Journal of Sustainable Urban & Regional Development Studies (JSURDS)

Abstract Background and Objective: Optimal location selection for agricultural areas refers to the process of identifying the best lands for crop cultivation while considering various factors such as climatic conditions, soil properties, and water resources to enhance productivity and sustainability. This study aims to identify suitable agricultural areas in Ardabil County by utilizing remote sensing data and the Analytical Network Process (ANP).

Methodology: In this research, environmental factors influencing agriculture were first identified and classified into four main categories: topography (elevation, slope, aspect), climate (temperature, precipitation, evapotranspiration), water resources (surface runoff, soil moisture, groundwater reserves, distance from streams), and land cover (soil texture, land use, NDVI). Satellite data from reliable sources such as MODIS, CHIRPS, GRACE, Landsat 8, and TerraClimate were used, and necessary analyses were conducted within the Google Earth Engine environment. The weighting of criteria was carried out using Super Decisions software and ANP analysis. Finally, the optimal agricultural area map was generated using ArcGIS software.

Findings and Conclusion: The results of the study indicated that the western and southeastern regions of Ardabil County have high potential for sustainable agriculture due to favorable climatic conditions, adequate water resources, and suitable topography. In these areas, factors such as elevation, precipitation, soil moisture, and slope have had positive impacts on agricultural growth and productivity. In contrast, the central and eastern regions face more challenges in agriculture due to reduced precipitation, low elevation, and limited water resources. The increase in evapotranspiration and the lack of groundwater reserves highlight the need for modern irrigation methods and the cultivation of drought-resistant crops in these areas.

Abstract Background and Objective: Landslides are among the most common natural hazards, causing extensive economic, environmental, and human losses. This study aims to assess and map landslide susceptibility in the Siah Rud watershed located in Gilan Province, using a hybrid approach that integrates the Analytic Network Process (ANP) model with Geographic Information Systems (GIS).
Methodology: This applied, descriptive-analytical research utilizes the ANP model within the GIS environment to delineate landslide hazard zones in the Siah Rud watershed. Ten influential factors were selected for the analysis: land use, elevation, slope, slope aspect, vegetation cover, precipitation, geological formations, and distances from roads, rivers, and faults. The relevant thematic layers were derived using various data sources, including DEMs, Landsat 8 and Sentinel-2 satellite imagery, geological maps, and precipitation maps. Analytical tools in ArcGIS were employed to extract certain layers, such as distance from rivers and roads. The ANP model, which surpasses AHP in modeling interdependencies between criteria, was used for weighting and final analysis.
Results and Findings: According to the ANP weighting results, the variables with the highest influence on landslide occurrence were slope (0.27), geological formations (0.24), and precipitation (0.16). Spatial analysis further revealed that areas with steep slopes, weak volcanic and sedimentary formations, high rainfall, and proximity to faults and rivers had greater instability potential. In contrast, regions with dense vegetation, forest land use, and significant distances from roads and faults showed higher stability. The final GIS-based hazard zonation map classified the area into five risk levels, with 36% of the watershed falling into high and very high hazard zones. These high-risk areas were primarily concentrated in the upstream parts of the watershed, where the combination of steep topography, high rainfall, sparse vegetation, and unstable geological conditions contributes to widespread landslide susceptibility.

Abstract Background and Objective: Multi-criteria decision-making methods combined with Geographic Information Systems (GIS) enable the simultaneous evaluation of various environmental, infrastructural, and security criteria, assisting in the optimal selection of locations for the establishment of military bases. Accordingly, the aim of this study is to locate military bases in Meshginshahr County using the Analytic Network Process (ANP) model and GIS.
Methodology:To achieve the research objective, a survey method was first employed by designing a questionnaire to collect expert opinions on the factors influencing the siting of military bases in the study area. In the next step, necessary data layers were digitized based on existing maps, and a database was created in the ArcGIS environment. Data weighting was carried out using the ANP method. Finally, the zoning map of the study area was prepared by integrating and overlaying the selected layers.
Findings and Results: The results indicated that 15.67% of the county's area falls within the "highly suitable" class and 17.97% within the "suitable" class, mainly located in the central and eastern parts of the county. In contrast, 25.55% is categorized as "unsuitable" and 21.48% as "highly unsuitable," primarily found in the mountainous and steep western and southern regions.This study demonstrated that integrating multi-criteria decision-making models with GIS spatial analysis can significantly assist in optimizing the site selection of military bases while minimizing environmental and security conflicts.

Abstract Background and Objective: Floods are one of the natural phenomena that can cause significant damage to infrastructure, farmlands, and the environment. This phenomenon primarily occurs due to heavy rainfall, snowmelt, or a combination of these factors. Therefore, the aim of this study is to map flood hazard zones and examine their relationship with land use using the Analytic Network Process (ANP) model in the Razi Chay watershed in Ardabil Province.
Methodology: In this study, data from Landsat 8 satellite imagery from 2022, a 30-meter ASTER DEM map, a 1:50,000 scale topographic map, a 1:250,000 scale geological map, and other detailed information of the studied watershed were utilized. Ten parameters influencing flood occurrence were analyzed, including elevation, slope, slope aspect, vegetation cover, geological formations, distance from the river, flow direction, land use, precipitation, and drainage density. The Analytic Network Process (ANP) model was employed to determine the importance of each variable.
Findings and Conclusion: Among the studied parameters, slope (with a weight of 30%), elevation (with a weight of 21%), and land use (with a weight of 17%) were assigned the highest weights, indicating their significant influence in controlling flood occurrence in the Razi Chay watershed. The results show that approximately 36% of the Razi Chay watershed falls within high-risk and very high-risk zones. These areas are typically located in the lower part of the watershed, often at the confluence of the two main streams. Considering the spatial distribution of settlements in the region, it can be concluded that most of the settlements in the lower part of the watershed are exposed to flood risks