Search Results (27)

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

Earthquakes often cause prolonged electricity outages that disrupt essential health services and basic water, sanitation, and hygiene functions in hospitals and field clinics. This study combines a focused literature review with a time-step energy balance simulation developed in MATLAB 25.2 and Simulink to examine how power interruptions translate into public health risks and to evaluate microgrid-based resilience solution designs. Conventional electricity supply with diesel backup is compared with hybrid solar power, battery storage, and diesel generator configurations under five outage scenarios that vary by duration, fuel availability, and solar conditions. The results indicate that diesel-only strategies are highly vulnerable to fuel supply disruptions, leading to substantial downtime of critical services and increased unmet essential electricity demand. Hybrid microgrid configurations demonstrated a significant improvement in critical-load continuity, thereby enhancing the capacity to sustain essential care during prolonged outages. In the fuel-constrained 72 h outage scenario (S2: 24 h diesel availability), the hospital case shows critical service availability increasing from ~48% (diesel-only) to ~87% (PV + battery + diesel), with similar improvements for the field clinic (~46% to ~85%). Hybrid microgrids improve critical-load continuity via solar generation, battery buffering, and priority-based load shedding, while reducing diesel runtime and extending fuel autonomy. The model also relates energy performance to a WASH-supportability proxy relevant to infection prevention. Full article

Reducing damage caused by power outages is important against the background of severe natural disasters. Estimating the value of lost load (VoLL) is key to making an optimal investment plan for power systems. This paper aims to estimate the recent residential VoLL in Japan by using a survey. The contingent valuation method quantifies the residential willingness to pay (WTP) and its distribution in a 2 h outage during summer. When combining actual demand data, the VoLL is estimated at 501.1 JPY/kWh for a predictable outage and 559.9 JPY/kWh for a sudden one. In addition, the random utility model reveals the effect of people’s attributes on WTP. Larger annual incomes and electricity bills significantly increase WTP. Evacuation experiences and stockpiles also affect WTP in a sudden outage. Finally, 80% of respondents answered that refrigerators, air conditioners, and water supplies are important during outages. Full article

Hydrogen (H2) offers a green medium for storing the excess from renewables production instead of dumping it, thus being crucial to decarbonisation efforts. Hydrogen also offers a storage medium for the grid’s cheap electricity to be used during grid peak demand or grid power cutoffs. Funded by the Scottish Government’s Emerging Energy Technologies, this paper presents the design and performance analysis of a hydrogen uninterruptible power supply (H2GEN) for Cygnas Solutions Ltd., which is intended to enable continuity of supply in the residential sector while eradicating the need for environmentally and health risky lead–acid batteries and diesel generator backup. This paper presents the design, optimal sizing and analysis of two H2Gen architectures, one powered by the grid alone and the other powered by both the grid and a renewable (PV) source. By developing a model of each architecture in the HOMER space and using residential location weather data, the home yearly load–demand profile, and the grid yearly power outages profile in the developed models, the optimal sizing of each H2Gen design was realised by minimising the costs while ensuring the H2Gen meets the home power demand during grid outages To enable HOMER to optimise its selection, the sizes, technical specifications and costs of all the market-available H2GEN components were added in the HOMER search space. Moreover, the developed models were also used in assessing the sensitivity of the simulation outputs to several changes in the modelled system design and settings. Using a residential home with frequent power outages in New Delhi, India as a case study, it was found that the optimal sizing of H2Gen Architecture 1 is comprised of a 2 kW electrolyser, a 0.2 kg type-I tank, and a 2 kW water-cooled fuel cell directly connected to the AC bus, offering an operational lifetime of 14.3 years. It was also found that the optimal sizing of Architecture 2 is comprised of a 1 kV PV utilised with the same 2 kW electrolyser, 0.2 kg type-I tank and 2 kW water-cooled fuel cell connected to the AC bus. While the second design was found to have a higher capital cost due to the added PV, it offered a more cost-effective and environmentally friendly architecture, which contributes to the ongoing energy transition. This paper further investigated the capacity expansion of each H2GEN architecture to meet higher load demands or increased grid power outages. From the analysis of the simulation results, it has been concluded that the most feasible and cost-effective H2GEN system expansion for meeting increased power demands or increased grid outages can be realised by using the developed models for optimally sizing the expanded H2Gen on a case-by-case basis because the increase in these profiles is highly time-dependent (for example, an increased load demand or increased grid outage in the morning can be met by the PV, while in the evening, it must be met by the H2GEN). Finally, this paper investigated the impact of other environmental variables, such as the temperature and relative humidity, on the H2GEN’s performance and provided further insights into increasing the overall system efficiency and cost benefit through utilising the H2GEN’s exhaust heat in the home space for heating/cooling and selling the electrolyser exhaust’s O₂ as a commodity. Full article

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and $R^2$ is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article

