrient (nitrate and phosphate) and chlorophyll-a concentration. The natural vegetation buffer around Mindu Dam should be sufficient to prevent long-term water quality degradation from agricultural runoff in order to manage water quality sustainably. Therefore, land-use/cover management practices must be considered for sustainable resource management and water quality monitoring.

An investigation of the bio-medical waste produced in India during the COVID-19 pandemic and Maharashtra state (pre-COVID-19 and post-COVID-19) analysis: a GIS-based approach

BackgroundThe COVID-19 pandemic exerted substantial pressure on global healthcare systems and facilities, putting the lives of countless individuals at risk. In addition, the treatment of patients during the pandemic resulted in an unprecedented increase in the volume of medical waste generated, including biomedical waste (BMW) or healthcare waste (HCW), which poses a risk of infectious disease transmission. As the second most populous country in the world, India faced a severe challenge in managing its healthcare waste infrastructure during this time (2020–2021). Proper disposal of BMW was of utmost importance to prevent the spread of infectious agents and to safeguard public health.MethodsThe environmental monitoring and management framework of the country is well planned and governed by the Central Pollution Control Board (CPCB), which carefully handles the BMW across the states and union territory of the country. Through the execution of Android based application named ‘COVID19BMW’, India has laid the foundation of identification, classification, data collection, and management regarding the BMW. Further, the temporal scale of BMW generation tracking was further improved from a monthly to a daily basis by using the COVID19BMW tool. This data was used to map the change taken place across the India. Additionally, by using Geographical Information System the BMW is mapped using Choropleth method.ResultsThe current study conducted a national-level analysis of BMW generated during the COVID-19 pandemic in India. The results revealed that, in the year 2020, the seven states and the National Capital Territory (NCT) of Delhi generated the highest amounts of BMW, with Gujarat, Maharashtra, Kerala, Karnataka, Tamil Nadu, Uttar Pradesh, and West Bengal being the top BMW generating states. Additionally, the change detection equation was used to map the changes. The investigation analysed the daily changes in BMW generation between 2020 and 2021 at the national level. The results indicated a significant decreasing trend of -50.35% in BMW generation per day. In the case of Maharashtra state, the change detection analysis for the pre-COVID-19 and post-COVID-19 pandemic periods showed an increased trend of approximately 32%. However, in 2021, a decreasing trend was observed, with a -2.23% reduction in BMW generation compared to 2020 on the daily basis of BMW generation. These findings suggest that the COVID-19 pandemic has influenced BMW generation of waste, and the results can provide insights for improving waste management policies and practices.DiscussionIn this study, a Geographical Information System (GIS) was employed to create a mapped representation of the BMW data at national scale. Further, the study investigated changes in BMW generation in Maharashtra state during the COVID-19 pandemic. Analysis of changes in BMW generation revealed a pattern of BMW generation during the pandemic. The use of GIS technology to track these changes proved to be a valuable tool in providing a synoptic view of the overall BMW condition across India and identifying areas where infectious waste poses a significant threat. The visualisation of data using the GIS technique provided an easy means of identifying hotspots of BMW generation, which is more effective compared to a tabular format.

IMPACT OF SUSPENDED SEDIMENT ON PAHANG RIVER DEVELOPMENT USING GEOGRAPHIC INFORMATION SYSTEM

The measurement of different characteristics of a stream, including integrated water resource management, is dependent on sediment transport mechanisms. On the Pahang River, studies explored the spatial interpolation pattern of suspended sediment (SS) and water resource management. Sedimentation issues in the Pahang River have a significant impact on water resource management in the Pahang River basin. Furthermore, it may have an impact on local water consumption, recreational activities, and other factors, causing the river to become shallow and finally flood. This study was conducted to determine the SS pattern in the Pahang River with the approach of the Geographic Information System (GIS) technique and its significant colour based on spatial analysis. In addition, this study also evaluates the factors and effects of sedimentation through water source management. Three sampling stations from the Department of Irrigation and Drainage (DID) for three years (2000, 2004 and 2008) were selected along the Pahang River, where the parameter measured was suspended sediment (ton/year). The results obtained showed that the Pahang River receives a high amount of SS each year, where the higher amount was at the upper station (Sg. Yap), with an amount of 1876575 ton/year (2000), 613850.1 ton/year (2004) and 3458097 ton/year where it may be affected by sediment re-suspension and runoff from two outlets. Meanwhile, the downstream station (Lubuk Paku) received the least amount of SS, while the midstream station (Temerloh) received the most. The transit's speed and current may have an impact. This study's findings are critical in river and water resource management, especially of water resources for domestic use, ecotourism, river biodiversity, and hydrology.

Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine

Stream sediments from mine area are a converging source of water and soil pollution. The risk and development trends of metal(loid)s pollution in sediments from an abandoned arsenic-containing mine were studied using modelling techniques. The results showed that the combined techniques of geographic information system (GIS), random forest (RF), and numerical simulation (NS) could identify risk sources and diffusion trends of metal(loid)s in mine sediments. The median values of As, Cd, Hg, and Sb in sediments were 5.01, 3.02, 5.67, and 3.20 times of the background values of stream sediments in China, respectively. As (14.09%) and Hg (18.64%) pollution in mine stream sediments were severe while As is the main potential risk source with a strong spatial correlation. High-risk blocks were concentrated in the landfill area, with the surrounding pollution shows a decreasing trend of “step-type” pollution. The risk correlation between Hg and As (55.37%) in the landfill area is high. As a case of arsenic, the diffusion capacity of As within 500m is strong and stabilizes at 1 km when driven by the flows of 0.05, 0.5, and 5 m3/s, respectively. With the worst-case scenario flow (86 m3/s), it would take only 147 days for the waters within 3 km to become highly polluted. The high pollution levels in a stream under forecast of different distance intervals (500, 1500, 2000 m) within 6.5 km is arrived at approximate 344, 357, and 384 days, respectively. The study suggested the combined technique of GIS, RF, and NS can serve the risk source identification of contaminated sites and risk forecast of toxic element diffusion in emergency situations.

Coordinated analysis of county geological environment carrying capacity and sustainable development under remote sensing interpretation combined with integrated model

Geological environment carrying capacity (GECC) is the key to regional sustainable development (RSD). The study has become an international heat due to frequent global geological disasters, which have resulted in socio-economic losses, weakened GECC. Therefore, this study combined with quantity of information (QI), random forest (RF) and XGBoost (XGB) algorithms, constructed a GECC assessment model in Xiuyan, China based on remote sensing (RS) and geographic information system (GIS) techniques. The validity of the model was verified by disaster information in two period. The results reveal that the indicator system of RS image can reflect the geological factors’ influence on GECC more truthfully, and avoided duplication of indicators. The correlation coefficients of the indicators were all less than 0.9, which showed the validity of the indicator system. The QI_RF model performed best with high accuracy (0.96). The mean absolute error (MAE) is 0.098. The disaster management cost (DMC), elevation and rainfall were the main indicators with weights over 0.1. The GECC in study area is mainly balanced. Although the overloaded area was smallest, it was the key area to limit the RSD, which had been improved by adjusting the land or human activities planning. Compared with the results of geological disasters in two period, the disasters in overloaded area were reduced by 58%, which showed that the improvement measures under this model were effective. The method of GECC assessment based on RS and integrated model proposed in this study takes into account the common indicators that affect the development of geological disasters. It can provide reference for RSD under the influence of geological disasters and has universal applicability.

Natural radioactivity for soil samples in primary schools at Najaf city, Iraq

Natural radiation is the most significant contribution to the annual exposure of the world population, making it essential to assess the quantity of natural radiation in soil. This research aims to assess the level of natural radioactivity within the soil samples taken from primary schools in Al-Najaf, Iraq using gamma-ray spectroscopy. Specific activity was determined for the 238U series (214Bi), 232Th series (218Tl), 40K, and 235U. Twelve radiological hazard indices were computed. SPSS software version 23.0 was employed for data statistical analyzes to include average, standard error, standard deviation, box plot, frequency distribution, and Pearson's correlation coefficient. The results of the concentrations of 238U, 232Th, and 40K, were drawn using a geographic information system (GIS) technique. The results showed that the average values and standard error of 238U, 232Th, 40K, and 235U were 20.1 ± 0.65 Bq/kg, 11.5 ± 0.22 Bq/kg, 330.9 ± 7.1 Bq/kg, and 0.926 ± 0.03 Bq/kg, respectively. The results of 238U, 232Th, 40K, and 235U were compared to the average world value. This indicates that some schools' 238U and 40K levels had exceeded the acceptable worldwide levels. At the same time, the results of radiological hazard indices were seen to be within the permissible worldwide levels. As a result, the elementary schools under study can be argued that they are relatively free of natural radiation threats. The resulted data of the current research might be contributed to the database of natural radioactivity levels and radiation doses received by those who come into touch with these schools.

