Geographic Information System Technology Research Articles

The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

Landslides, whether induced by nature, or human activities, are one of the most prominent disasters which are of great concern in the world. They cause a lot of havoc to the environment hence a necessity to avoid them whenever possible. This literature review is basically aimed at finding out how Geographical Information System (GIS) and Remote Sensing (RS) technologies are used in avoiding landslides and risks associated with them at different levels; Challenges encountered while using GIS and RS techniques for landslide disaster risk reduction are identified; and therefore, dealing with them requires the involvement of developed countries who have the capacity to provide the necessary equipment to the developing countries that are faced with disasters. This study discusses the use of GIS and RS in mitigation of risks from landslides, and mainly points out how these techniques are applied to avoid disaster risks. Secondary data was reviewed from journal articles, institutional reports, and online publications from similar studies. GIS and RS tools are important in predicting, monitoring and managing landslide disasters. It was concluded that GIS and RS tools provide cheaper, reliable, and faster techniques of accessing spatial data in a given area, therefore regarded as essential technologies that may be necessary for predicting landslide occurrences, these technologies need to be considered in communities which are prone to landslides.

Causes and Impacts of Decreasing Chlorophyll-a in Tibet Plateau Lakes during 1986–2021 Based on Landsat Image Inversion

Lake chlorophyll-a (Chl-a) is one of the important components of the lake ecosystem. Numerous studies have analyzed Chl-a in ocean and inland water ecosystems under pressures from climate change and anthropogenic activities. However, little research has been conducted on lake Chl-a variations in the Tibet Plateau (TP) because of its harsh environment and limited opportunities for in situ data monitoring. Here, we combined 95 in situ measured lake Chl-a concentration data points and the Landsat reflection spectrum to establish an inversion model of Chl-a concentration. For this, we retrieved the mean annual Chl-a concentration in the past 35 years (1986–2021) of 318 lakes with an area of > 10 km2 in the TP using the backpropagation (BP) neural network prediction method. Meteorological and hydrological data, measured water quality parameters, and glacier change in the lake basin, along with geographic information system (GIS) technology and spatial statistical analysis, were used to elucidate the driving factors of the Chl-a concentration changes in the TP lakes. The results showed that the mean annual Chl-a in the 318 lakes displayed an overall decrease during 1986–2021 (−0.03 μg/L/y), but 63%, 32%, and 5% of the total number exhibited no significant change, significant decrease, and significant increase, respectively. After a slight increase during 1986–1995 (0.05 μg/L/y), the mean annual lake Chl-a significantly decreased during 1996–2004 (−0.18 μg/L/y). Further, it decreased slightly during 2005–2021 (−0.02 μg/L/y). The mean annual lake Chl-a concentration was significantly negatively correlated with precipitation (R2 = 0.48, p < 0.01), air temperature (R2 = 0.31, p < 0.01), lake surface water temperature (LSWT) (R2 = 0.51, p < 0.01), lake area (R2 = 0.42, p < 0.01), and lake water volume change (R2 = 0.77, p < 0.01). The Chl-a concentration of non-glacial-meltwater-fed lakes were higher than those of glacial-meltwater-fed lakes, except during higher precipitation periods. Our results shed light on the impacts of climate change on Chl-a variation in the TP lakes and lay the foundation for understanding the changes in the TP lake ecosystem.

Field Apps Help Small Water Utilities Get Started With Digital Maps

Geographic information system technology can help small and rural utilities start collecting asset information using smart devices in the field.

Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City

With the development of machine learning and GIS (geographic information systems) technology, it is possible to combine them to mine the knowledge rules behind massive spatial data. GIS, also known as geographic information systems, is a comprehensive discipline, which combines geography and cartography and has been widely used in different fields. It is a computer system for inputting, storing, querying, analyzing, and displaying geographic data. This paper mainly studies the spatial differentiation characteristics of rural areas based on machine learning (ML) and GIS statistical analysis. This paper studies 21 township units in Yongtai County. In this paper, ENVI remote sensing image processing software is used to carry out the geometric correction of Landsat-8 remote sensing data. ML is multidisciplinary and interdisciplinary, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and other disciplines. It is specialized in studying how computers simulate or realize human learning behavior to obtain new knowledge or skills, and reorganize existing knowledge structures to continuously improve its own performance. The purpose of using band fusion is to provide more data information for the study and improve the accuracy of land classification results. Through the extraction of evaluation elements, this paper preliminarily confirms the evaluation index object of a rural human settlement environment evaluation system from the perspective of spatial layout rationality. This paper uses a VMD-GWO-ELM-based three-stage evolutionary extreme learning machine evaluation method to simulate the model. In the same way, when the model is trained again, extra weight is given to extract the feature points to reduce the similarity. Experimental data show that GWO-SVM has good classification performance, with the cross-validation rate reaching 91.66% and the recognition rate of test samples reaching 82.41%. The results show that GIS statistics can provide a reference for environmental protection, which is conducive to land-use planning, implementation of environmental impact assessment of land-use planning, and ultimately achieving sustainable development.

