Geographic Information System Database Research Articles

DEVELOPMENT OF A GIS-BASED SYSTEM FOR IRRIGATION MANAGEMENT AND FLOW DISTRIBUTION

Abstract. The systematic management of irrigation operation of medium and large gravity irrigation systems (IS) significantly affects the delivery efficiency of irrigation from the source to the farm level. An interactive spatial database that integrates system data handling and irrigation distribution assessment functionality can be implemented, which has not yet been developed in existing NIS in the Philippines. This study proposes a framework model in developing a geographic information system (GIS) database for irrigation systems that integrates a GIS-based irrigation network distribution analysis model. The creation of a model framework for the development of a functional GIS-based database for IS management and flow distribution is comprised of five stages namely: (1) selection and determination of significant irrigation system data, (2) determination of required estimation parameters, (3) GIS development, (4) irrigation network distribution model development, and (5) selection of applicable water distribution assessment techniques. The GIS-based database and integrated model for irrigation provide essential data records of the irrigation system that could easily be viewed systematically and integrated into any GIS. In addition, data packages can easily be shared and run even on low-end computers. Features of the proposed irrigation model include flow volume simulation, time of delivery simulation, irrigation scheduling, remote sensing model integration for automated evapotranspiration computation, volumetric assessment, assessment of the timing of irrigation, and distribution optimization.

An “Earthly” Manifestation of Power: The Architectural and Urban Contexts of Iznik Tiles Produced under the Patronage of the Ottoman Palace

ABSTRACT Çini-making, which is an Ottoman underglaze ceramics tradition included in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity, was used extensively in the monumental architecture of the Ottoman Empire in the fifteenth and seventeenth centuries, including mosques built by the Ottoman sultans and their family members. Iznik tiles reveal undeciphered relationships between Ottoman patronage and monumental architecture, and unwritten architectural rules and norms concerning tile designs. To contribute to the conservation, interpretation, and presentation of the mosque spaces, it is important to understand the tangible and intangible cultural qualities of Iznik tiles. This study examines the cultural qualities of Iznik tiles by analysing the historical and architectural qualities of the five selected sultanic mosques and by integrating the literature and field surveys through Geographic Information Systems (GIS) modelling and database. We aim to analyse and develop an evaluation model to investigate the cultural qualities of Iznik tiles on regional, urban, and architectural scales using GIS.

Integrating GIS and BIM with MCDM for infrastructure planning: a comprehensive framework

PurposePresent study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.Design/methodology/approachThis analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.FindingsA comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.Research limitations/implicationsThe study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.Practical implicationsThe proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.Originality/valueBy integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

An open geospatial database as a tool for geoheritage management at national scale: The case study of Greece

Geological heritage or geoheritage is of at least equal significance to – and sometimes also interwoven with – cultural heritage. Hence, it holds the potential of scientific, educational, cultural, aesthetic, and touristic value. Nevertheless, geoheritage has not attracted the same level of attention as cultural heritage to date, especially regarding its sustainable management and suitable conservation strategies. Yet actions and measures are mandatory to preserve and highlight geological heritage and to reduce threats that may cause its deterioration or even extinction. To this end, geospatial science and technology provide the means for documenting and dealing with geological heritage throughout the individual steps of the geoheritage management process. In this study, we present a holistic approach to geoheritage management at the national level for Greece, based on the implementation of a Geographical Information Systems (GIS) database that also enables the coupling of geoheritage with a plethora of readily available geospatial information from remote sensing and other sources. The results demonstrate that an appropriate, geospatial record of geological heritage can have a crucial contribution to geoheritage management from its identification, to its monitoring, protection, and exploitation for educational, scientific, recreational, and other purposes.

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

A Framework for Data Collection and Management to Support the Comprehensive Development of African Agriculture

