A comprehensive fire risk assessment is very important in dense urban areas as it provides an estimation of people at risk and property. Fire policy and mitigation strategies in developing countries are constrained by inadequate information, which is mainly due to a lack of capacity and resources for data collection, analysis, and modeling. In this research, we calculated the fire risk considering two aspects, urban infrastructure and the characteristics of a high-rise building for a dense urban area in Zanjan city. Since the resources for this purpose were rather limited, a variety of information was gathered and information fusion techniques were conducted by employing spatial analyses to produce fire risk maps. For this purpose, the spatial information produced using unmanned aerial vehicles (UAVs) and then attribute data (about 150 characteristics of each high-rise building) were gathered for each building. Finally, considering high-risk urban infrastructures, like the position of oil and gas pipes and electricity lines and the fire safety analysis of high-rise buildings, the vulnerability map for the area was prepared. The fire risk of each building was assessed and its risk level was identified. Results can help decision-makers, urban planners, emergency managers, and community organizations to plan for providing facilities and minimizing fire hazards and solve some related problems to reduce the fire risk. Moreover, the results of sensitivity analysis (SA) indicate that the social training factor is the most effective causative factor in the fire risk.

With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges and issues in many countries. The concentration of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM_2.5), recognized as the main air pollutant in Iran, penetrates the respiratory system through inhalation, causing respiratory and cardiovascular diseases, reproductive disorders, central nervous system disturbances, and cancer. Accurate and high spatiotemporal modeling of air pollutant concentrations is crucial for air quality management and exposure assessment in epidemiological studies. Air quality monitoring stations provide valuable data on air pollution levels at specific locations, but they have limitations in fully capturing the air quality across entire areas and cities in a country. Meanwhile, the rapid growth of computational technologies and the availability of air quality data have enabled researchers to develop complex models using deep learning for modeling various air pollutant concentrations. In this research, PM_2.5 pollutant concentration modeling for monthly continuous distribution estimation has been implemented and evaluated based on deep learning models. The results of the presented models were compared to select the model with the best performance. Additionally, the influence of the parameters used on pollutant concentration levels was analyzed. In this study, we applied deep learning techniques, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), to model PM_2.5 concentrations for continuous monthly distribution estimation. We utilized satellite data, ground-based observations, and meteorological parameters as input features. The models were evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The ConvLSTM model outperformed others with an RMSE of 4.95 µg/m³ and an R² of 91.24%. Sensitivity analysis indicated that among 18 input parameters, population density, AOD, NO₂, and rainfall had the most significant impact on PM_2.5 concentrations.

Urban land-use allocation is a complicated problem due to the variety of land-uses, a large number of parcels, and different stakeholders with diverse and conflicting interests. Various approaches and techniques have been proposed for the optimization of urban land-use allocation. The outputs of these approaches are almost optimum plans that suggest a unique, appropriate land-use for every land unit. However, because of some restrictions, such stakeholder opposition to a specific land-use or the high cost of land-use change, it is not possible for planners to propose a desirable land-use for each parcel. As a result, planners have to identify other priorities of the land-uses. Thus, ranking land-uses for parcels along with optimal land-use allocation could be advantageous in urban land-use planning. In this paper, a parcel-level model is presented for ranking and allocating urban land-uses. The proposed model benefits from the capabilities of geographic information systems (GIS), fuzzy calculations, and Multi-Criteria Decision-Making (MCDM) methods (fuzzy TOPSIS), intends to improve the capabilities of existing urban land-use planning support systems. In this model, as a first step, using fuzzy calculations and spatial analysis capabilities of GIS, quantitative and qualitative evaluation criteria are estimated based on physical characteristics of the parcels and their neighborhoods. In the second step, through the fuzzy TOPSIS method, urban land-uses are ranked for each of the urban land units. In the third step, using the proposed land-use allocation process and genetic algorithm, the efficiency of the model is evaluated in urban land-use optimal allocation. The proposed model is tested on spatial data of region 7, district 1 of Tehran. The implementation results demonstrate that, in the study area, the land-use of 77.2% of the parcels have first priority. As such, the land-use of 22.8% of the parcels do not have first priority, and are prone to change.

Air pollution is one of the major environmental issues in Iran, driven by industrial greenhouse gas emissions, vehicle exhaust, power plants, and natural sources such as dust storms. Among various pollutants, PM₂.₅ is especially concerning due to its high prevalence in urban areas. Governments have utilized air quality monitoring stations to measure PM₂.₅ concentration levels to implement policies aimed at controlling this pollutant. Fixed monitoring stations, which operate for regulatory purposes, are typically located in urban areas where a large portion of the population resides. However, the measurements obtained from these stations are limited due to their insufficient number, uneven distribution, and inadequate spatial coverage, which prevent continuous pollutant estimation over large areas and the generation of comprehensive risk maps. To address these limitations, this study introduces a novel hybrid modeling framework for modeling the spatiotemporal distribution of PM₂.₅ across Iran. The proposed approach integrates Cellular Automata with the XGBoost algorithm, leveraging both satellite-based products and ground observations. Within this framework, XGBoost generates continuous transition potentials for each neighborhood window, enabling dynamic representation of spatial variations in PM₂.₅ levels. The resulting model achieved an adjusted R² of 0.88, demonstrating strong performance in capturing regional and temporal variability. A key strength of the method is its ability to localize the influence of predictor variables across different regions, allowing the development of region-specific models within each neighborhood window and improving both interpretability and predictive accuracy.