Geographic Information System Technology Research Articles

Preface

It is with great pleasure that we introduce the proceedings of The National Conference on GIS Application, hosted by the Vietnam National University of Agriculture on November 11-12, 2023. The conference, held under the theme “GIS Application: Digital transformation in natural resources and environmental management,” serves as a platform for the exchange of cutting-edge research and ideas within the dynamic landscape of Geographic Information Systems (GIS).In the context of Digital Transformation and Industry 4.0, this conference stands as a pivotal gathering aimed at exploring and leveraging GIS technologies for the sustainable management of natural resources and the environment. The event encompasses 8 sessions, each dedicated to a specific facet of GIS application, ranging from environmental and natural resource management to healthcare and urban infrastructure.The diverse array of sessions includes: GIS, GPS, and Remote Sensing in environmental and natural resources management; GIS, GPS, and Remote Sensing in land management, urban management, and infrastructure; GIS, GPS, and Remote Sensing in Climate Change research for natural disaster prevention and mitigation; GIS, GPS, and Remote Sensing in agriculture, forestry, and aquaculture; GIS and GPS in state management; GIS, GPS, and Remote Sensing in the field of medicine and public health care; GIS and GPS in water supply and drainage management and urban works; Artificial intelligence (AI), Big Data in the field of GIS.The response to the conference was overwhelming, with over 100 submitted papers reflecting the dedication and enthusiasm of researchers in the GIS community. Following a meticulous review process on the Easychair and Morressier platforms, 28 outstanding papers have been selected for publication in the IOP Conference Series: Earth and Environmental Science.We extend our sincere gratitude to all contributors, reviewers, and participants who have contributed to the success of this conference. It is our hope that these proceedings will serve as a valuable resource and inspire further advancements in GIS application for the betterment of our natural environment and resource management practices.We wish you an insightful and fruitful exploration of the diverse research presented in these proceedings.Sincerely,On behalf of editor boardProf. Dr. Nguyen Kim LoiDepartment of GIS and Natural ResourcesFaculty of Environment and Natural ResourcesNong Lam University, Ho Chi Minh CityVIETNAMList of Editor board is available in this Pdf.

ANALISIS SISTEM INFORMASI GEOGRAFIS PARIWISATA DI KABUPATEN SEMARANG

As time goes by, technology is also developing, especially in the field of Geographic Information Systems (GIS). Geographic Information Systems (GIS) have become an important tool in analyzing, managing, and designing. In various fields and sectors. Semarang Regency is a region located in the province of Central Java which has many very diverse tourist destinations. Tourist destinations in Semarang Regency are relatively low in visitors compared to other regions. Therefore, the aim of this research is to design an application that functions to introduce tourist destinations in Semarang Regency to the wider community by adopting Geographic Information System (GIS) technology. This application makes it easier for visitors to find out tourist locations, see locations virtually using images, see place ratings based on public assessments, looking at the costs required to access tourist areas, etc. This design method uses qualitative methods and system design using UML (Unified Modeling Language).

Application of Geographic Information Systems (GIS) and the Practice of Public Administration