Agriculture is a key source of food and health for humanity. However, the hazardous way this activity is still cared for in some developing countries like the Republic of Benin, with poor quality farming data collection mechanisms, can guarantee neither quality and sustainability of production, nor comprehensiveness and efficiency of agricultural interventions. Such shortcomings ineluctably contribute to the limited success of the Comprehensive African Agriculture Development Program (CAADP) promoted by the African Union and its regional chapters, in most African countries. Based on these preliminary remarks, this paper suggests a systematic and automated monitoring, evaluation, learning, and adaptation (MEL) framework that will further be tested, consolidated, validated, and promoted, first in the Republic of Benin, and later in other African countries. This framework is made of local, regional, and national geographic information systems (GIS) which will further serve as databases for planning and interventions in the agricultural sector. The proposed GIS superposes lands’ details (sizes, soils’ characteristics, rainfall data, etc.), stakeholders’ details (demographics, crops, livestock, agricultural practices, etc.), and agricultural value chains’ details. Local, regional, and national agricultural value chains’ development platforms will bring stakeholders together, and serve as sources and co-users of the GIS data. Local, regional, and national MEL and GIS management skilled AEAS agents will facilitate systematic data collection and GIS database management. The data collection and treatment tools should allow the use of all kinds of data formats, including voice and image records, given the oral orientation of most agricultural stakeholders. This original idea emerged from observations during national and regional field experiences, and from the literature that revealed the efficiency of automated real-time and quality data for comprehensive and evidence-based decision-making in development interventions.

Impact of Land Use and Land Cover Change on Deforestation in the Central Taraba State: A Geographic Information System and Remote Sensing Analysis

Deforestation, the widespread clearance of forests for various land uses, has become a significant global environmental issue with far-reaching consequences. Deforestation in the study area was identified, categorized, assessed, and interpreted using Landsat 5, 7, and 8 from the years 2008, 2014, and 2020, respectively. A geographic information system (GIS) database of land use and land cover categories and their changes were created. The results showed that several anthropogenic human activities, including agriculture and wood harvesting, were driving a general retreat of the forest area. The study further shows that between 2008 and 2020, forest area decreased by 8.5% with an annual rate of 0.33%, other vegetative areas increased by 2% with an annual rate of 0.077%, and non-vegetative areas increased by 1.5% with an annual rate of 0.58%. The hotspot map shows that the rate at which the reserve is deteriorating and the conversion of the forest area to other vegetation and bare ground are only a matter of time. The study recommended that the government should adopt rigorous policies to protect forest reserves from unauthorized habitation by encouraging the use of alternative firewood fuel sources to reduce the pressure on the forest.

Reducing non-revenue water in Luxor-Egypt using GIS

Unaccounted for Water, referring to all losses from the water system, illegal or otherwise, frequently exceeds 40% of all water produced. This represents a major strain on utility operations and budgets. A large amount of Unaccounted for Water indicates large losses in the revenue of the utilities. Deficiency in financial resources normally leads to a deficiency in the performance of the utility. This deficiency in performance may affect the operation and maintenance of treatment plants and network components. The main objective is to reduce Unaccounted for Water rates. The multi-stage systematic process involves measuring water flow, leak detection, installing and fixing meters, reducing illegal connections, and increasing billing and collections, and developing Geographic Information System layers and databases for all of them. Another objective is to connect Geographic Information System maps with Billing, hydraulic systems, hotline systems, etc. After choosing a pilot area, we review similar activities, conduct field visits, and hold meetings, prepare the timeline schedule, prepare, and implement base maps and the water network, analyze, and compare end-line results, and report the results. A high reduction in the Unaccounted for Water percentage is expected due to fixing the discovered leaks. The issue of Unaccounted for Water, encompassing losses from the system, whether legal or illegal. This places a significant burden on both utility operations and budgets. A substantial volume of Unaccounted for Water signifies considerable revenue losses for the utilities, and the financial constraints often translate into performance deficiencies within the utility. We formulated a methodology aimed at assisting utilities in lowering Unaccounted for Water rates. This structured, multi-stage process includes activities such as measuring water flow, implementing leak detection measures, installing, and repairing meters, minimizing illegal connections, and enhancing billing and collection.

An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.

Global Geospatial Data for Flood and Landslide Susceptibility Mapping

Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.

Fostering Urban Cohesion: Exploring Morphological Adaptations in Budapest’s IX District through a Typological Survey

In response to the imperative to enhance urban structures for global sustainability and improved quality of life, the European Union has diligently established parameters and policies fostering urban cohesion and territorial integration. Embracing the guidelines conceived by the European Commission, this research presents a case study examining morphological conditions in the IX District of Budapest, Hungary—a strategically chosen area undergoing renovation. The primary goals of the intervention are to address social and spatial segregation, enhance urban performance, and promote global resilience. Employing a Typological Survey methodology, an in-depth assessment was conducted and translated into a Geographic Information System (GIS) database. Consequently, the morphological analysis successfully identified five distinct types of elements composing the urban structure of the studied area. This analysis revealed a highly heterogeneous constitution characterized by dynamic and continuous changes, reflecting the evolving nature of the urban landscape. Findings indicate noteworthy improvements in the performance and quality of public spaces while preserving the historical morphological characteristics that have long defined this area and its urban landscape.

