Geoadditive Bayesian regression models for water mains failure rate prediction

Application of pure linear deterioration models for Water Distribution Networks (WDNs) is not effective in the representation of the physical degradation of water pipes because of the theoretical approach of water pipes deterioration or simply the uncertainty related to the specific form of effects that a covariate has on the response variable. Polynomial approaches are convenient to represent the complexity of the physical phenomena. However, even high degree polynomials wiggly estimate the relationships and are unsatisfactory in some regions where they fail to fit the observed data. Flexible regression techniques that enable automatic data-driven estimation of nonlinear relations between covariates and response constitute an alternative approach that is able to represent the physical deterioration process. In this study, a Geoadditive Bayesian regression model with smooth nonlinear splines functions for the continuous covariates and spatially distributed effects for the geospatial information of the pipes is applied to predict the failure rate of metallic water mains. The results highlight nonlinear dependency between continuous covariates and the response variable. A map representing the effect of the covariates and the geospatial location of the pipes on the response variable is produced. This map can be used as an early indicator to localize areas where the effect of covariates on the failure rate is high and prioritize them for inspections and maintenance

A systematic review of studies on fine and coarse root traits measurement: towards the enhancement of urban forests monitoring and management

The analysis of fine and coarse roots' functional traits has the potential to reveal the performance of the root system, which is pivotal in tree growth, development, and failure in both natural and urban forest ecosystems. Furthermore, root traits may be a powerful indicator of tree resilience mechanisms. However, due to the inherent difficulties in measuring 'the hidden half,' and despite the recent advancements, the relationships among root functional traits and biotic and abiotic drivers still suffer from a lack of information. Thus, our study aimed to evidence knowledge milestones and gaps and to categorize, discuss, and suggest future directions for effective experimental designs in fine and coarse root studies. To this end, we conducted a systematic literature review supported by backward manual referencing based on 55 root functional traits and 136 plant species potentially suitable for afforestation and reforestation of natural and urban forest ecosystems. The majority of the 168 papers on fine and coarse root studies selected in our review focused predominantly on European natural contexts for a few plant species, such as Fagus sylvatica, Picea abies, Pinus sylvestris, and Pinus cembra, and root functional traits such as standing biomass, phenology production, turnover rate, and non-structural carbohydrates (NSC). Additionally, the analyzed studies frequently lack information and uniformity in experimental designs, measurements, and statistical analysis, highlighting the difficult integration and comparison of outcomes derived from different experiments and sites. Moreover, no information has been detected in selected literature about urban forest ecosystems, while most of the studies focus on natural forests. These biases observed during our literature analysis led us to give key indications for future experiment designs with fine and coarse roots involved, which may contribute to the building up of common protocols to boost the monitoring, managing, and planning of afforestation and reforestation projects

Unearthing Current Knowledge Gaps in Our Understanding of Tree Stability: Review and Bibliometric Analysis

Forest preservation and management are paramount for sustainable mitigation of climate change, timber production, and the economy. However, the potential of trees and forests to provide these benefits to the ecosystem is hampered by natural phenomena such as windthrow and anthropogenic activities. The aim of the current research was to undertake a critical thematic review (from 1983 to 2023) informed by a bibliometric analysis of existing literature on tree stability. The results revealed an increase in tree stability research between 2019 and 2022, with the USA, France, and Italy leading in research output, while Scotland and England notably demonstrated high research influence despite fewer publications. A keyword analysis showed that tree stability can be divided into four themes: tree species, architecture, anchorage, and environmental factors. Prominent studies on tree stability have focused on root anchorage. However, more recently, there has been a growing emphasis on urban forestry and disease-induced tree damage, underscoring a shift towards climate change and diversity research. It was concluded that considerable knowledge gaps still exist; that greater geographic diversification of research is needed and should include tropical and sub-tropical regions; that research relating to a wider range of soil types (and textures) should be conducted; and that a greater emphasis on large-scale physical modelling is required. Data and knowledge produced from these areas will improve our collective understanding of tree stability and therefore help decision makers and practitioners manage forestry resources in a more sustainable way into the future

Understanding Natech Risk Due to Storms - Analysis, Lessons Learned and Recommendations