Geographic Information System (GIS) applications are tools that allow users to create interactive queries (user-created searches), analyse spatial information, edit data in maps and present the results of all these operations. This paper was particularly focused on the application of GIS in the practice of Public Administration. Public Administration is a branch of management sciences that studies how government organizations operate and how they make decisions. The complexity in achieving equitable distribution of government actions as well as implementation of such actions to the public requires a thorough analysis of costs and benefits. Many GIS tools could be useful to public administrators, such as measurement tools, mapping tools, spatial analysis tools, data capturing and management tools. Data management tools allow administrators to organize and manage large amounts of data. Therefore, GIS technology offers a framework for gathering and managing information and improving planning efforts in the public domain for effective service delivery. They are quite available at the disposal of Public Administration experts if they are to successfully meet the challenges surrounding the procedure of planning development.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a “V” -shaped oscillation change.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This study was conducted in Oluwa Forest Reserve to assess and predict its aboveground carbon sequestration potentials using LandSat Thematic Mapper data. The Oluwa Forest Reserve, Ondo State, Nigeria, is recognized for its rich biodiversity and extensive size. To estimate its forest aboveground biomass and carbon should be complex and costly endeavour requiring the expertise of various professionals and equipment. Consequently, this study explored the use of Geographic Information System (GIS) and Remote Sensing (RS) technology using LandSat bands to estimate spectral indices in fitting linear models to predict the aboveground carbon sequestration potentials of the tropical rainforest ecosystem of Oluwa Forest Reserve. The observed aboveground carbon from sample plots and the estimated spectral indices were used to model the spread of aboveground carbon of Oluwa Forest Reserve. Positive linear relationship exists between the observed and the spectral indices data estimated. Therefore, linear models were fitted and the best-fit was determined using statistical measures. The aboveground carbon average estimated from the sample plots and the predicted were 150.70 t/ha and 149.80 t/ha, respectively. The coefficient of determination 94% and Root Mean Square Error = 6.38E-16, respectively were obtained statistically. The chosen model predicts the aboveground carbon spread of Oluwa Forest Reserve adequately. The study revealed that spectral data, GIS and RS are critical for large forest aboveground carbon modelling and mapping for efficiency.

Smart Garden Management System Based on the combination of Internet of Things and Geographic Information System Technology

The Internet of Things (IoT) technology is inevitably forging ahead in a number of areas all among them being the agricultural sector and in particular the affected environment. This paper utilizes the farm's IoT where through an effective analysis, the smart garden is designed and managed. It blends field research, project implementations and theoretical analysis to improve the case implementation. It can be considered as more rigorous and practical. The study intends, first and foremost, to look at agricultural IoT based technologies and how IoT changes the way farmers work and agricultural landscapes look. Different theories have been used in the implementation of these concepts which include agriculture, tourism, and landscape design. The technical side will integrate the Internet of Things technology, the adaptation theory of agricultural industrialization, the formation of the theory of ecotourism, the application of tourism psychological theory in tourism discipline, the ecology theory of landscape and aesthetics theory, and landscape gardening planning and design theory. A comprehensive examination of the smart park has been conducted, including relevant theories and the application of planning and design principles. This systematic research aims to provide scientific direction for the agricultural aspects of the smart park. In order to offer scientific direction for the planning and design of agricultural Internet of Things (IoT) in smart gardens, this study presents a theoretical framework for the planning and design of smart gardens that incorporate agricultural IoT. The framework is comprehensively explained, encompassing its concept, distinguishing features, relevant theories, and guidance approaches.

Suitability of Site Selection for Mountain Railway Engineering Spoil Disposal Areas from a Multi-Scenario Perspective

The current approach to selecting sites for abandoned spoil areas primarily relies on qualitative methods, often overlooking the impact of policy factors on decision-making. Traditional single-site selection strategies may not be flexible enough to accommodate evolving external policy demands. Addressing this challenge is crucial for ensuring the site selection for abandoned spoil areas is both scientifically sound and policy-compliant. This research integrates various analytical methods, including principal component analysis, complex network theory, the CRITIC method, and the ordered weighted averaging method, to thoroughly evaluate the factors influencing site selection. Utilizing geographic information system (GIS) technology, the study simulates different policy scenarios, such as construction cost, social and ecological concerns, natural security, spatial accessibility, and a comprehensive balance approach. It specifically analyzes the suitability of the spoil site of a segment of the Chongqing ZW Railway under these policy conditions. Based on the actual policy situation in the local area, six potential suitable sites were screened with the help of field investigation. This study can offer a methodological framework and theoretical guidance for optimally locating mountain railway engineering waste disposal sites. In addition, the methodology presented in this study can be adapted to the development and change in policy scenarios.

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.

Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems.

