Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran

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

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

This study extensively explores the impact of climate change on meteorological droughts within metropolises in Iran. Focused on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, this research employed CMIP6 climate models under varying climate change scenarios (SSPs) to forecast severe meteorological droughts spanning the period from 2025 to 2100. The investigation utilized a diverse set of drought indices (SPI, DI, PN, CZI, MCZI, RAI, and ZSI) to assess the drought severity in each city. This study is crucial as it addresses the pressing concerns of rapidly decreasing water levels in Iran’s dams, serious declines in underground aquifers, and the compounding issues of land subsidence and soil erosion due to excessive groundwater withdrawal in the face of severe droughts. This study culminated in the generation of box plots and heatmaps based on the results. These visual representations elucidated the distribution of the drought values under different indices and scenarios and provided a depiction of the probability of severe drought occurrences until the end of the century for each city. The resulting findings serve as invaluable tools, furnishing policymakers with informed insights to proactively manage and fortify metropolitan resilience against the evolving challenges posed by a changing climate