Advanced GIS 2024-04-02T14:39:16+00:00 Asst. Prof. Lütfiye KUŞAK Open Journal Systems Wildfire hazard and risk assessment: The case of Gabala district 2023-08-22T13:00:32+00:00 Firuza Aghayeva <p>As one of the main natural resources for humans, protection of forest resources is one of the main ecological problems of the world. Forests are a source of oxygen, as well as they have some features that can ensure ecological balance. For example, forests are one of the major factors that prevent landslides, erosion processes, flood events, as well as protecting land resources, hydrological resources and optimization micro climatic condition. Decreasing of forest stock affects the fauna directly. There are some factors that impact decreasing of forest resources, for example, settlements, industry and forest supply and etcetera. Forest fires occurring in different parts of the world every year eradicated acres of forest stock. The formation of forest fires is influenced by factors such as climate, anthropogenic and topological effects. Research area is district of Gabala, which is situated at south slopes of Greater Caucasus. Gabala is distinguished by the abundance of forest resources in the territory of Azerbaijan, which is poorly provided with forest reserves. 32405.15 hectares of this district are covered with forests. Taking into account that 40% of the fire incidents that occurred on the southern slope of the Greater Caucasus in 2021 and 2022 took place here, this place was taken as a research area. Wildfire hazard and risk assessment and fire risk zonation, anthropogenic and topological effects are considered in this article and mapping had been done. The resulting values were classified according to the risk group and the results were compared with the fire area data. As a result of the comparison, was not found fire process in the categories of no risk or low risk. 90% of fire incidents could be classified as medium risk, high risk and critic high-risk categories. Consequently, this is an indicator of the validity of the selected parameters and the conducted assessment</p> 2024-01-02T00:00:00+00:00 Copyright (c) 2023 Advanced GIS Comparative evaluation of the performance of different regression models in land valuation 2024-01-25T11:53:29+00:00 Şükran Yalpır Erol Yalpır <p>Lands can play a dominant role in the real estate market, especially due to their legal zoning rights. These properties are preferred investment options compared to financial instruments due to factors such as high returns and long-term reliability. Today, Machine Learning (ML) algorithms are used to accurately determine the land value. Regression models, capable of handling complex relationships, integrating Geographic Information System (GIS), and providing a comparative approach, lead the way among these algorithms. In this study, Lasso, Elastic-Net, ML.Net, and Ordinary Least Squares (OLS) regression models were employed to predict land values in the central neighborhoods of Konya's Selçuklu, Meram, and Karatay districts. The datasets containing legal, physical, spatial, and local criteria of 440 lands were obtained, and GIS analyses were conducted to prepare the spatial data. Based on the modeling results, it can be observed that ML.Net exhibited successful performance with metric values of MAE=0.043, MSE=0.005, RMSE=0.060, and R2=0.82. Comparatively, ML.Net's 9% superior performance compared to the commonly encountered OLS in the literature is of significant importance. The results demonstrated the usability of various regression models for land valuation and highlighted that ML.Net can yield improved outcomes, particularly in modeling high-market-value lands.</p> 2024-02-07T00:00:00+00:00 Copyright (c) 2024 Advanced GIS Land use and land cover classes affected by the possible sea level rise in Mersin city center (Türkiye) 2024-01-13T09:08:06+00:00 Onur Güven Ümit Yıldırım Cüneyt Güler Mehmet Ali Kurt <p>In this study, a sea level rise (SLR) investigation was carried out in an area representing the Mersin city center located in the south of Türkiye. The study area covers an area of <em>ca</em>. 