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

Geospatial Analysis of Archaeological Sites in Ikara Local Government Area of Kaduna State, Nigeria

Ikara Local Government Area of Kaduna State, Nigeria has rich and diverse archaeological heritage. The research employed geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, to analyze and document the archaeological sites in Ikara. The research aims to assess the Geospatial distribution of Archaeological sites in the study area by mapping to locate the Archaeological sites in the study area, determine the pattern of distribution of the Archaeological sites and examine the finds and features in the sites. The research employed a mixed-method approach that combines field surveys, archival research, and geospatial analysis. The research findings show fifteen (15) archaeological sites dispersedly distributed across the Eight (8) wards in the study area with Ikara ward having the highest number of archaeological sites. Also, the dominant cultural materials across the sites are finds in forms of potsherds and features in form of House foundation. The cultural remains on the Archaeological sites are House foundations, mounds, granary, potsherds, iron slag, open grave, stone tools and grinding stones. Based on the findings, dispersed pattern of the Archaeological sites in the study area was deduced from the data collected especially at the southern part of the area of study where the sites are found to be most concentrated. Base on the research findings, it is recommended that further research shall be undertaken using GIS and Remote Sensing Technology for effective Archaeological site discovery, management and preservation.

Landslide susceptibility modelling in the Doda Kishtwar Ramban (DKR) region of Jammu and Kashmir using Remote Sensing and Geographic Information System

The present study represents a significant understanding, employing cutting-edge Remote Sensing (RS) and Geographic Information System (GIS) technologies by conducting a comprehensive landslide susceptibility modelling in the Doda, Kishtwar, and Ramban (DKR) districts of the Union Territory of the Jammu and Kashmir in the NW Himalaya. The primary objective of this study is to determine the areas that are at high risk of landslides and accordingly classified these risk zones into five distinct categories, ranging from very high (VH) to very low (VL) susceptibility. To achieve this, a multifaceted approach, involving the utilization of various terrain thematic layers in the GIS environment was applied. The preferred framework employed in this study is the Weighted Overlay (WO) analysis. The final outcome of the study is the final landslide susceptibility map which is a significant and valuable resource for a wide range of stakeholders, including decision-makers, land managers, and local communities. It equips these stakeholders with the information and insights required to adopt proactive strategies in response to the identified landslide susceptibility zones.

Assessing road vulnerability in seismic conditions: a comprehensive study

Road infrastructure is indispensable for societal functionality, yet it is highly susceptible to the devastating impacts of seismic events. This paper focuses on enhancing the resilience of road systems by systematically identifying, quantifying, and assessing factors contributing to their vulnerability during earthquakes. The core objective of this study is to develop and validate a seismic vulnerability index for road sections, which aims to evaluate and classify the susceptibility of road infrastructure to earthquake-induced damage comprehensively. This systematic approach is pivotal for guiding effective mitigation strategies and prioritization efforts. To fulfill this objective, the study employs the Analytic Hierarchy Process (AHP) to introduce a novel methodology for calculating the seismic vulnerability index, incorporating various factors that influence road vulnerability. This method allows for the accurate classification of road sections into distinct levels of susceptibility, providing a solid foundation for implementing targeted interventions and enhancing road resilience. Further, the paper validates the theoretical model through several case studies, demonstrating the practical applicability and effectiveness of the seismic vulnerability index in real-world scenarios. Additionally, the use of Geographic Information System (GIS) technology to simulate earthquake scenarios within an urban road network offers valuable insights into the potential seismic behavior of roads. These simulations are crucial for identifying critical areas that require intervention and for planning resilience-building efforts more effectively. By proposing a comprehensive framework that combines rigorous analysis, empirical validation, and advanced simulation techniques, this paper seeks to make a significant contribution to the field of infrastructure resilience. It aims to advance the understanding of road vulnerability in seismic conditions and supports the development of strategic approaches for enhancing the resilience of road infrastructure against earthquakes. Through the development and validation of a seismic vulnerability index, this study meets its primary objective, providing a valuable resource for researchers, policymakers, and practitioners in disaster management and infrastructure planning.