As standards of living generally improve across the globe, there is a corresponding change in people’s perception and acceptance of risk. The impact of natural hazards is an emerging threat to industrial facilities, pipelines, offshore platforms and other infrastructure that handles, stores or transports hazardous substances. When accidentally released, hazardous substances can lead to fires, explosions, and toxic or radioactive releases. These so-called Natech accidents are a recurring but often overlooked feature of many natural disasters and have often had significant human, environmental and economic impacts. Industries and authorities must be able to learn from incidents and capture the lessons that are needed to safely conduct business and produce goods for the whole of society. Among natural events, storms can seriously affect the integrity of an industrial installation and lead to accident scenarios such as fires, explosions and the dispersion of chemicals in the environment. In addition, scientists expect an overall worsening of extreme weather events in this century due to climate change, which will further increase the threat to industrial facilities. This report analyses past technological accidents with hazardous materials releases and damage to industrial facilities caused by the impact of storms. It discusses the vulnerability of industrial sites including that of the main equipment types present at the facility and analyses how they are damaged. The first part of the report describes the storm hazard. It discusses storm types and their occurrence, as well as the main effects that cause damage to human settlements and the environment. The report lists strong winds, heavy precipitation, lightning and storm surge as the main effects responsible for damage to industrial installations. In the second part of the report, we perform an analysis of past storm-triggered Natech events. Using different sources of public information on technological incidents, this study: 1. Analyses incident statistics; 2. Reviews a number of “landmark” accidents; 3. Discusses the lessons learned. From the analysis of past events, the report concludes that Natech events caused by storms are frequent and that their relative occurrence is increasing compared to the overall occurrence of technological incidents from other causes in the analysed databases. The largest losses were generally triggered by heavy rain and flooding, while the most frequent trigger was lightning. The study also highlighted the role of a loss of power supply in triggering an accident or hampering the mitigation of its consequences. The study presents lessons learned from the forensic analysis of past events and puts forward recommendations for future risk reduction for all storm effects. The most important lesson is that storm predictions based on past events are not sufficient to be well prepared for future events, in particular in the face of climate change.JRC.E.2-Technology Innovation in Securit

Comparing Likelihood of Tree Failure Assessments Using Different Assessment Techniques

Tree risk assessment is an important arboricultural practice used to determine a tree’s risk of failing and causing damage to property or injury to people. Risk assessment includes three assessments: the likelihood of failure, the likelihood of striking a target, and the severity of consequences. To assess the likelihood of tree failure, arborists consider factors that affect the loads a tree experiences (for example, whether it is sheltered or exposed to strong winds, whether the crown is porous or dense, and how tall and wide is the crown) and factors that affect its load-bearing capacity (for example, what is the strength of wood in the tree and how large are the cross-sectional areas of branches, trunk and roots). They also visually inspect a tree for defects that reduce the tree’s load-bearing capacity. Symptoms of decay include cavities, dead/sloughing bark, depressions, cracks, resin/sap flow, excessive basal tapering and canopy dieback. Signs of pathogens may also indicate the presence of decay, including fruiting bodies (mushrooms/conks), fungal mycelia, bacterial ooze, and nematodes. But the presence of symptoms or signs alone does not necessarily correlate with the full extent of decay. When visual inspections are inconclusive, additional tools (such as mallets to sound a decayed area) or advanced diagnostic technologies (such as resistance drilling and sonic tomography) may be needed. The industry consensus is that tools and advanced technologies provide more accurate information regarding the internal condition of a tree, however there are comparatively few 3 | P a g e rigorous data to confirm whether the tools provide meaningful information for arborists assessing tree risk. The goal of this study was to understand better whether the additional information provided by advanced assessment techniques altered an experienced risk assessor’s rating of the likelihood of failure compared to their visual assessment. To investigate this question, I compared likelihood of failure ratings for 30 trees assessed by 18 arborists who used visual and advanced decay detection techniques. Mean likelihood of failure ratings for assessments derived using sonic tomography were higher compared to the other assessment techniques. However, when assessors consulted with a peer about their likelihood of failure ratings after performing each of the assessment techniques, mean likelihood of stem failure ratings were lower. Additionally, no assessment technique consistently reduced variability in ratings among arborists. Tree risk management programs can use the findings of this study to enhance the systematic procedures they use to assess tree risk in the urban landscape

Assessment of Unmanned Aerial Systems and lidar for the Utility Vegetation Management of Electrical Distribution Rights-of-Ways

Utility Vegetation Management (UVM) is often the largest maintenance expense for many utilities. However, with advances in Unmanned Aerial Systems (UAS; or more commonly, “drones”) and lidar technologies, vegetation managers may be able to more rapidly and accurately identify vegetation threats to critical infrastructures. The goal of this study was to assess the utility of Geodetics’ UAS-lidar system for vegetation threat assessment for 1.6 km of a distribution electric circuit. We investigated factors which contribute to accurate tree crown detection and segmentation of trees from within an UAS-lidar derived point cloud, and the factors which contribute to accurate tree risk assessment. The study adapted the International Society of Arboriculture’s (ISA) tree risk assessment methodology to the application of remotely sensed tree inventory. We utilized the lidar detected and segmented tree crowns for tree risk analysis based upon each tree’s height, elevation, and location in relation to the electrical infrastructure. The individual tree detection and segmentation results show that our canopy type parameter and the routine used for field- and lidar-derived tree matching to have the largest effect on the classification agreement of field and lidar derived datasets. The Threat Detection classification also demonstrated a significant effect due to our canopy modeling parameter, where single canopy models possessed higher average Kappa agreement statistic and divided canopy models detected a larger number of threats on average. Ultimately, our best model was capable of the correct detection, segmentation, matching, and classification of half of the field trees which were determined to be vegetation threats

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

Predictive and Prescriptive Analytics for Managing the Impact of Hazards on Power Systems

