A New Integrated Approach for Municipal Landfill Siting Based on Urban Physical Growth Prediction: A Case Study Mashhad Metropolis in Iran

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

The suitability mapping of an urban spatial structure for earthquake disaster response using a gradient rain optimization algorithm (GROA)

The urban spatial structure has a prominent role in the earthquake response process which should primarily be assessed in the areas that are most vulnerable to earthquake hazards. Search and rescue teams need to map and identify the appropriateness of urban infrastructures for disaster reaction after a quake to enable ease of movement and quick assistance to the casualties. The key objective of this study is to compute the appropriateness of a municipal spatial structure for crisis reaction after a destructive earthquake, with an emphasis on finding the most critical areas (those that are prone to emergency response disruption). The main contribution involves improving a geographic information system (GIS)-based earthquake-triggered hybrid framework for suitability analysis using a fuzzy analytical hierarchical process (FAHP) and gradient rain optimization algorithm (GROA). The modifying of a rain optimization algorithm (ROA) to a GROA based on gradient descent is carried out to avoid local optima, which results in optimizing the identification process of the key locations for emergency response. The planned approach has been executed in Tehran, the capital of Iran. The implementation consequences reveal the supreme crucial areas for emergency response in the study area with a demonstration of the efficiency of the GROA compared to the basic ROA. Both indicate that these sites are located in the west and southwest, while the junction degree and width of the roads are the most significant factors affecting a city's suitability for emergency response. In addition, the GROA is less sensitive to local optima and more economical than the ROA. Moreover, several rescue experts and urban planners expressed their high satisfaction (95 %) with the five-level suitability map for prioritizing the deployments of troops along with the critical area maps for preventing heavy casualties produced by the GROA.

A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas: A Case Study of Tehran

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.

An Assessment of Social Resilience against Natural Hazards through Multi-Criteria Decision Making in Geographical Setting: A Case Study of Sarpol-e Zahab, Iran

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.

Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak

The coronavirus (COVID-19) pandemic has caused disastrous results in most countries of the world. It has rapidly spread across the globe with over 156 million cumulative confirmed cases and 3.264 million deaths to date, according to World Health Organization (WHO) Coronavirus Disease (COVID-19) Dashboard. With these huge amounts of causalities in the world, Geographic Information Systems (GIS) as a computer-based analyzer could help governments, experts, medical staff, and citizens to prevent and respond to the incidence. On the other hand, the COVID-19 pandemic involves many unknown parameters where most of them have a spatial dimension. Thus, spatial analysis and GIS could provide appropriate decision-making tools, predictive models, statistical methods, and new technologies for COVID-19 outbreak control, also help the people for avoiding direct contact and preserving social distance. This article aims to review the most promising categories of GIS-based solutions in this domain. We divided the solutions into ten classes including spatio-temporal analysis, SDSS approaches, geo-business, context-aware recommendation systems, participatory GIS and volunteered geographic information (VGI), internet of things (IoT), location-based service (LBS), web mapping, satellite imagery-based analysis, and waste management. The main contribution of this paper is proposing different geospatial guidelines that could provide reliable and useful protocols for COVID-19 outbreak control to minimize causalities, restrict incidence, establish effective urban communication, provide new approaches for business in lockdown situations, telehealth treatment, patient monitoring, adaptive decision making, and visualize trend analysis.

A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection

Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers.

Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination

Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems. The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives. First, a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done. Second, the Rough Set theory is used to reduce the integration uncertainties, as there are numerous quantitative and qualitative data. Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter. The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios. The final results indicated that among the entire studied buildings, 71.5% were highly vulnerable as concerning the highest earthquake scenario (intensity=7MM and acceleration calculated based on the epicenter), while in the lowest earthquake scenario (intensity=5MM), the percentage of vulnerable buildings decreased to approximately 57%. Also, the findings proved that the distance from the fault line rather than the earthquake center (epicenter) has a significant effect on the seismic vulnerability of urban buildings. The model was evaluated by comparing the results with the weighted linear combination (WLC) method. The accuracy of the proposed model was substantiated according to evaluation reports. Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.

Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran

INTRODUCTION

Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019.

METHODS

This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05.

RESULTS

The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed.

CONCLUSIONS

Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.