Creation of Turkiye Risk Map for Cydalima Perspectalis (Box Tree Moth) by Weighted Overlay Analysis

Boxwood (Buxus spp.), which is widespread all over the world except Antarctica and widely used in many areas, is threatened by Cydalima Perspectalis (box tree moth). Cydalima Perspectalis (box tree moth), which has become widespread in Europe, is a new invasive species originating from East Asia and has become widespread in Turkiye in the last ten years. This species was first encountered in parks and gardens in Sarıyer, Istanbul, in 2011. Since this year, it has continued to spread rapidly in Turkiye. The pest causes intense damage by causing drying on boxwood areas, which causes economic and ecological losses. The rich ecological and topographical conditions of our country are also favorable for the spread of this species. Therefore, it is important to develop pest control methods and take early measures. Within the scope of this study, the aim was to realize early detection of box tree moth (Cydalima perspectalis) inTurkiye and make a risk map in this context. For this purpose, a risk map was created by determining the risky areas where Cydalima perspectalis can spread inTurkiye using weighted overlay analysis from geographic information system (GIS) technologies. Existing boxwood (Buxus spp.) locations obtained from field studies were compared with the risk map and analyzed. As a result of the analysis, the presence of Cydalima Perspectalis was found in the boxwood in the identified risk areas, and it is expected to be transmitted in boxwood that is in the risk area but not observed to have Cydalima Perspectalis. With this research, a risk map for Cydalima Perspectalis was made for the first time.

Assessing the climate change adaptation of Sam Son city to guide tourism development using AHP model

The tourism industry in Vietnam is developing rapidly and making significant contributions to the country's economy. However, tourism is also facing challenges related to climate change, especially coastal tourism. The article presents the adaptive capacity of Sam Son city in the development of the tourism industry in respond to the impact of climate change. The adaptive capacity of the Thanh Hoa city is constrained by three groups of factors, including infrastructure, socio-economic, and policy mechanisms. This article employs a GIS (Geographic Information System) technology approach and an Analytic Hierarchy Process (AHP) model to spatialize and quantify the adaptive capacity factors. Among the adaptive factors, the group of policy mechanism factors has the highest weight contributing to the adaptive capacity of Sam Son, with weights ranging from 0.25÷0.29. Meanwhile, the factor of the percentage of households with solid houses contributes insignificantly to adaptive capacity with a weight of 0.04. The study results in Sam Son city show that: The Adaptive Capacity (AC) index of the Sam Son city in guiding the development of the tourism industry is affected by the impact of climate change ranging from 0.28÷0.72, belonging to three levels: low (AC = 0.0÷0.30) was found in 6/11 communes and wards, medium (AC = 0.31÷0.63) was found in 7/11 communes and wards, and high (AC = 0.64÷1.0) was found in 3/11 communes and wards. Overall assessment, the AC of Sam Son city, Thanh Hoa province is at an average level in condition of climate change.

Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi

For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geographic Information Systems across all jurisdictions. Maps showing crime locations and crime hotspot zones are therefore not included in the crime data and statistics report provided by the Malawi Police Service. In this light, a lot of people have become victims of various forms of crimes in areas where those crimes are also prevalent. To collect, track, and analyze crime data in Malawi for this study, Geographical Information System (GIS) particularly network analysis techniques were used. Network Analysis was used to identify crime hotspots by analyzing crime data as a network of interconnected events and locations. The rationale behind this was to treat each crime event as a node in the network and the spatial relationships between the crimes as edges. By analyzing this network, patterns and relationships between crime events were revealed, allowing for the identification of crime hotspots. The study found that Lilongwe in the central region and the capital city registered the highest number of crimes seconded by Blantyre in the southern region and followed by Mangochi. Mzimba recorded high crimes in the northern region. In Malawi, the traditional systems of intelligence and criminal record keeping have failed to satisfy the demands of today's crime situation. Manual methods neither give accurate, dependable, or complete data 24 hours a day nor do they help in trend forecasting and decision assistance. It also leads to poorer productivity and inefficient workforce use. The appropriate application of information technology is the solution to this ever-increasing challenge.