Natural hazards and extreme weather events have the potential to cause significant disruptions to the electric power grid. The resulting damages are, in some cases, very expensive and time-consuming to repair and they lead to substantial burdens on both utilities and customers. The frequency of such events has also been increasing over the last 30 years and several studies show that both the number and intensity of severe weather events will increase due to global warming and climate change. An important part of managing weather-induced power outages is being properly prepared for them, and this is tied in with broader goals of enhancing power system resilience. Inspired by these challenges, this thesis focuses on developing data-driven frameworks under uncertainty for predictive and prescriptive analytics in order to address the resiliency challenges of power systems. In particular, the primary aims of this dissertation are to: 1. Develop a series of predictive models that can accurately estimate the probability distribution of power outages in advance of a storm. 2. Develop a crew coordination planning model to allocate repair crews to areas affected by hazards in response to the uncertain predicted outages. The first chapter introduces storm outage management and explains the main objectives of this thesis in detail. In the second chapter, I develop a novel two-stage predictive modeling framework to overcome the zero-inflation issue that is seen in most outage related data. The proposed model accurately estimates customer interruptions in terms of probability distributions to better address inherent stochasticity in predictions. In the next chapter, I develop a new adaptive statistical learning approach based on Bayesian model averaging to formulate model uncertainty and develop a model that is able to adapt to changing conditions and data over time. The forth chapter uses Bayesian belief network to model the stochastic interconnection between various meteorological factors and physical damage to different power system assets. Finally, in chapter five, I develop a new multi-stage stochastic program model to allocate and relocate repair crews in impacted areas during an extreme weather event to restore power as quickly as possible with minimum costs. This research was conducted in collaboration with multiple power utility companies, and some of the models and algorithms developed in this thesis are already implemented in those companies and utilized by their employees. Based on actual data from these companies, I provide evidence that significant improvements have been achieved by my models.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168024/1/ekabir_1.pd

Gulf Cooperation Council Countries’ Electricity Sector Forecasting : Consumption Growth Issue and Renewable Energy Penetration Progress Challenges

The Gulf Cooperation Council (GCC) countries depend on substantial fossil fuel consumption to generate electricity which has resulted in significant environmental harm. Fossil fuels also represent the principal source of economic income in the region. Climate change is closely associated with the use of fossil fuels and has thus become the main motivation to search for alternative solutions, including solar and wind energy technologies, to eliminate their reliance on fossil fuels and the associated impacts upon climate. This research provides a comprehensive investigation of the consumption growth issue, together with an exploration of the potential of solar and wind energy resources, a strict follow-up to shed light on the renewable energy projects, as currently implemented in the GCC region, and a critical discussion of their prospects. The projects foreshadow the GCC countries’ ability to comply with future requirements and spearhead the renewable energy transition toward a more sustainable and equitable future. In addition, four forecasting models were developed to analyse the future performance of GCC power sectors, including solar and wind energy resources along with the ambient temperatures, based on 40 years of historical data. These were Monte Carlo Simulation (MCS), Brownian Motion (BM), and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model model-based time series, and bidirectional long short-term memory (BI-LSTM) and gated recurrent unit (GRU) model-based neural networks. The MCS and BM prediction models apply a regression analysis (which describes the behaviour of an instrument) to a large set of random trials so as to construct a credible set of probable future outcomes. The MCS and BM prediction models have proven to be an exceptional investigative solution for long-term prediction for different types of historical data, including: (i) four types of fossil fuel data; (ii) three types of solar irradiance data, (iii) wind speed data; and, (iv) temperature data. In addition, the prediction model is able to cope with large volumes of historical data and different intervals, including yearly, quarterly, and daily. The simplicity of implementation is a strength of MCS and BM techniques. The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, an approach that helps to reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. This iii research proposes a forecasting framework that applies the SARIMAX model to forecast the long-term performance of the electricity sector (including electricity consumption, generation, peak load, and installed capacity). The SARIMAX model was used to forecast the aforementioned factors in the GCC region for a forecasted period of 30 years from 2021 to 2050. The experimental findings indicate that the SARIMAX model has potential performance in terms of categorisation and consideration, as it has significantly improved forecasting accuracy when compared with simpler, autoregressive, integrated, moving average-based techniques.The BI-LSTM model has the advantage of manipulating information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BI-LSTM’s output layer concurrently receives information from both the backward and forward layers. The BI-LSTM prediction model was designed to predict solar irradiance which includes global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) for the next 169 hours. The findings demonstrate that the BI-LSTM model has an encouraging performance in terms of evaluation, with considerable accuracy for all three types of solar irradiance data from the six GCC countries. The model can handle different sizes of sequential data and generates low error metrics. The GRU prediction model automatically learned the features, used fewer training parameters, and required a shorter time to train as compared to other types of RNNs. The GRU model was designed to forecast 169 hours ahead in terms of forecasted wind speeds and temperature values based on 36 years of hourly interval historical data (1st January 1985 to 26th June 2021) collected from the GCC region. The findings notably indicate that the GRU model offers a promising performance, with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalisable processes. The GRU model is characterised by its superior performance and influential evaluation error metrics for wind speed and temperature fluctuations. Finally, the models aim to help address the issue of a lack of future planning and accurate analyses of the energy sector's forecasted performance and intermittency, providing a reliable forecasting technique which is a prerequisite for modern energy systems