ASSESSMENT OF CROPLAND TRANSITION TO SOLAR FARMS AND OTHER LAND USE/COVER USING RS, GIS AND ANN-CA

Abstract. The balance between food security and energy security is a national issue of extreme importance. A more stable supply of electricity could be achieved as solar farms expand but at the expense of losing some of the prime agricultural lands which endangers availability of sufficient agricultural produce. This study aims to use ANN-Cellular Automata (CA) via the Geographic Information System (GIS) platform and remote sensing (RS) data to assess the impact of cropland transition to solar farms and other land use/land cover (LULC). Several remotely sensed data were processed including MODIS land cover data (MCD12Q1), VIIRS nighttime lights (VNL v2.1), Advanced Himawari Imager Shortwave Radiation (AHI-SWR) product, and population density (LandScan) as inputs to the Cellular Automata-Artificial Neural Network (CA-ANN) model to simulate LULC changes in Tarlac Province, Philippines via the Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS. For years 2019, 2023 and 2027 with 2015 as the base year, results showed an increasing trend for savannas and grassland with ΔLULC values of +11.4% to +15.1% and +0.2% to 3.5%, respectively. Meanwhile, a decreasing trend is observed for built-up/water, forest, and cropland with ΔLULC values of −3.0% to −6.3%, −8.5% to −21.1%, and −3.9% to −4.2%, respectively. Results also showed a conversion of a 100-ha area of croplands to solar farm from year 2019 to 2023 which translates to an estimated monetary loss from agricultural produce due to solar farm conversion amounting to Php 7,584,720.00 (~USD 138,000) which is equivalent to the total average annual income of about 67 families in Tarlac. Lastly, the simulated 2027 LULC map showed pixels with unrealistic conversions from solar farm (year 2023) to cropland (year 2027). To improve the model, it is recommended to add more spatial data to effectively capture factors that may contribute to the expansion of solar farms in the future. Moreover, high resolution LULC maps (vector maps if available) can be used instead of a course resolution satellite-derived raster data. Nonetheless, this study has demonstrated the use of RS, GIS and machine learning techniques to model cropland conversion to solar farms and other LULC classes. Results from this study can provide scientific data to policy makers, solar industry players and other relevant stakeholders in doing technoeconomic assessment of solar farm development and expansion considering its effect on energy security and food security towards national sustainable development.

A Geographical Analysis of Religious Services in Bardarash City Using Geographical Information System (GIS)

Religion service is one the topics that the geographical people have been focusing on. This takes place through being aware of their division, existence and their abilities with attempts to mention their disadvantages. These services in our researching area consists of 10 Friday mosques,6 mosques, 4 halls used for religion celebrations and 6 cemeteries. In this paper it has been attempted to know the extent to which these places serve people and to what extent they fit with the principles of these places. For achieving these goals this paper has adapted the (GIS) geographical information system technique. A questionnaire was distributed among people of the place where the research is made to know whether they think these services serve people in a good way or not.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season.The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.

Simulating future land-use within the uThukela and uMngeni catchments in KwaZulu-Natal