Preparation Geodatabase of Sedimentological and Geological Properties by Using GIS Technology: A Case Study Basrah Province, Southern Part of Iraq

This study uses a combination of remote sensing and geographic information systems (GIS) to create a geodatabase file of geological features in the southern Iraq (Basrah region, as a case study). The geodatabase includes information about sedimentology, topography, structure, and a surface digital elevation model (topographic model). ETM+ Satellite Imagery group with a resolution of 28.5m was the primary data for which the Basrah map was created. Collected sediment data from literature and analysis in this study were inputted to plot geospatial sediment maps of the study area. The sediment maps were created using the SEDCLASS program developed by USGS, which uses of Shepard's and Folk's Schemes to classify the sediments. This geodatabase is a vital, essential step forward that offers a dynamic for future regional development. There is a crucial need to research and create models related to geo-environmental impacts.

Applying an Analytic Hierarchy Process and a Geographic Information System for Assessment of Land Subsidence Risk Due to Drought: A Case Study in Ca Mau Peninsula, Vietnam

The increase in extreme weather events causes secondary hazards that can influence people and the environment enormously. The Ca Mau Peninsula is known as one of the areas most severely affected by drought, and excessive groundwater exploitation is one of the reasons leading to a higher risk of land subsidence. This study uses the Delphi method and the KAMET rule table to analyze and select indicators that affect subsidence. The study uses the analytic hierarchy process (AHP) analytical hierarchy method to evaluate the weights of influencing factors, combined with geographic information system (GIS) technology to overlay the map layers of the main influencing factors and build a subsidence risk warning zoning map of the study area. The influencing factors selected to evaluate the impact on land subsidence in the study area during the drought period included geological structure, soil characteristics, groundwater flow exploitation, water flow in the dry season, current land use status, and evaporation in the dry season. The weights of these factors were evaluated based on the synthesis of relevant documents as well as consultation with experts. The results indicate that nearly two-thirds of the Ca Mau Peninsula area is currently at very low or low risk of subsidence. Meanwhile, 23% of the area is at medium risk, nearly 9% is at high risk, and 0.1% of the study area is at very high risk. Subsidence risk warning zoning maps can provide a visual and general overview of areas with high subsidence risk, supporting managers in making reference plans for socio-economic development in the Ca Mau Peninsula.

Kaedah dan Kegunaan dalam Perlaksanaan Integrasi BIM-GIS dan Potensinya dalam Pengurusan Sistem Saliran: Satu Tinjauan

The ability of Geographic Information System (GIS) applications to manage three-dimensional (3D) data is increasing along with advancements in current technology, and this ability can be further enhanced through the integration of Building Information Modeling (BIM) technology. While the combination of BIM and GIS technologies provides significant benefits to human life, there are several challenges and limitations that need to be addressed for successful implementation. Therefore, research on previous articles will be conducted to examine the challenges and potential of this integration. This paper will begin with searching for relevant previous articles using appropriate keywords on Web of Science and Scopus. The search will be conducted in two parts, articles related to BIM-GIS integration with keywords "BIM" AND "GIS," and articles concerning the usage of 3D data in GIS applications for drainage system management using keywords "GIS" AND "3D" AND "DRAINAGE." From these articles, observations will be made regarding (1) the structure of BIM-GIS integration data, (2) implemented integration methods, (3) utilization of integration models, and (4) the potential of BIM-GIS Integration in managing 3D drainage system data. The research identifies key challenges in BIM-GIS integration, including differences in class numbers, dissimilar class arrangement, geometry creation methods, and georeference system application. To address these challenges, standard protocols based on specific requirements need to be established by considering organizational needs to ensure a consistent integration process. The paper also recognizes significant potential in employing BIM-GIS integration for drainage system management.