385 km<sup>2</sup>. Future projections provided by the Intergovernmental Panel on Climate Change (IPCC) were used for the SLR assessment. These projections are for the years 2100, 2200, 2300, 2400, and 2500 and the SLR for these periods are 0.83 m, 2.03 m, 3.59 m, 5.17 m, and 6.63 m, respectively. It is aimed to determine the areas affected by the SLR that will occur according to these projections. In this context, land use and land cover (LULC) data were obtained from the CORINE 2018 dataset. The data obtained were adapted within the boundaries of the study area and processesed using various GIS analyses. The results have shown that all LULC classes are greatly affected by the SLR, but in varying degrees. Land losses as a result of SLR are as follows: 0.4% at 0.83 m SLR, 9.8% at 2.03 m SLR, 16.7% at 3.59 m SLR, 21.6% at 5.17 m SLR, and 25% at 6.63 m SLR.</p> 2024-03-19T00:00:00+00:00 Copyright (c) 2024 Advanced GIS A geo-spatial analysis of precipitation distribution and its impacts on vegetation in Rwanda 2024-01-25T14:22:09+00:00 Rosette Ambiance Shema Li Lanhai <p>Rwanda is home to a diverse and picturesque landscape that encompasses a range of ecosystems, including rainforests, savannas, and agricultural regions. The intricate relationship between rainfall patterns and vegetation types shapes these varied landscapes, which are crucial for supporting biodiversity and agricultural productivity across the country. Our comprehensive geospatial analysis employs advanced geographical information systems (GIS) techniques and remote sensing data to assess spatial and temporal variations in precipitation across distinct regions of the country. Utilizing historical precipitation data and satellite-derived vegetation indices, our analysis spans extensive periods, incorporating annual and monthly rainfall records from 1990 to 2020 and MODIS/Terra Vegetation Indices spanning 2000 to 2020. Advanced remote sensing methodologies are employed to investigate the correlations between precipitation patterns and vegetation dynamics. The study reveals discernible spatial variations in Rwanda's precipitation distribution, elucidating marked seasonal fluctuations. Identified regions experiencing notable changes in precipitation levels exhibit a direct impact on vegetation health and density. Recorded annual rainfall data illustrates variations across different years, indicating fluctuating levels such as 1160.1 mm (1990), 1078.2 mm (2000), 1402.4 mm (2010), and 1391.1 mm (2020). Corroborating NDVI imagery demonstrates increased vegetation cover in 2010 and 2020, aligning with higher recorded rainfall during these years. The research underscores the significance of these findings in understanding the intricate interplay between precipitation distribution and vegetation dynamics and offers actionable insights essential for sustainable land management, optimized resource allocation, and the formulation of resilience-building strategies. These insights are particularly crucial in the context of adapting to and mitigating the effects of climate change.</p> 2024-03-27T00:00:00+00:00 Copyright (c) 2024 Advanced GIS Assessment of flood susceptibility utilizing remote sensing and geographic information systems: A case study of Mpazi sub-catchment in the city of Kigali 2024-02-25T13:18:38+00:00 Patience Manizabayo Hyacinthe Ngwijabagabo Isaac Nzayisenga Sabato Nzamwita Laika Amani Eugene Uwitonze Katabarwa Murenzi Gilbert <p>The Mpazi sub-catchment has been facing recurring floods, which pose significant threats to the community and environment. However, GIS technology has proven to be a valuable tool in assessing flood risks and vulnerability in the region. By analyzing spatial data such as land use, elevation, and rainfall patterns, detailed flood maps can be generated to simulate flood scenarios and develop effective management plans. The study conducted in this region revealed that there is a high susceptibility to flood hazards, particularly during the rainy season. The study identified the most vulnerable areas in the region and categorized them as follows: very high risk (39.74%), high risk (13.02%), moderate risk (30.22%), low risk (5.12%), and very low risk (11.9%). It is important to note that floods not only impact the environment but also infrastructure, such as residential and commercial buildings. The insights provided by this study are invaluable for stakeholders in developing effective flood management strategies to mitigate. Hence, all concerned government departments and citizens should collaborate actively to alleviate the ongoing rise in flooding and its impact. Adherence to land use and zoning regulations is crucial in this regard to address the issue effectively.</p> 2024-04-02T00:00:00+00:00 Copyright (c) 2024 Advanced GIS