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Abstract Floods accompanied by thunderstorms in developed cities are hazardous, causing damage to infrastructure. To secure infrastructure, it is important to employ an integrated approach, combining remote sensing, GIS and precipitation data. The model was developed based on the estimation of event-based runoff and investigated the relationship between runoff and impervious surfaces. The novel approach of combining Hydrologic Engineering Center’s River Analysis System (HEC-GeoRAS) along with satellite imagery was utilized, where spatial data was combined with real-time values to run the model. As a first step, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was fed with information about precipitation, slope, soil type, as well as land use and land cover. The results reveal that the subbasins of Deira, Nief and Jumeirah have the largest impervious area and, thus, a higher probability of flood occurrence. The model was calibrated and validated using previous runoff events and by comparing observed and simulated streak flow and peak discharge against those reported in previous studies. It was found that the model is efficient and can be used in similar regions.

Urbanization without planning causes concerns about biodiversity loss, congestion, housing, and ecosystem sustainability in developing countries. Therefore, resilience and regeneration following urbanization are critical to city planning and sustainable development. Integrating multi-criteria decision-making methods (MCDM) with geographic information systems (GIS) can be a promising method for analyzing city resilience and regeneration. This study aims to use two MCDMs, the Analytic Network Process (ANP) and the Best–Worst Method (BWM), to evaluate the resilience of metropolitan neighborhoods in Tehran. Fourteen criteria were selected to represent the city’s resilience, and the weights of two models were evaluated for their spatial patterns using GIS. The results showed that the building age was the most important criterion in both methods, while the per capita green space was the least important criterion. The weights of the most important criterion, the building age, for the ANP and BWM, were 19.56 and 18.98, respectively, while the weights of the least important criterion, the per capita green space, were 2.197 and 1.655, respectively. Therefore, the MCDM with GIS provides an approach for assessing city resilience and regeneration priority.

Consumption of water varies throughout the day due to the daily routines of the consumer. This pattern of daily water consumption is called the water demand profile. The initiatives to create these profiles are to improve hydraulic performance and to build energy conservation strategies for designed networks in Dubai. Therefore, the aim of the work presented here was to develop and analyze a domestic consumption profile for selected developments with socio-demographic factors including weekday/weekend variation, population, income, fasting during the month of Ramadan, and the outbreak of COVID-19. Data from more than 7000 smart meters were collected while water meters of more than 350 residential flats were examined manually. Water demand profiles generated from the data showed weekdays have more predictable peaks (morning 6–8 am and evening 5–7 pm) than weekends. During Ramadan, peak hours shifted to 7–10 am followed by 3–4 pm during workdays while peaks for low-income areas were higher due to a stricter working routine. The COVID-19 crisis has led to significant rise in observed consumption, with over a 30% increase during the month of Ramadan. The observed results, if compared with further end-use studies on more factors affecting demand profiles, can help in generating both cost and energy efficient networks. HIGHLIGHTS The paper develops a consumption profile for the unique case of COVID-19 and Ramadan.; The paper links different socio-demographic factors to assess the position of water peak hours.; The paper observes and develops different consumption profile for different days of the week (weekday and weekend).; The paper produces a water profile for the first time in Dubai.; The paper shows how culture variations can affect the consumption pattern.;

Anaerobic digestion is a versatile method for wastewater treatment as it not only reduces the waste but also leads to production of renewable energy. Modeling of the anaerobic process requires knowledge of biological and physico-chemical conditions, bacterial growth kinetics, substrate utilization, and product synthesis. However, the complexity of the process calls for highly sophisticated models requiring very high level of expertise and knowledge in the subject. This paper presents an approach for modeling of anaerobic digestion process through which the correlation between various process parameters can be studied, knowledge can be extracted, and system behaviour can be predicted. The datasets have been generated using a synthetic Matlab-Simulink-Excel model and process modelling is done using Kohonen Self organizing maps (KSOM). The resulting KSOM provided a visual interpretation of the inter-relationships between parameters (OLR, Sac, pH, Shco3, Q, Sglu_in, Qgas_out, Sglu_out, and Sch4_gas_out) which would help semi-skilled operators for operation and control of such plants. The model accurately predicts the variations in methane and total gas output with respect to changes in input parameters as the correlation is more than 90% for most of the parameters. This methodology offers a platform for scientists and researchers in comprehending the system behaviour under various operating conditions, even with missing data.