Survey of indoor tracking systems using augmented reality

<p><span lang="EN-US">Augmented reality overlays virtual content on the physical world, displaying location-based information more efficiently. Tracking is a trace detail of the location recorded, either by taking a reading based on a set time interval, set distance, and change in direction by more than a certain angle, or a combination of these. Tracking is divided into two types, outdoor tracking, and indoor tracking. Augmented reality added value and information to tracking applications, whether indoor or outdoor. Recently, outdoor tracking by the global positioning system (GPS) became an essential component in navigation applications. However, indoor tracking is still challenging in the augmented reality field. Most augmented reality indoor tracking use optical and sensor-based tracking, such as markers, beacons, Raspberry Pi, and route planner modules for the building. With the technology enhancement, indoor tracking started to rely on WiFi, Bluetooth, geographic information system, radiofrequency identification, and sensor chip technologies. Our paper describes the recent augmented reality tracking techniques from 2011 to 2021. The paper compares image detection algorithms with various communication technologies, disscusing the advantages and the limitations of each technology.</span></p>

Application of Parametric Design in the Optimization of Traditional Landscape Architecture

Parametric design, with its unique scientific and logical nature, is gradually applied in the field of landscape design. Therefore, the GIS (geographic information systems) technology of parametric software was applied to the optimization of traditional landscape architecture, and its practical application quality was discussed. The actual analysis results showed that the evaluation result of parametric design had the highest score of 7.71 in behavioral perception. The overall score was 7.28, showing a high scientific nature. In the evaluation of landscape environmental benefits, after the optimization of landscape greening by parametric design, the air cleanliness and living comfort were generally improved, compared with those before optimization, and the highest values were 11.97 ± 6.01 and 5.86 ± 2.11 respectively. In the evaluation of the economic benefits of gardens, the economic income of gardens in the past 8 years generally increased, reaching the highest of 3.5795 billion yuan, with a growth rate of 78.92%. At the same time, the return on investment reached 26.17%, far exceeding the expected 20%. Among the social benefits, the weight of increasing employment opportunities was the largest at 0.36. In summary, parameterized optimization of traditional landscape design can effectively improve its social, environmental, and economic benefits and has good practical value.

Cultural heritage sites risk assessment based on RS and GIS—takes the Fortified Manors of yongtai as an example

As the carrier of history and culture, cultural heritage has outstanding value and is increasingly vulnerable to natural hazards and anthropogenic changes. The research on the risk of cultural heritage sites around the world is becoming more and more mature, but the research on Chinese cultural heritage sites is inadequate. The purpose of this study is to fill the gap in the research on multi-disaster risk assessment of cultural heritage sites in China and to propose an integrated approach to risk assessment based on Remote Sensing (RS) and Geographic Information System (GIS) for the protection and development of cultural heritage. This study identified six threats to cultural heritage sites: landslides, floods, erosion, urban sprawl, fires, and modern road networks. Taking Fortified Manors of Yongtai, Fujian Province, China as the research object, this study collected historical satellite images, relevant remote sensing, and thematic data, used GIS technology to establish a basic spatial database, and constructed a risk assessment system in the study area using the Analytic Hierarchy Process (AHP) and the Certainty Factor (CF) model. The risk of Fortified Manors of Yongtai was divided into five levels: extremely low risk, low risk, medium risk, high risk, and extremely high risk. It is found that most of the Fortified Manors are in high risk or above, accounting for 69.28%, indicating that most of them are in a very dangerous environment, and it is urgent to take measures to monitor and protect them.

