Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 °C, affected negatively, the confirmed cases.

The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities’ structures, infrastructures, and services. In this context, smart water management technology provides the data and tools to help users more effectively manage water usage. Data collected with smart water devices are being integrated with building management systems to show how much water is used by occupants as well as to identify the consumption areas to use water more efficiently. By this approach, smart buildings represent an innovative solution that enhances a city’s sustainability and contributes to overcoming environmental challenges due to increasing population and climate change. One of the main challenges is resource-saving and recovery. Water is an all-important need of all living beings, and the concerns of its scarcity impose a transition to innovative and sustainable management starting from the building scale. Thus, this manuscript aims to provide an updated and valuable overview for researchers, consumers, and stakeholders regarding implementing smart and sustainable technologies for water resource management, primarily for building-scale uses.

Nature-Based Solutions (NBS) have been proven to effectively mitigate and solve resource depletion and climate-related challenges in urban areas. The COST (Cooperation in Science and Technology) Action CA17133 entitled “Implementing nature-based solutions (NBS) for building a resourceful circular city” has established seven urban circularity challenges (UCC) that can be addressed effectively with NBS. This paper presents the outcomes of five elucidation workshops with more than 20 European experts from different backgrounds. These international workshops were used to examine the effectiveness of NBS to address UCC and foster NBS implementation towards circular urban water management. A major outcome was the identification of the two most relevant challenges for water resources in urban areas: ‘Restoring and maintaining the water cycle’ (UCC1) and ‘Water and waste treatment, recovery, and reuse’ (UCC2). s Moreover, significant synergies with ‘Nutrient recovery and reuse’, ‘Material recovery and reuse’, ‘Food and biomass production’, ‘Energy efficiency and recovery’, and ‘Building system recovery’ were identified. Additionally, the paper presents real-life case studies to demonstrate how different NBS and supporting units can contribute to the UCC. Finally, a case-based semi-quantitative assessment of the presented NBS was performed. Most notably, this paper identifies the most typically employed NBS that enable processes for UCC1 and UCC2. While current consensus is well established by experts in individual NBS, we presently highlight the potential to address UCC by combining different NBS and synergize enabling processes. This study presents a new paradigm and aims to enhance awareness on the ability of NBS to solve multiple urban circularity issues.

Buildings portion in global energy consumption is 40%, and in the building envelope, the roof is a crucial point for improving indoor temperature, especially in the last and second last floors. Studies show that green roofs can be applied to moderate roof temperature and affect the indoor temperature in summer and winter. However, the performance of green roofs depends on several parameters such as climate, irrigation, layer materials, and thickness. In this context, the present research deals with a comprehensive experimental analysis of different thermal impacts of green roofs in summer and winter in a Mediterranean climate. Measurements carried out in one year in three different types of green roofs with different thicknesses, layers, and with and without the insulation layer. The analysis determined the possible period that indoor cooling or heating might be required with and without green roofs and demonstrated the positive impact of green roofs in moderating the roof temperature and temperature fluctuations, which in summer was remarkable. In conclusion, since in the Mediterranean climate, the thermal differences between green roofs and conventional roofs in summer are much higher than winter, it seems that the green roof without an insulation layer would show better performance.

Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.