Quality Management in GIS

This paper centers on the crucial elements of quality control (QC) and quality assurance (QA) in the domain of geographic information systems (GIS). A dependable and resilient GIS database is at the core of any spatial system, and it is of utmost significance across a wide range of fields, including Hajj, Umrah, tourism, real estate, and natural resource management. Given the inherent intricacy of spatial databases and their requirement for specialized technical upkeep, applications that depend on them also necessitate specialized technical expertise. To address inconsistencies within the database, best practices for database maintenance are implemented. This paper presents a systematic approach to the updating of comprehensive spatial databases and provides an overview of the quality procedures that are essential to this process. It also discusses the various safeguard mechanisms that are employed to enhance data consistency, completeness, integrity, and other qualitative aspects. The primary objective is to establish a customer-focused quality standard for spatial databases. Considering the broad definition of quality as "the extent to which project deliverables meet requirements," GIS projects face the challenge of precisely defining specifications and relevant quality standards that are tailored to specific project types and deliverables. The increasing demand for digital maps and GIS applications in Egypt emphasizes the urgent need for quality control and quality assurance procedures. This paper describes the implementation of these procedures in the creation of ten geological maps of Egypt at a scale of 1:500,000. This collaborative effort involves Cairo University and the Ministry of Water Resources and Irrigation in Egypt. Additionally, this project provides an internship opportunity for students in the College of Engineering, Civil Department, enabling them to gain practical experience in the field.

Predicting Rainfall-Induced Landslides in Muriwai, Auckland: A Comprehensive Multi-Model Approach for Enhanced Risk Management

Landslide studies hold significant importance in understanding and mitigating the risks associated with these geohazards. Particularly in regions like New Zealand, where diverse topography and climatic conditions contribute to frequent landslide occurrences, such studies are crucial. This research focuses on Muriwai, Auckland, a region that has experienced substantial landslide activity triggered by rainfall. The study adopts a multi-model approach, integrating both empirical and process-based models to predict rainfall-induced landslides. This approach leverages the strengths of individual models, including logistic regression, random forest, support vector machines, artificial neural networks, and decision trees, thereby enhancing the robustness and accuracy of predictions. A comprehensive dataset, comprising historical landslide records, climatic data, and terrain and geological data, is used to train and validate these models. The data is spatially aligned within a unified Geographic Information System (GIS) database, ensuring consistency and accuracy in the analysis. The multi-model ensemble provides a probabilistic prediction of landslide occurrence, which is visualized as a landslide susceptibility map. This map serves as a valuable tool for understanding the spatial distribution of landslide risks in Muriwai. The performance of each model and the ensemble is evaluated using several metrics, ensuring the reliability of the predictions. The results are interpreted to understand the influence of different factors, particularly climatic parameters, on landslide occurrences. This study's findings contribute to effective landslide risk management and mitigation strategies in Muriwai, Auckland, and provide valuable insights for similar studies in other regions.

DEVELOPMENT MODEL FOR VIRTUAL REALITY (VR) TOURISM IN RURAL AREAS: A GIS-BASED APPROACH

This paper provides an overview of a development model for virtual reality (VR) tourism using Geographic Information System (GIS) data and spatial distribution analysis of potential tourism products. The study employs a mixed-methods approach, combining qualitative and quantitative data collection techniques. Qualitative methods, such as interviews and focus groups, gather insights from stakeholders, including tourists, local communities, and tourism industry professionals, regarding their perceptions, expectations, and preferences. Quantitative methods, such as surveys and data analysis, provide a comprehensive understanding of visitor demographics, preferences, and the economic impact of VR tourism. The findings of the study focus on identifying and analysing the components of tourism products and data information specifically within the rural area of the Perak Tengah District. Information about tourism products is collected and encoded into a GIS database. The introduction of a new classification system justifies the development model and outlines a detailed process for each stage of the framework's methodologies. The originality and value of this research used a mixed-methods approach to gather qualitative and quantitative data, illustrating the overview of VR tourism development using the GIS tool approach specifically for tourism products in rural areas.