Changing Climatic Conditions Affect Snow Cover in Annapurna Region of Nepal

Changing climatic conditions affecting the physical environment, hydrology, forest and wildlife, agriculture, and other sectors of the economy has become a major concern worldwide. The Annapurna Range in the Central Himalayas in Nepal is experiencing impacts of climate change on various fronts, including temperature increase and change in snow cover area (SCA). The objectives of this study were to assess spatio-temporal variation in temperature, precipitation, and SCA in the Annapurna massif and establish a relationship between these variables. This study analyzed the daily maximum and minimum temperature and precipitation records of six weather stations in the region, and performed a long-term analysis (1990–2020) of snow cover over Annapurna massif analysing Satellite images from the past three decades provided by satellite 5 through 8 of the Landsat program and Geographic Information System (GIS) technology. The SCA was determined through Landsat images using the Normalized Difference Snow Index (NDSI). Temperature analysis showed that the northeast (Chame) and northwest (Jomsom) parts of the massif were undergoing a consistent increase in average temperature at the rate of 0.07 and 0.03∘∘C per year, respectively. A north-south gradient was observed in total annual precipitation with total precipitation increasing in Ghandruk and Ranipauwa (Muktinath) at 50 mm and 4.8 mm per year, respectively, and decreasing in Manang Bhot at 4.7 mm per year. The precipitation events increased during spring in the west (Lete), northwest (Jomsom), and south (Ghandruk) of the massif, whereas it decreased in the north (Manang Bhot) and northeast (Chame) for all seasons. The SCA varied from 397 km22 to 1735 km22 with a significantly decreasing trend in December. There was a non-significant SCA increase during March, indicating that more snow coverage could appear in the spring in the future. These results could help local communities, government agencies, tourism industries, and other stakeholders develop resource management plans and climate change adaptation strategies.

Relief Modeling in the Restoration of Extractive Activities Using Drone Imagery.

In the field of mine engineering, a cross-section topographic survey is usually carried out to perform volumetric calculations of earth movement in order to restore areas affected by extractive activities. Nowadays, Remote Sensing and Geographical Information System (GIS) technologies make it possible to perform the same work by using indirect methods such as images obtained by photogrammetric flights. In this context, Unmanned Aerial Systems (UAS) are considered a very convenient option to develop mapping projects in short periods of time and to provide quality geospatial information such as Digital Elevation Models (DEM) and orthophotos of centimetric spatial resolution. In the present study, this approach has been applied in a gravel extraction area to obtain data for estimating the filling volume of material required for the restoration of the relief (DEM(r)). The estimation of the DEM(r) is later used to calculate a difference of height values (DEM(r)-DEM) that will serve as a variable in the basic operation of volume calculation. The novelty of the presented method is the simulation of a relief adapted to the surrounding morphology, including the derived channel network and the visibility impact, improving what would be a simple clogging. Likewise, the generation of 3D models allows visualizing a new morphological structure of the relief. The proposed approach, based on GIS tools, allows analyzing water flow connectivity integration of the DEM(r) with the environment and estimating potential landscape impacts from the main focuses of a visual basin, both of which are key aspects of restoration modeling that are not always properly addressed.

Analysis of Himalayan marmot distribution and plague risk in Qinghai province of China using the “3S” technology

To provide guidance for plague surveillance and a reliable basis for plague prevention and control, we analyzed the habitat characteristics of Himalayan marmots, developed Himalayan marmot information collection system V3.0 based on global navigation satellite system (GNSS), remote sensing, and geographic information system (“3S”) technology, and drew a predictive spatial distribution map of Himalayan marmots in Qinghai Province. Field survey data of 352 marmot plague sites in Qinghai Province were collected in 2014, and the data from 80 sample sites were included. The Himalayan marmot habitat characteristics were analyzed based on “3S” technology using five environment variables (elevation, slope, aspect, vegetation cover, and grass type) and the geographical coordinates. Himalayan marmot information collection system V3.0, which has been approved by the National Copyright Administration of the People’s Republic of China (No.00764743), was used to draw a predictive spatial distribution map of Himalayan marmots in Qinghai province. Moreover, from 2015 to 2017, positioning data of the plague-foci and plague-free areas in Qinghai Province were collected using GNSS receptor for field validations to verify the accuracy of the marmot predictive spatial distribution map. Elevation, slope, vegetation cover, and grassland type were identified as important environmental factors that determine the spatial distribution of Himalayan marmots. The suitable range of environmental features was 3400–4600 m elevation, 5°–20° slope, 0.60–1.00 vegetation cover, and alpine meadows. The Himalayan marmot predictive spatial distribution map in Qinghai Province based on “3S” technology and marmot information collection system V3.0 had a spatial resolution of 30 m. Field validation in areas of Qinghai Province revealed a prediction accuracy and mean absolute error of 0.8669 and 0.1331, respectively, which indicated excellent prediction accuracy. This study greatly improved the work efficiency of plague surveillance and effectively reduced the work intensity of researchers. Application of “3S” technology and marmot information collection system V3.0 has improved the data collection efficiency, provided new technical means for plague investigation and research, and provided a reference for development of plague surveillance programs. The research results will play a positive role in promoting the improvement and perfection of plague prevention and control strategies in Qinghai province and even in China.