Due to anthropogenic activities, the earth's surface is consistently being altered. These alterations take the form of Land-use/cover change (LULCC), which is a fundamental driver of global, regional and local environmental change. LULCC studies have become pivotal in supplementing our understanding and observations of environmental change. However, understanding the past and present spatial-temporal variability of LULCC characteristics and their link to future land-use/cover trajectories at a catchment scale is limited, particularly in Southern Africa. To address this limitation, this study simulated future land-use change utilising a spatially distributed, empirical land-use modelling approach for the uThukela and uMngeni catchments in the KwaZulu-Natal province of South Africa. The CA-Markov model, a popular and frequently utilised model employed in land-use and land-cover (LULC) predictive modelling, was selected to simulate LULCC conjointly with Geographic Information Systems (GIS) techniques. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model's reliability and capability to successfully predict future LULC in the study sites. Future projections indicated that both study areas are anticipated to experience anthropogenic induced LULCC, which further fragments the landscape configuration, functionality and ecological stability. Historical analysis of LULCC between 1990 and 2018, in the study catchments revealed considerable declines in the areas under grassland and indigenous forest, while the areas under cultivated land, commercial forestry and urban LULC classes increased. Future LULC projections showed that urban and agriculture land-uses increased significantly, with natural land-use classes such as grassland, other vegetation and indigenous forest declining in spatial extent across both. With an understanding of the extent of projected LULCC by 2030 within both catchments, proactive planning and management within the framework of sustainable land-use planning and water resource management in the respective catchments can be undertaken. Moreover, the results of the land-use modelling study can be used to infill historical data gaps, support effective land-use planning and provide a means to evaluate the impacts of different future development pathways.

Geospatial mapping of ecoraces of tasar silkworm (Antheraea mylitta Drury) using remote sensing and geographic information system techniques

Tropical Tasar silkworm Antheraea mylitta Drury (Family: Saturniidae) produces Tasar silk which has very high demand globally and because of their wide range of distribution in tropical regions, they have adapted to different niches forming different ecoraces. In Odisha, alone seven types of ecoraces of A. mylitta have been reported, namely Modal, Sukinda, Nalia, Jata-Daba, Adaba, Umerkote, and Boudh. These ecoraces show differences in their phenotypes, genetic constitution, fecundity, voltinism, etc. In the past few decades, there is the substantial deterioration of habitats due to many anthropogenic activities. This has resulted in a large decline in wild Tasar cocoon production. Lepidopterans are accepted as ecological indicators of ecosystem health and it is apprehended that through anthropogenic and climatic factors this precious genetic resource, which took millions of years to evolve, may face the threat of extinction shortly if adequate conservation measures are not taken. This paper attempts the geospatial mapping of these ecoraces in the state of Odisha through RS and GIS tools, which will provide a greater scenario for their conservation

Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes.

Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full spatiotemporal configuration with urban development using innovative remote sensing (RS) technologies. Focusing on this issue, the authors propose an innovative methodology for the analysis of the urban and greening changes over time by integrating deep learning (DL) technologies to classify and segment the built-up area and the vegetation cover from satellite and aerial images and geographic information system (GIS) techniques. The core of the methodology is a trained and validated U-Net model, which was tested on an urban area in the municipality of Matera (Italy), analyzing the urban and greening changes from 2000 to 2020. The results demonstrate a very good level of accuracy of the U-Net model, a remarkable increment in the built-up area density (8.28%) and a decline in the vegetation cover density (5.13%). The obtained results demonstrate how the proposed method can be used to rapidly and accurately identify useful information about urban and greening spatiotemporal development using innovative RS technologies supporting sustainable development processes.

Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco

An assessment of potential groundwater areas in the Ifni basin, located in the western Anti-Atlas range of Morocco, was conducted based on a multicriteria analytical approach that integrated a set of geomorphological and hydroclimatic factors influencing the availability of this resource. This approach involved the use of geographic information systems (GIS) and hierarchical analytical process (AHP) models. Different factors were classified and weighted according to their contribution to and impact on groundwater reserves. Their normalized weights were evaluated using a pairwise comparison matrix. Four classes of potentiality emerged: very high, high, moderate, and low, occupying 15.22%, 20.17%, 30.96%, and 33.65%, respectively, of the basin’s area. A groundwater potential map (GWPA) was validated by comparison with data from 134 existing water points using a receiver operating characteristic (ROC) curve. The AUC was calculated at 80%, indicating the good predictive accuracy of the AHP method. These results will enable water operators to select favorable sites with a high groundwater potential.