Substandard Supply Minutes is the key performance indicator for asset management in the drinking water sector. A novel methodology translates production site failures into outage scenarios, allowing for calculation of Substandard Supply Minutes (SSM) based on all clients in the supply area. Drinking water utilities can conduct scenario studies, pinpoint high-risk assets, and compare production sites. This method can contribute to a better risk-based policy for design, investment and maintenance. Effective implementation necessitates a deeper understanding of failures of components at production sites. Full article

Water distribution systems (WDSs) need electric power supply to operate their pumps. Long-lasting power outages (blackouts) can disrupt the availability of water for citizens. If the water supply is limited by constrained pumping capacities caused by the blackout, water demand reduction could help preserve this limited supply, while increased water withdrawal, i.e., stockpiling, could deplete it. This study investigates the effects and subsequent uncertainty of demand response, especially stockpiling, on WDSs in a blackout. Therefore, we (i) model residential water demand reduction, regular water demand, and water stockpiling in a blackout, (ii) simulate the effect of the demand response on the WDS of Darmstadt, Germany, and (iii) investigate uncertainty resulting from the demand response and initial states of the WDS at time of the onset of the blackout. The findings indicate that the demand response and initial tank levels are the main sources of uncertainty and that demand-side management bears the potential to improve water service availability during a blackout. Full article

Two main reasons for using intermittent water supply (IWS) systems are water scarcity and power outages. As a result of IWS systems, consumers have inequitable water supply and high operating costs for water utilities. This study proposes a new methodology for assessing and improving the IWS systems’ resilience under limited water and electricity supply. First, a global resilience analysis (GRA) of the network was conducted to identify its main vulnerabilities. Second, adaptation intervention strategies were considered to improve the network’s resilience. Results indicate that system resilience is improved through an operation intervention strategy. Full article

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters. Full article

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities. Full article

Global warming increases the risk of power outages. Mine water pumping stations pump approximately 100 million m³ of water per year (2023). The cessation of mine water pumping would expose neighboring mines and lower lying areas to flooding. The pumping stations have some containment, but a prolonged shutdown could cause environmental problems. Remediation of the resulting damage would be costly and time-consuming. The combination of the problems of dewatering abandoned mines and storing energy in the form of hydrogen to ensure continuity of power supply to pumping stations has not been the subject of extensive scientific research. The purpose of this paper was to develop options for protecting mine water pumping stations against the “blackout” phenomenon and to assess their investment relevance. Six technically feasible options for the modernization of mine water pumping stations were designed and analyzed in the study. All pumping station modernization options include storage of the generated energy in the form of green hydrogen. For Q1 2024 conditions, the option with the partial retail sale of the produced hydrogen and the increased volume of produced water for treatment is recommended for implementation. Full article

Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system. Full article

Even though drinking water utilities are not meant to fight wildfires, they quickly become stakeholders, if not first responders, when their resources are needed for firefighting. The August 2023 wildfires on the island of Maui, Hawaii, USA, have highlighted weaknesses at this intersection. While attention has focused on the wildfire causes or water quality impacts afterward, few studies have analyzed the response. We review this extreme case to support disaster-response lessons for water utilities and to guide further research and policy. First, emergency water releases were not available in a timely manner. Second, fire and wind toppled power lines, causing power outages that inhibited pumping water. Third, many structures were a total loss despite water doused on them, consuming valuable water. Finally, water was lost through damaged premise plumbing in burned structures, further reducing system pressure. These conditions emphasize that water utilities need to access emergency water supplies quickly, establish reliable backup electricity, coordinate with firefighters on priority water uses, and shut valves in burned areas to preserve water. While further research will certainly follow, we present these early lessons as starting points. Full article

Extreme rainfall may induce flooding failures of electricity facilities, which poses power systems in a risk of power supply interruption. To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method was proposed. First, a scenario generation scheme considering waterlogged faults of power facilities was constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios were merged with several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram–Charlier level expansion methods were adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution was performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently, the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance were considered to quantitatively assess the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method was verified in case studies based on the Southeast Australia Power System. Simulation results indicated that the risk of load shedding in extreme precipitation scenarios can be quantitatively estimated, and the overload probability of lines can be controlled within the allowable range through the proposed optimal load shedding scheme. Full article

The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a structured method for data modeling is implemented based on the KDD methodology. Tweets from January to June 2022 related to the companies in the sector are processed, and a temporal analysis of the evolution of sentiment is performed based on the dictionaries Bing, AFINN, and NRC. Subsequently, the LDA algorithm (Latent Dirichlet Allocation algorithm) is used to visually identify the topics with the greatest negative impact reported by the users in each of the 6 months by adding the temporal dimension. The results revealed that, for Aqueduct (water supply service), the topic with the highest dissatisfaction is related to the “Water Tank Request” processes; for Enel (energy services) “Service Outages”; and for Vanti (gas services), “Case solution and request information”. Temporal patterns of tweets, sentiments, and topics are also highlighted for the three companies. Full article