Examining How Social Risk Factors Are Integrated Into Clinical Settings Using Existing Data: A Scoping Review.

Integrating social care into clinical care requires substantial resources. Use of existing data through a geographic information system (GIS) has the potential to support efficient and effective integration of social care into clinical settings. We conducted a scoping literature review characterizing its use in primary care settings to identify and address social risk factors. In December 2018, we searched 2 databases and extracted structured data for eligible articles that (1) described the use of GIS in clinical settings to identify and/or intervene on social risks, (2) were published between December 2013 and December 2018, and (3) were based in the United States. Additional studies were identified by examining references. Of the 5,574 articles included for review, 18 met study eligibility criteria: 14 (78%) were descriptive studies, 3 (17%) tested an intervention, and 1 (6%) was a theoretical report. All studies used GIS to identify social risks (increase awareness); 3 studies (17%) described interventions to address social risks, primarily by identifying relevant community resources and aligning clinical services to patients' needs. Most studies describe associations between GIS and population health outcomes; however, there is a paucity of literature regarding GIS use to identify and address social risk factors in clinical settings. GIS technology may assist health systems seeking to address population health outcomes through alignment and advocacy; its current application in clinical care delivery is infrequent and largely limited to referring patients to local community resources.

A New Discovery of Xu Xiake’s Whereabouts in Zhejiang Province from the Perspective of Digital Xu Studies

By extracting the tracing information of Xu Xiake’s travels to Zhejiang Province recorded in his The Travel Notes of Xu Xiake, this research constructed his tracing database by using geographic information system technology, and carried out detailed spatial analysis and visual expression of Xu Xiake’s travels. This research resolved two controversies. First, when Xu Xiake visited Zhejiang Province for the second time in 1616, he went into Kaihua (Anhui Province) from Changshan. After visiting Huangshan Mountain, he traveled across Tunxi to Changshan, then went through Jiangxi into Fujian Province. Second, on his way home after visiting Tiantai Mountain and Yandang Mountain for the third time, Xu Xiake and Xu Zhongzhao parted at Shangyu and went to Siming Mountain alone.

Rural Landscape Change: The Driving Forces of Land Use Transformation from 1980 to 2020 in Southern Henan, China

Rapid urbanization has had an important impact on the pattern and function of rural land use. To better understand the key drivers of the landscape pattern evolution in southern Henan in China from 1980 to 2020, we used techniques of GIS(Geographic Information System) technology and the geodetector model in the research area of landscape pattern evolution characteristics. The research results show that the land use transformation in the rural areas of southern Henan has been characterized by the conversion of production land to living land and ecological land, with the highest conversion rate and continuous growth of construction land, a decreasing trend of cropland, and continuous and stable growth of land for forest and water body in the past 40 years. Land use conversion in the rural areas of southern Henan is mainly concentrated in the northern, central, and southern areas, and the spatial conversion has shifted from mountainous areas to the plains. The center of gravity of forest, cropland, and water body has most obviously shifted, and human interference and ecological environment destruction are the main influencing factors. The overall landscape pattern in the rural areas of southern Henan has increased in fragmentation and landscape heterogeneity, evenness has decreased, irregular patches have increased, and landscape connectivity has decreased. The combined effect of the six dimensions of elevation, slope, night lighting, average annual precipitation, average annual temperature, and population density in the rural areas of southern Henan has led to the transformation of land use and changes in landscape pattern. Physical geographic factors are the main drivers of rural landscape pattern changes in southern Henan, while population density changes and urbanization are secondary drivers. The results of the study have important guiding significance for the further optimization of rural landscape patterns and the sustainable development of rural areas.