Assessing the Socio-Economic Setup of Ravangla, Sikkim, and the Vulnerability of its Community using Geoinformatics

The geoinformatics platform integrates remotely sensed data and Geographic Information Systems (GIS). It has a vast range of applications. The technique is widely used not only to study the physical aspects of a region but also to analyse the social and economic setup. The objective of this study is to understand the socio-economic setup of Ravangla, which is a small tourist town located in South Sikkim, India. The primary data have been collected from the field survey and analysed using GIS tools. The social indices such as social structure, housing condition, and the condition of basic facilities such as health, education, transport, market, and others have been analysed in the present study. GIS has been used to study the spatial distribution of these social and economic parameters. The technique has also been used to assess the socio-economic vulnerability of the community living in Ravangla. The Composite Vulnerability at the community level has been calculated based on socioeconomic parameters and a vulnerability map has been prepared. Based on the Composite Vulnerability Index, the households in the study area have been classified as Highly, Moderately, and Less Vulnerable. The GIS techniques have been used to amalgamate the socioeconomic components of vulnerability to recognize the most vulnerable households in Ravangla. The study will help planners, policymakers, and administrators to take necessary steps for mitigating socio-economic problems in the study area.

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field (652000-659000E-W and 4528000-4536000N-S) in 2019. This study's primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS (Geographical Information Systems) and deep learning (DL) technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88% was attained when classifying "class" data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

GIS-based approach and multivariate statistical analysis for identifying sources of heavy metals in marine sediments from the coast of Hong Kong.

Hong Kong is an urbanized coastal city which experiences substantially different metal loads from anthropogenic activities. This study was aimed at analyzing the spatial distribution and pollution evaluation of ten selected heavy metals (As, Cd, Cr, Cu, Pb, Hg, Ni, Zn, Fe, V) in the coastal sediments of Hong Kong. The distribution of heavy metal pollution in sediments has been analyzed using the geographic information system (GIS) technique, and their pollution degrees, corresponding potential ecological risks and source identifications, have been studied by applying the enrichment factor (EF) analysis, contamination factor (CF) analysis, potential ecological risk index (PEI), and integrated multivariate statistical methods, respectively. Firstly, the GIS technique was used to access the spatial distribution of the heavy metals; the result revealed that pollution trend of these metals was decreased from the inner to the outer coast sites of the studied area. Secondly, combining the EF analysis and CF analysis, we found that the pollution degree of heavy metals followed the order of Cu > Cr > Cd > Zn > Pb > Hg > Ni > Fe > As > V. Thirdly, the PERI calculations showed that Cd, Hg, and Cu were the most potential ecological risk factors compared to other metals. Finally, cluster analysis combined with principal component analysis showed that Cr, Cu, Hg, and Ni might originate from the industrial discharges and shipping activities. V, As, and Fe were mainly derived from the natural origin, whereas Cd, Pb, and Zn were identified from the municipal discharges and industrial wastewater. In conclusion, this work should be helpful in the establishment of strategies for contamination control and optimization of industrial structures in Hong Kong.

GDAL and PROJ Libraries Integrated with GRASS GIS for Terrain Modelling of the Georeferenced Raster Image