A WORKFLOW FOR VALIDATION AND EVALUATION OF A DYNAMIC VISUALIZATION PLATFORM FOR UNDERGROUND ANTIQUITIES

Abstract. Smart Eye is a research project focused on developing and operating, in real-time, an innovative system, that allows the visualization of invisible, known, covered by the ground or by constructions, monuments and finds in an archaeological site, using an Augmented Reality (AR) environment. The visitors of an archaeological site will be able to observe the covered antiquities (3D models and descriptive information) on the screen of their mobile device (smart phone and/or tablet). The system integrates mobile positioning and pose estimation technologies, AR algorithms, Geographic Information Systems (GIS) and spatial databases. The AR application is implemented by IT scientists and is subsequently evaluated in the field to identify both technical problems (e.g. are files uploaded at high speed?) and problems identified by the average user of the AR application (e.g. would it be better if the button to display the 3D model of the archaeological excavation was larger on the screen or of a different color?). The paper will present, on one hand, the validation and evaluation protocols according to the relevant literature, and on the other, the exact evaluation methodology of the Smart Eye system. In addition, the problems identified and the way they were solved during the 1st evaluation of the application will be presented, as well as the problems identified during the 2nd evaluation.

Development of a geographic information system for monitoring work with customers for the supply, maintenance and automation of elevator equipment for LLC «Expert-Agro»

As part of the production practice in LLC «Expert-Agro» developed a geographic information system (GIS) for the supply and maintenance of the produced elevator equipment. In the MS Access system, a database of customers has been formed that contains the names of enterprises, addresses and coordinates of their location, contact information, information on orders (purchase, delivery and installation of equipment, maintenance of existing equipment, repair work, etc.). At the same time, a message is automatically generated when the day of completion of the order approaches, which allows you to control the efficiency of working with customers and, if necessary, extend the lead time of the order. In the MS Visual Studio integrated environment, using the gmap.Net library, geolocation was implemented to display customers on the map, which makes it possible to assess the geography of LLC «Expert-Agro» and plan further expansion of interaction. Controlling the location of customers also provides a solution to logistical problems for the supply of equipment. The system additionally provides for the selection of process parameters sensors, actuators, control controllers with input / output modules and other devices for automating the supplied equipment. At present, the GIS database is being filled with information on manufactured equipment and devices that are used in standard automation projects.

Associations of residential greenness with unhealthy consumption behaviors: Evidence from high-density Hong Kong using street-view and conventional exposure metrics.

Residential greenness was theoretically associated with health-related consumption behaviors concerning the socio-ecological model and restoration environment theory, but empirical studies were limited, especially in high-density cities. We examined the associations of residential greenness with unhealthy consumption behaviors (infrequent breakfast consumption, infrequent fruit consumption, infrequent vegetable consumption, alcohol drinking, binge drinking, cigarette smoking, moderate-to-heavy smoking, and heavy smoking) using street-view and conventional greenness metrics in high-density Hong Kong. This cross-sectional study employed survey data from 1,977 adults and residence-based objective environmental data in Hong Kong. Street-view greenness (SVG) was extracted from Google Street View images using an object-based image classification algorithm. Two conventional greenness metrics were used, including normalized difference vegetation index (NDVI) derived from Landsat 8 remote-sensing images and park density derived from a geographic information system database. In the main analyses, logistic regression analyses together with interaction and stratified models were performed with environmental metrics measured within a 1000-m buffer of residence. A standard deviation higher SVG and NDVI were significantly associated with fewer odds of infrequent breakfast consumption (OR=0.81, 95% CI 0.71-0.94 for SVG; OR=0.83, 95% CI 0.73-0.95 for NDVI), infrequent fruit consumption (OR=0.85, 95% CI 0.77-0.94 for SVG; OR=0.85, 95% CI 0.77-0.94 for NDVI), and infrequent vegetable consumption (OR=0.78, 95% CI 0.66-0.92 for SVG; OR=0.81, 95% CI 0.69-0.94 for NDVI). The higher SVG was significantly associated with less binge drinking and the higher SVG at a 400-m buffer and a 600-m buffer were significantly associated with less heavy smoking. Park density was not significantly associated with any unhealthy consumption behaviors. Some of the above significant associations were moderated by moderate physical activity, mental and physical health, age, monthly income, and marital status. This study highlights the potential beneficial impact of residential greenness, especially in terms of street greenery, on healthier eating habits, less binge drinking, and less heavy smoking.