A novel integrated approach for monitoring drought stress in an aeolian desertification area using Vegetation Drought Status Index

Abstract Drought is a costly natural disaster. The accuracy and applicability of different drought indexes in drought monitoring at different research areas are also different. Based on remote sensing (RS) and geographic information system technology, we propose a new RS-based drought index, Vegetation Drought Status Index (VDSI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and data from 81 verification points regarding the in situ soil drought status. The model was applied to drought monitoring in Xinjiang Uygur Autonomous Region of China. Based on the comprehensive influence of water absorption on the reflectance spectrum of vegetation and soil, the reflectance of soil in MODIS bands B6 and B7 is generally higher than that of vegetation, so the model can directly obtain the surface soil moisture index. The correlation analysis (R2 > 0.79) was valid; the change trends were the same; i.e., VDSI is a reliable referential index for agricultural drought monitoring.

Geographic information systems applications in India's public health are we moving towards the right direction?

Governments worldwide focus particularly on digital healthcare sensors for leveraging data and technology like Geographic Information Systems (GIS) to improve governance and service delivery. Geoinformatics technology can help with epidemiological research and outbreak response, minimizing the health consequences in communities beforehand, during, and then after epidemic episodes. We can all agree that location and time play a crucial role in carrying out an efficient public health response. Since location information is essential for every stage of planning, response, and recovery, GIS helps the location-based support of public health preparedness programmes like support for decisions, resource allocation, communication and collaboration, and civic participation. GIS scales to situations ranging from adverse weather to pandemics. Public health professionals can coordinate their efforts with those of other organizations and external stakeholders due to maps and apps. The public health preparedness community may achieve significant strides by incorporating GIS data, models, communication and engagement centres, and location-centric apps. GIS technology can help with this efficient method for gathering data, performing analysis, where they are most needed, interacting with decision-makers, and finally achieving health equity can be created with the aid of a location-based strategy. During COVID-19, this reality was disseminated more extensively through the news media and the national, state, and local governments. This paper evaluates the application of GIS in the Indian public health system and the various aspects of public health where GIS may emerge as a game-changer for future policy decisions.

Student Engagement and Educational Benefits of Web GIS-Based Projects

The evolution of geographic information systems (GIS) based on the internet (Web GIS) created new opportunities for the use of GIS technology in education. In the present study a learning environment was developed which incorporates the project-based learning (PBL) methodology based on Web GIS technology. In this context, 3 research projects (GIS based projects) were designed and implemented. Project 1 and 2 combined fieldwork and GIS, and the topic was related to local community issues. Project 3 was an internal project since it was conducted only in the computer lab with data collected from the internet. In order to investigate the effectiveness of this learning environment in students' engagement, a quasi-experimental study was conducted on 58 senior high school students. A pre-test was conducted in advance and a post-test upon concluding all three projects. The results reveal the pedagogical value of the aforementioned learning environment, since it provides multiple pedagogical benefits, having a positive effect on students' engagement.

Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software.

Residential carbon emissions are one of the critical causes of climate problems such as global warming. It is significant to explore the development and evolution trend of residential carbon emissions research for mitigating global climate change. However, there have been no studies that comprehensively review this research field. Based on the research papers on residential carbon emissions included in the Web of Science core database and China National Knowledge Infrastructure database, the CiteSpace bibliometric analysis software was used in this paper to draw the visual knowledge map of residential carbon emissions research and reveal its research status, research hotspots, and development trend. We found that residential carbon emissions research has gone through the stage of "emergence-initiation-rapid development", and the research in the United States and the United Kingdom has played a fundamental role in developing this research field. Research hotspots mainly focus on analyzing energy demand, quantitative measurement, and impact mechanisms of residents' direct and indirect carbon emissions and low-carbon consumption willingness. The focus of research has gradually shifted from qualitative analysis based on relevant policies to the analysis of quantitative spatiotemporal measurements and drive mechanisms of direct and indirect carbon emissions from residential buildings, transportation, and tourism based on mathematical models and geographic information system technologies. Modern intelligent means such as remote sensing technology and artificial intelligence technology can improve the dynamics and accuracy of this research, but there are few related types of research at present. Based on these research status and trends, we proposed that the future research direction of residential carbon emissions should focus more on spatial analysis and trend prediction based on intelligent methods under a low-carbon background.