Libraries with pre-written codes optimize the workflow in cartography and reduce labour intensive data processing by iteratively applying scripts to implementing mapping tasks. Most existing Geographic Information System (GIS) approaches are based on traditional software with a graphical user’s interface which significantly limits their performance. Although plugins are proposed to improve the functionality of many GIS programs, they are usually ad hoc in finding specific mapping solutions, e.g., cartographic projections and data conversion. We address this limitation by applying the principled approach of Geospatial Data Abstraction Library (GDAL), library for conversions between cartographic projections (PROJ) and Geographic Resources Analysis Support System (GRASS) GIS for geospatial data processing and morphometric analysis. This research presents topographic analysis of the dataset using scripting methods which include several tools: (1) GDAL, a translator library for raster and vector geospatial data formats used for converting Earth Global Relief Model (ETOPO1) GeoTIFF in XY Cartesian coordinates into World Geodetic System 1984 (WGS84) by the ‘gdalwarp’ utility; (2) PROJ projection transformation library used for converting ETOPO1 WGS84 grid to cartographic projections (Cassini–Soldner equirectangular, Equal Area Cylindrical, Two-Point Equidistant Azimuthal, and Oblique Mercator); and (3) GRASS GIS by sequential use of the following modules: r.info, d.mon, d.rast, r.colors, d.rast.leg, d.legend, d.northarrow, d.grid, d.text, g.region, and r.contour. The depth frequency was analysed by the module ‘d.histogram’. The proposed approach provided a systematic way for morphometric measuring of topographic data and combine the advantages of the GDAL, PROJ, and GRASS GIS tools that include the informativeness, effectiveness, and representativeness in spatial data processing. The morphometric analysis included the computed slope, aspect, profile, and tangential curvature of the study area. The data analysis revealed the distribution pattern in topographic data: 24% of data with elevations below 400 m, 13% of data with depths −5000 to −6000 m, 4% of depths have values −3000 to −4000 m, the least frequent data (−6000 to 7000 m) <1%, 2% of depths have values −2000 to 3000 m in the basin, while other values are distributed proportionally. Further, by incorporating the generic coordinate transformation software library PROJ, the raster grid was transformed into various cartographic projections to demonstrate distortions in shape and area. Scripting techniques of GRASS GIS are demonstrated for applications in topographic modelling and raster data processing. The GRASS GIS shows the effectiveness for mapping and visualization, compatibility with libraries (GDAL, PROJ), technical flexibility in combining Graphical User Interface (GUI), and command-line data processing. The research contributes to the technical cartographic development.

Marine Suitability Assessment for Mariculture: Combining GIS and AHP Technique in Dampier Strait Conservation Area, West Papua Province, Indonesia

Highlight Research Mariculture is one of the activities that can pressure the aquatic environment. Mariculture activities are carried out by considering the sustainability aspects of environmentally friendly implementation. The suitability of the location for mariculture activities refers to the geographical and hydro–oceanographic conditions seen from the waters' physical, chemical, and biological parameters. The suitability index produced using multiple approaches (GIS-based AHP) can be used to determine the potential locations for mariculture development. Abstract Potential mariculture activities in the Dampier Strait Conservation Area, which are indicated by the availability of water space and local commodities, can have a multiplier effect on socio-ecological aspects. Therefore, reasonable development steps are needed to carry out environmentally friendly aquaculture. This study aims to determine the potential use of water space for the development of mariculture activities based on the level of suitability using multiple approaches. Suitability analysis was carried out with Geographic Information System (GIS) based on the Analytical Hierarchy Process (AHP) by considering the area’s characteristics using several aspects seen from the geographical conditions and water quality (physical, chemical, and biological). Based on the suitability analysis results, the potential water space that can be used for mariculture development activities, namely seaweed and pearl oyster cultivation was 1,130.45 ha (0.49% of the overall space allocation 232,588.59 ha). A justification was then carried out to determine a suitable location in terms of its accessibility and environmental friendliness. The analysis has shown promising results in determining potential locations for developing mariculture activities for seaweed and pearl oyster commodities based on sustainable and environmentally friendly cultivation.

Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt

The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, and mixed Na–Ca–HCO3 water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.

Application of Remote Sensing and Geographical Information System (GIS) in Flood Vulnerability Mapping: A Scenario of Akure South, Nigeria

In flooding, dry land capable of residential, agricultural, and other economic activities is submerged by overflowing water. This causes loss of lives, properties, and destruction of infrastructure. This study applies remote sensing and GIS techniques to produce a flood vulnerability map of the Akure South metropolis. For this study, satellite image data (Landsat 8), location map of Akure South metropolis, SRTM DEM, rainfall data, soil data, and GPS coordinates; acquired during the field survey were integrated to map areas vulnerable to flooding. Using Pairwise Comparison, the various weights of factors constituting flood in the area were acquired. A weighted linear combination and analytical hierarchical process were used to produce the flood hazard and flood vulnerability map. ArcGIS Pro 2.7.3 software was used in spatial and attribute data acquisition, processing, and information presentation. The flood vulnerability results indicated that the very high vulnerability zone occupied 13.9% of the study area, while high vulnerability zone occupied 25.5%. Moderate vulnerability zone occupied 36.8% while low vulnerability zone occupied 23.8% of the study area. The study shows that, remote sensing and GIS can be effectively implemented to analyse and provide results on flood vulnerability required for prompt and effective decision-making on floods.