Journal of Spatial Analysis

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Soil erosion is one of the environmental problems that is a threat to natural resources, agriculture and the environment, and in this regard, assessing the temporal and spatial amount of soil erosion has an effective role in management, erosion control and watershed management. The main aim of this study was to estimate soil erosion in Amoqin watershed and its relationship with well-known vegetation-based and topographic-related indices. The meteorological data has been used to determine the rainfall erosivity. The rainfall erosivity index was calculated using the modified Fournier index during the 10-year available recorded rainfall data. The value of LS factor has been calculate using digital elevation model. Meanwhile, C and P factors were determined based on the utilization scheme and condition of the study area. Data were analyzed and processed using ArcMap 10.1, ENVI 5.3, and Excel software. In this study, RUSLE model was used to estimate soil erosion, in GIS environment. According to the results, the amount of factor R in Amoqin watershed varies from 12.32 to 50.52 MJ/ha/h per year. The variation of soil erodibility index (K) over the study area is between 0.25 to 0.42. The amount of LS factor varies between 0.19 and 0.38, which is more in high slopes, especially around the waterways and uplands of the study area. The variation of C factor was estimated to be around -0.18 to 0.4. In general, it can be said that the central part of Amoqin watershed has less C value due to the greater area of agricultural activities and the highest amount is related to western areas, especially southwest areas because existing the rangeland areas. Due to the lack of protective measures in the study area, the amount of factor P was considered as unity for the whole region. The base layers of RUSLE factors were obtained and overlayed in GIS to calculate the soil loss in tons per hectare per year. The map of annual soil loss indicate that the erosion amounts varies between 1.21 to 5.53 tons per hectare per year in different parts of the study area. According to the results, the vegetation factor with a coefficient of determination 0.47% had a significant correlation with soil loss. The stream power index with the coefficient of determination of % 0.07% had the lowest correlation with soil erosion values.

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Investigation of Construction Wastes Release in Roadside Using AHP

Introduction
Although construction waste is an integral part of municipal waste, due to the differences between this waste and waste and environmental issues, a suitable model should be designed for optimal productivity and acquisition of resources. The increasing volume of urban materials and rubbish, especially the rubbish from the destruction of their construction and worn-out urban textures, has created many problems in large cities, as well as environmental problems that have arisen due to unprincipled and unprofessional disposal of these materials. Has attracted these materials. Research shows that the amount of this waste is equal to 10 to 15% of the total materials used in construction operations. This amount is much higher than what is estimated by the estimators.

Data and research method
In Iran and other developing countries, construction and construction waste is a major part of municipal waste, which in addition to high costs for its disposal, also has adverse consequences on the environment. The volume of this garbage is so much that now this issue has become a social and environmental problem not only in Iran but also in developed countries due to the limitation of natural resources and preservation of national capital for future generations as well as environmental protection And it is necessary because with proper management and efficient planning and reducing the volume of construction waste, not only the waste of natural resources and national capital is prevented, but also additional and ancillary costs are reduced and it is economically beneficial.
In this study, first, the effective criteria in selecting the burial site in the study area are determined. These criteria are reviewed and used by various standards, including standards related to the Environmental Protection Organization, the Ministry of Interior and international standards, as well as by reviewing resources and studies on the process of locating landfills in the country and abroad and by examining the conditions of the region. The study and the influencing factors are compiled in the study area. The layers related to each criterion in the relevant table will be prepared, processed and converted from the relevant organizations. The method of this dissertation is applied-modeling in terms of purpose, because on the one hand, the concepts and rules related to the field of knowledge are carefully analyzed, and on the other hand, the relationships between these concepts and rules are evaluated and determined by experts. In this study, there is a need to use the decision theory method to evaluate and investigate the status of construction waste disposal along roads to increase trust and confidence in decision making.
The data analysis tools of this research are SPSS, Expert Choice and Matlab for conducting the research. In the research process, after data collection, the next step involves data analysis. Cronbach's alpha coefficient was used to evaluate the reliability of the localization tools of the research components. In order to describe the data, the mean and standard deviation of the research data have been used.

The four-step process of multi-criteria decision-making process and fuzzy logic calculations to investigate the dumping of construction debris along roadsides is as follows:
Step 1 - Modeling causal relationships based on similarity to the ideal solution
Step 2 - Parallel comparisons and determining the weight of causal relationships based on the evaluation of decision options between the criteria for assessing the status of construction debris on the sidewalks,
Step 3 - Prioritize Based on Causal Relationships Based on Evaluation of Decision Options
 Step 4 - Fuzzy Prioritization and Final Analysis Investigation of Construction Waste Disposal Status

