The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron ...(ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, sensitivity, and adaptability capacity attributed to a total of 16 spatial criteria, which were inputted into the neural network. To train the neural network and compute an earthquake vulnerability map, we used a combined Multi-Criteria Decision Analysis (MCDA) process with 167 vulnerable locations as training data, of which 70% (117 points) were used for training, and 30% (50 points) were used for testing and validation. The Mean Average Error (MAE) of the implemented neural network was 0.085, which proves the efficacy of the designed model. The results showed that 29% of Tehran’s total area is extremely vulnerable to earthquakes. Our factor importance analysis showed that factors such as proximity to fault lines, high population density, and environmental factors gained higher importance scores for earthquake vulnerability assessment of the given case study. This methodical approach and the choice of data and methods can provide insight into scaling up the study to other regions. In addition, the resultant outcomes can help decision makers and relevant stakeholders to mitigate risks through resilience building.
Many parts of the world may have suitable conditions and potential to establish two or more renewable energy farms. Given the pervasive use of renewable energy globally, assessing the potential of ...regions to establish a multi-renewable energy farm is of great importance. This study aimed to assess the potential for the establishment of renewable energy farms (solar, wind, biomass, and geothermal) in the eastern regions of Iran. For the first time, the potential for establishing multi-renewable energy farms in an area has been assessed. For this purpose, a series of environmental and economic criteria were addressed and investigated. Respectively, Analytical Network Process (ANP) and Fuzzy logic were employed for obtaining the required weights as well as accounting for the element of uncertainty among the different criteria. The final suitability maps for identification of the most optimal locations for the institution of renewable energy farms were obtained using Weighted Linear Combination (WLC) method. The study area was classified as highly suitable for the establishment of renewable energy farms, as maintained by the final results, wherein 5, 13, 23, and 19% of the entire study area were selected as eligible placements for the institution of biomass, geothermal, solar, and wind power plants, respectively. In consonance with the final map obtained using a combination of individual suitability maps, a total of 5465 km2 worth of area was categorized as highly suitable for the establishment of renewable energy farms. Results also were indicative of the prominence of the different weights assigned to each criterion on identifying the optimal choices of the region concerning the establishment of renewable energy farms. The results can be further used for and are highly advantageous to various managerial, planning, and decision-making procedures in connection with the development of prospective renewable energy sources.
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•Multi-renewable energy farms assessment and mapping is carried out in Eastern regions of Iran.•GIS-MCDA model is used for multi-renewable energy farms potential mapping.•Four renewable energy including solar, wind, biomass and geothermal is considered.•A sensitivity analysis of criteria’s weight proves the high stability of the model.
Preparedness against floods in a hazard management perspective plays a major role in the pre-event phase. Hence, assessing urban vulnerability and resilience towards floods for different risk ...scenarios is a prerequisite for urban planners and decision makers. Therefore, the main objective of this study is to propose the design and implementation of a spatial decision support tool for mapping flood vulnerability in the metropolis of Tehran under different risk scenarios. Several factors reflecting topographical and hydrological characteristics, demographics, vegetation, land use, and urban features were considered, and their weights were determined using expert opinions and the fuzzy analytic hierarchy process (FAHP) method. Thereafter, a vulnerability map for different risk scenarios was prepared using the ordered weighted averaging (OWA) method. Based on our findings from the vulnerability analysis of the case study, it was concluded that in the optimistic scenario (ORness = 1), more than 36% of Tehran’s metropolis area was marked with very high vulnerability, and in the pessimistic scenario (ORness = 0), it was less than 1%was marked with very high vulnerability. The sensitivity analysis of our results confirmed that the validity of the model’s outcomes in different scenarios, i.e., high reliability of the model’s outcomes. The methodical approach, choice of data, and the presented results and discussions can be exploited by a wide range of stakeholders, e.g., urban planners, decision makers, and hydrologists, to better plan and build resilience against floods.
Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these ...sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.
The increase of Land Surface Temperature (LST) and the formation of heat island in megacities have become an emerging environmental concern. The main objective of this study is to predict the ...intensity of Tehran's heat island in the year 2033 based on historical changes of land cover and LST. For this purpose, Landsat satellite images were integrated with meteorological stations' measurements from 1985 to 2017. The Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were used to predict the land cover changes and to the modelling of the Surface Urban Heat Island Intensity (SUHII), Surface Urban Heat Island Ratio Index (SUHRI) was used. Subsequently, using statistical analysis of the effect of historical land cover changes on LST variations, SUHII for 2033 was predicted. Our findings show that within this period, the built-up lands increased significantly from 39% in 1985 to 65% in 2017. The intensity of heat island increased with an increase in the value of SUHII from 0.02 to 0.19. Our predictive analysis reveals that the intensity of the Tehran's heat island will increase to 0.32 by 2033. Our conclusions draw attentions to the increasing LST now and in the future in Tehran so that urban planners and local authorities take adequate actions for controlling its environmental impacts.
Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban ...growth on spatial and temporal changes of SES over a set of neighboring Iranian cities, Amol, Babol, Qaemshahr, and Sari, which are located in moderate and humid climate conditions. Firstly, the built-up footprint was prepared using Landsat images based on the Automatic Built-up Extraction Index (ABEI). Then, the surface biophysical characteristics were calculated. Secondly, the SES was modeled using the Remotely Sensed Ecological Index (RSEI), and the spatio-temporal changes of the SES were evaluated. The results revealed that the average RSEI for these cities increased from 0.48, 0.51, 0.53, and 0.55 in 1986 to 0.69, 0.77, 0.75, and 0.78 in 2022, respectively. The proportion of the poor ecological condition class in these cities rose from 10%, 3%, 5%, and 1% to 74%, 64%, 54%, and 41% during the 1986–2022 period. Our findings indicate that the SES of these cities significantly decreased while they experienced large physical growth. The findings and the methodical approach of the study provide a data-driven approach for monitoring SES in fast growing regions, which is required for studying the impact of climate change on society.
The aim of this study was to propose an approach for assessing the social resilience of citizens, using a locative multi-criteria decision-making (MCDM) model for an exemplary case study of Sarpol-e ...Zahab city, Iran. To do so, a set of 10 variables and 28 criteria affecting social resilience were used and their weights were measured using the Analytical Hierarchy Process, which was then inserted into the Weighted Linear Combination (WLC) model for mapping social resilience across our case study. Finally, the accuracy of the generated social resilience map, the correlation coefficient between the results of the WLC model and the accuracy level of the social resilience map were assessed, based on in-situ data collection after conducting a survey. The outcomes revealed that more than 60% of the study area falls into the low social resilience category, categorized as the most vulnerable areas. The correlation coefficient between the WLC model and the social resilience level was 79%, which proves the acceptability of our approach for mapping social resilience of citizens across cities vulnerable to diverse risks. The proposed methodological approach, which focuses on chosen data and presented discussions, borne from this study can be beneficial to a wide range of stakeholders and decision makers in prioritizing resources and efforts to benefit more vulnerable areas and inhabitants.
The purpose of this study is to design and implement a spatial model to identify optimal areas for energy sources (solar power plant (SPS), wind turbine generator (WTG), Distributed Gas ...Turbine-Generators (DGTG), and direct connection to power distribution system (PDS) to supply electricity to agricultural wells using a combination of data-driven methods and geographic information system (GIS). This study used 13 spatial criteria to identify suitable areas for SPS, 11 for WTG, 11 for DGTG, and 3 for PDS. A combination of data-driven decision-making (DDDM) and GIS was used for data analysis. The results of the present study showed that in the pilot study area, solar energy has a higher potential than other energies. Also, the results of the sensitivity analysis showed that changing the weight of the criteria has no significant effect on the model outputs and clearly proves the stability of the proposed model. The hybrid power plant scenarios showed that had the highest number of pixels suitable for development were associated with double and triple hybrid power solutions generator, respectively. These were hybrid SPS and DGTG and hybrid SPS, PDS, and DGTG systems.
Identifying potential locations for installation of solar power plants is a critical step in utilizing sustainable energy resources. In this study, a GIS-based Multi-Criteria Decision Analysis ...(GIS-MCDA) technique is used to generate maps that represent potential areas for solar power plants in four provinces with different climate conditions in Iran. The concept of risk is included in the GIS-MCDA process using the Ordered Weighted Averaging (OWA) model. The OWA model can provide various risk-taking (optimistic) and risk-aversion (pessimistic) scenarios to determine the suitable power plant areas. The results of this study indicate that provinces located in an arid climate such as Yazd contain a more suitable area for the solar power plants compared to wet climate provinces (e.g., Mazandaran). The sensitivity analysis of results show that the criterion “fault” has the minimum effect while the criteria “slope” and “road network” have the maximum effects on the area of the highly desirable class.
•The GIS-MCDA is used to evaluate the locations for establishment of solar power plants.•The evaluation process involves the locations in different climate conditions.•An OWA-based technique is utilized to incorporate risk in solar energy assessment.•The sensitivity of results to each criterion is evaluated.
Solar energy is one of the important energy sources and countries have realized the important role of renewable energies due to the depletion of conventional energy sources. In this study, a ...GIS-based analysis is utilized for investigating the feasibility of solar energy in Iran. To evaluate the concept of risk into the GIS-based analysis for determining optimal areas for installation of solar power plants an Ordered Weighted Averaging (OWA) approach is used for the first time. Integration of OWA-based approach and GIS analysis provide models that determine the priority of regions from risk-free decision to risky decision strategies. The results show that Kerman, Yazd, Fars, Khuzestan, Sistan and Baluchistan, South Khorasan and Isfahan provinces have a good capacity to invest in solar energy projects. The GIS-based analysis indicates that the values of installed solar power plants percentages in high chosen areas for the most pessimistic and optimistic strategies are 7% and 64%, respectively.
•A new model is used to evaluate the solar energy in Iran.•To evaluate solar energy potential a risk analysis is added to GIS-based method.•An Order Weight Averaging is utilized for first time in solar energy assessment.•A location analysis is done for currently working solar plants.•A maximum of 498 W/m2 solar radiation is achievable yearly.
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