Predicting Soil Erosion Rate at Transboundary Sub-Watersheds in Ali Al-Gharbi, Southern Iraq, Using RUSLE-Based GIS Model

The empirical soil loss model, RUSLE, was used in conjunction with remotely sensed data and geographic information system technology to delineate the soil erosion and watershed priorities in terms of conservation practices at seven boundary sub-watersheds (labeled as SW-00, SW-01, …, SW-06) between Iraq and Iran in the district of Ali Al-Gharbi, southern Iraq. The six factors of the RUSLE model, i.e., the rainfall erosivity, the soil erodibility, the slope steepness length, the crop management, and management practice, were calculated or estimated using information from different data sources such as remotely sensed data and previous studies. The results revealed that the annual soil erosion loss ranges from 0 to 1890 (tons h−1 y−1) with an average of 0.66 (tons h−1 y−1). Values of soil erosion were classified into five classes: very low, low, moderate, high, and very high. The potential soil loss in the high and very high classes ranges from 14.84 to 1890 (tons h−1 y−1), and these classes occupy only 27 km2 of the study area, indicating that the soil loss is very low in the area being examined. In terms of the spatial distribution of soil loss, the northern and northeastern parts (mountains and hills) of the sub-watersheds where the slope is steeper are more likely to erode than the plain area in the southern and southeastern portions, indicating that slope, in addition to rainfall erosivity, has a dominant effect on the soil erosion rate. The study of soil erosion in the watersheds under consideration reveals that only the northern portions of the SW-00, SW-02, and SW-04 watersheds require high priority conservation plans; however, these portions are primarily located in mountain regions, making the implementation of conservation plans in these areas impractical. Due to low soil loss, other sub-watersheds, particularly SW-01, SW-03, SW-05, and SW-06, are given low priority.

Analysis of geothermal potential in Hangjiahu area based on remote sensing and geographic information system

Geothermal resources are one of the most valuable renewable energy sources because of their stability, reliability, cleanliness, safety and abundant reserves. Efficient and economical remote sensing and GIS (Geographic Information System) technology has high practical value in geothermal resources exploration. However, different study areas have different geothermal formation mechanisms. In the process of establishing the model, which factors are used for modeling and how to quantify the factors reasonably are still problems to be analyzed and studied. Taking Hangjiahu Plain of Zhejiang Province as an example, based on geothermal exploration and remote sensing interpretation data, the correlation between the existing geothermal hot spots and geothermal related factors was evaluated in this paper, such as lithology, fault zone distance, surface water system and its distance, seismic point distance, magmatic rock and volcanic rock distance, surface water, farmland, woodland temperature and so on. The relationship between geothermal potential and distribution characteristics of surface thermal environment, fault activity, surface water system and other factors was explored. AHP (Analytic Hierarchy Process) and BP (Back Propagation) neural network were used for establishing geothermal potential target evaluation models. The potential geothermal areas of Hangjiahu Plain were divided into five grades using geothermal exploration model, and most geothermal drilling sites were distributed in extremely high potential areas and high potential areas. The results show that it is feasible to analyze geothermal potential targets using remote sensing interpretation data and geographic information system analysis databased on analytic hierarchy process analytic hierarchy process and back propagation neural network, and the distribution characteristics of surface thermal environment, fault activity, surface water system and other related factors are also related to geothermal distribution. The prediction results of the model coincide with the existing geothermal drilling sites, which provides a new idea for geothermal exploration.

Optimization of escape routes during mine fire using GIS

ABSTRACT One of the essential factors in underground mining operations is safety during potential mine accidents, like fires, for example. We used Geographical Information System (GIS) for network analysis to identify the best escape routes from different locations inside an underground mine during a fire to a safe place inside or outside the mine. We created an effective evacuation system with a more accurate 2D model based on fire dynamics, the actual dimensions of the mine, and its related elements. We applied our system to the Maghara coal mine to predict the possible fire outbreak scenarios. Our results could be used as a guide to evacuation and rescue plans during harmful events in underground mines, especially fires. Our system is an integrated system that analyses potential fire scenarios and elects the best evacuation plan using GIS technology.