Result and Discussion
The most important results of the study of the dumping of construction debris along the roadsides are that,
1- The most important criterion in the cluster "Environmental factors of construction waste disposal" with code (A), "Soil pollution in the city" with code (AB) with fuzzy network weight of 0.096; And
2- The most important criteria in the cluster "Applications of GIS in urban management of construction debris disposal" with code (B), "Urban green space management" with code (BA) with fuzzy network weight equal to 0.191; And "Urban management related to health" with code (BB) with fuzzy network weight equal to 0.120; Were calculated. on the other hand,
3- The most important criterion in the cluster "Economic factors of construction waste disposal" with code (C), "Construction waste management training cost" with code (CD) with fuzzy network weight equal to 0.123; Prioritized,

conclusion
The results of the present study can be said that, after reviewing the theoretical foundations of the research and reviewing the research background, it was found that due to research gaps in the fields of economic factors of construction waste disposal, GIS applications in urban management, construction waste disposal, environmental factors, Utilization of a combined fuzzy multi-criteria decision-making methodology to investigate the status of construction debris dumping along roadsides; It is possible to realize the innovation of the present research in filling the mentioned research gaps.

Key words: Construction Debris, Civil Waste Management, Multi-Criteria Decision Making, Karaj.

 

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Landslide risk assessment is essential for all landslide damage mitigation plans. The purpose of this research is to assess the risk of landslides in the Shahrood watershed of Qazvin province. First, the landslide susceptibility map was prepared using fuzzy operators. the landslide distribution map and also 11 effective factor layers including slope, slope direction, altitude, land use, lithology, distance to road, distance to stream, distance to fault, earthquake acceleration, precipitation, and maximum daily precipitation were first prepared. After determining the frequency ratio and fuzzy membership values for the map classes of different factors, the landslide susceptibility map was prepared using different gamma values. Then, after preparing the fuzzy map of vulnerability for different land use units, the amount of landslide risk was determined from the product of two maps of landslide susceptibility and vulnerability. In general, 104 landslides with a total area of 1401 hectares were recorded in this region, 70% of which were used for modeling (73 landslides with an area of 982 hectares) and the remaining 30% (31 landslides with an area of 418 hectares) were used to assess the accuracy. The evaluation results showed that the highest value of Qs index (equal to 1.34) belongs to the gamma equal to 0.93 and therefore this model has higher accuracy than other gamma values. The importance of features at risk ranges from 0.05 (no coverage) to 1 (residential and industrial areas). To deal with landslide damages, three general policies including suitable for development, prevention, and treatment were proposed, which should be applied based on the two factors of risk and vulnerability for different areas of landslide risk. Finally, in order to reduce landslide damages, suitable land uses for high-risk regions were introduced. 

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Drought is one of the most important challenges faced by pastoralists around the world. This phenomenon can have significant negative effects on livestock health, production, and livelihoods. However, pastoralists can adapt to drought and reduce its negative effects by adopting various strategies. Semi-nomadic people in Darbandi, Kalat-Naderi County, have been facing drought since 2007 due to their livestock farming. Since livestock farming has profound impacts on the lifestyle and livelihoods of these communities, the present study examines their experience in facing drought and identifies their management strategies in these conditions. The research method is qualitative and the research tool is in-depth interviews with 20 semi-nomadic people in Darbandi, Kalat-Naderi. Sampling was purposeful and carried out until theoretical saturation was reached to ensure that a wide range of perspectives and experiences were collected. The data from the interviews were analyzed using a qualitative grounded theory approach to extract key patterns and concepts. According to the findings, the semi-nomadic Darbandi people of Kalat County have adopted various strategies in the face of drought, which are classified into four categories: rangeland and grazing management strategies, livestock nutrition management, water consumption management, and livelihood diversification. These results can be used as a basis for formulating better policies in the field of crisis management and rural development. Also, these results can be used for more effective planning to reduce the vulnerability of nomads to drought.

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Deforestation is one of the primary challenges and environmental threats facing forest ecosystems, including the Hyrcanian forests, and occurs under the influence of various natural and anthropogenic drivers. This study aimed to model the probability of deforestation occurrence within the Loveh forest management district located in northern Iran. The dataset comprised 104 documented deforestation points and 14 explanatory variables, derived through spatial analysis using GIS and environmental, topographic, and anthropogenic data. To assess the relationships among variables and predict the likelihood of deforestation, two statistical models were employed: logistic regression and the Generalized Additive Model (GAM). The results revealed that the GAM outperformed the logistic regression model, achieving a higher Kappa coefficient (0.84) and Area Under the Curve (AUC) value (0.956), and providing a more realistic spatial distribution of deforestation risk. The most influential variables included distance from roads, slope, wind effect, and elevation. Based on the GAM output, approximately 20% of the study area was categorized as high and very high risk. These findings underscore the pivotal role of access infrastructure, human pressure, and climatic factors in accelerating deforestation processes. The results of this study can serve as a scientific basis for prioritizing conservation interventions, reassessing road development policies, and enhancing spatial planning for sustainable forest management in northern Iran.