158 Associations Between County Level Poverty and Firearm Injuries in the State of Maryland

OBJECTIVES/GOALS: The United States is experiencing an epidemic of firearm deaths and injuries. Poverty and other socioeconomic factors have been linked to firearm injuries on the national level. The goal of this study is to examine the relationship between county level poverty and firearm injuries in the State of Maryland. METHODS/STUDY POPULATION: This is a cross sectional study assessing fatal and non-fatal firearm injuries of all ages between 2018-2020 utilizing data from the State of Maryland’s Health Services Cost Review Commission. Our primary analysis will involve calculating injury and mortality rates to assess if fatal and non-fatal firearm injuries are associated with county-level poverty, defined as the percentage of the population living below the federal poverty line. Rates will be calculated by determining county level population within subgroups using the National Historical Geographical Information System database. We will also conduct regression analyses to adjust for confounding variables selected based on evidence from prior research. Some of these variables include age, sex, race, urbanicity, and the social vulnerability index. RESULTS/ANTICIPATED RESULTS: An abundance of prior research has demonstrated differences in firearm injury by age, sex, and race. Prior studies have also shown that poverty is associated with higher rates of firearm-related deaths among youth. Based on that foundational data, we anticipate that regression analyses will demonstrate that counties with higher poverty levels will have higher rates of fatal and non-fatal firearm injuries, even after controlling for other known risk factors. DISCUSSION/SIGNIFICANCE: Findings from this study will contribute to growing evidence on the role of poverty in the burden of firearm injuries and mortality. This will have policy implications regarding the allocation of public health resources and interventions aimed at reducing firearm-related injuries and deaths in Maryland.

Evaluation of database and some soil characteristic of Kumkale Agricultural Enterprise soils in GIS

General Directorate of Agricultural Enterprises (GDAE), which has been operating since 1994, is a state institution whose aim is to protect the gene resources as well as the production of seeds, breeding and raw materials for agriculture and agriculture-based industry. There are more than 30 agricultural enterprises established for similar purposes in Türkiye. One of these enterprises is Kumkale Agricultural Enterprise (KAE) located within the borders of Çanakkale province. The aim of this study is to reveal the current potentials of KAE lands, for which detailed studies have been made before, to create a database in Geographic Information System (GIS) and to produce various thematic maps. In this study, important soil characteristics such as slope, soil depth, surface stones, drainage, as well as soil series and physiographic units included in the survey report were mapped and the areas covered were calculated in GIS. With the digitization has determined that the agricultural enterprises lands in question are spread over an area of 7309 decares in total. In terms of soil series, the highest distribution area belongs to Karabatak series (948.2 da), while Alluvial soils (3094.8 da) have the highest distribution area in terms of physiographic units. In addition, most of the lands consist of 0-2% slope (4478.9 da), deeper than 120 cm (5936.8 da), stone-free (5677.2 da) and without drainage problems. As a result, it will be useful to examine both the temporal and spatial changes of the lands of GDAE, which has a significant production capacity in Türkiye, to make new field studies and compare them with the old survey studies in terms of guiding future studies.

USING MACHINE LEARNING COUPLED WITH REMOTE SENSING FOR FOREST FIRE SUSCEPTIBILITY MAPPING. CASE STUDY TETOUAN PROVINCE, NORTHERN MOROCCO

Abstract. Currently there is a public awareness to protect the environment especially forest ecosystems and the forest fire dilemma has become a topic of intense research around the world. In this setting, this study evaluates forest fire susceptibility (FFS) in northern Morocco using three geographic information system (GIS) based on machine learning algorithms: XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). To this effect, a Geographic Information System (GIS) database was developed involving ten independent causal factors (elevation, aspect, slope, distance to roads, distance to residential areas, land cover, normalized difference vegetation index (NDVI), rainfall, temperature and wind speed) and 345 fire pixels. The 345 pixels were split into two sets for training (70%) and validation (30%) and the spatial relationships between factors affecting FFs and fire pixels was analyzed using XGB, RF, and SVM models to generate the FFS maps. The effectiveness of the models was evaluated using the receiver operating characteristic curve, the area under the curve (AUC), and several statistical measures. The results of the three models hinted that XGBoost had the highest performance (AUC = 0.856), followed by RF (AUC) = 0.827), and SVM (AUC = 0.803) in the forecasting of the forest fire. The resulting FFS maps areas can provide crucial support for the management of Mediterranean forest ecosystems and can enhance the effectiveness of planning and management of forest resources and ecological balances in these areas.