In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the te...
Forecasting the occurrence potential of landslides is important but challenging. We aimed to forecast the failure potential of the Helong landslide, which is temporarily stable but has clearly deformed in recent years. To achieve the goal, we used re...
Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a com...
The estimation of the area susceptible to rock failure and the prediction of its movement process are pivotal for hazard mitigation, yet they are also challenging. In this study, we proposed a novel integrated method combining field investigation, re...
Patterns in motion characterize failure precursors in granular materials. Currently, a broadly accepted method to forecast granular failure from data on motion is still lacking; yet such data are being generated by remote sensing and imaging technolo...
Expansive soil is one of the most widely distributed special soils in the world. It is widely developed in Henan, Anhui, Guangxi and other places in China, and highly overlaps with densely populated and economically active areas. Expansive soil is co...
The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure pred...
To improve power system risk prediction under complex and extreme operating conditions, a hybrid N-BEATS-based framework is proposed for equipment assessment and loss of load expectation (LOLE) forecasting. The method integrates thermal circuit model...
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This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme...
With the improvement of short-term flood forecasting and short-term rainfall forecast accuracy, as well as the advance of hydrological and meteorological information collection and collation methods, the reservoir flood regulation method taking rainf...
Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining u...
The development of appropriate assessment methods of gas-pipeline functionality contributes to the reduction of failure consequences and helps engineers to make the right decisions as to the optimal solution choice for technical facilities, as well a...
This paper explores an important problem in tourism demand analysis, namely, the inherent uncertainty involved in projecting tourism demand. Tourism demand continues to be severely affected by unforeseen events associated with the current global heal...
High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advance...
The intermittent and lumpy demand of spare parts requires the choice of the right forecasting model among a variety of existing methods. Spare parts have an uneven lifecycle and mean time to failure for each individual item. As a result, they have a...
Lithium–sulfur (Li-S) batteries are promising energy storage solutions due to their high density and cost-effectiveness. However, the risk of thermal failure limits their widespread use. Understanding thermal failure characteristics and develop...
Accurate forecasting of solar energy production plays a crucial role in optimizing power system reliability, scheduling, and integration of renewable energy sources into the grid. From a sustainability perspective, improved forecasting accuracy suppo...
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failur...
Accurate electricity forecasting is essential for smart cities to maintain grid stability by allocating resources in advance, ensuring better integration with renewable energies, and lowering operation costs. However, most forecasting models that use...
Advancements in energy technologies created a new application for gas turbine generators, which are used to balance load. This usage also brought new challenges for maintenance because of harsh operating conditions that make turbines more susceptible...
The concept of risk assessment is an important tool in the asset integrity management of power distribution systems. This manuscript presents a risk-based asset integrity management (RBAIM) methodology for the optimization of power distribution asset...
The secure operation of the renewable-integrated power system is affected by extreme weather conditions such as typhoons. In order to meet the operational requirements of the system, it is necessary to dynamically evaluate the resilience of the renew...
It is very important to analyze the performance of the model rainstorm forecast for understanding the intensity and position deviation of the model precipitation and improving the forecast ability. This paper uses classical scoring and the MODE (Meth...
Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. Howev...
Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use...
Under the strong interference of sky background noise, the reliability of celestial navigation system (CNS) measurement will drop sharply, which leads to performance deterioration for ships’ strapdown inertial navigation system (SINS)/CNS integ...
Forecasting flood characteristics (e.g., water levels and velocity) is a growing concern due to climate change. It is therefore necessary to consider the stability conditions of earthen levees used to mitigate floods during a flood risk assessment. T...
Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load fore...
Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (L...
In the process of developing mature deposits, a number of geological and technological complications arise. In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. Th...
This paper introduces a novel approach to anomaly-based failure forecasting that jointly optimizes both the ensemble function and the anomaly threshold used for decision making. Unlike conventional methods that apply fixed or classifier-defined thres...
As a prevalent geological hazard in underground engineering, the accurate prediction of mine earthquakes is crucial for ensuring operational safety and enhancing mining efficiency. The deformation localization method effectively predicts the instabil...
Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Des...
This paper examines the causes of widespread maize production failure in Ghana during the 2020 minor growing season. A mixed-methods approach was used to study smallholder maize farmers in the Ejura-Sekyedumase Municipality to provide a holistic unde...
This study presents a systematic review of fatigue analysis methodologies and failure criteria for asphalt binders and mixtures employed in various cyclic fatigue testing configurations. The investigation focuses on two principal predictive approache...
In many tourism destinations, sustainability of the local economy leans on small and medium-sized hotels that are individually owned and operated by members of the community. Suffering from seasonality more than their big competitors, these hotels sh...
The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, whic...
Health risks connected with fine particulate matter (PM2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM2.5. Accurately forecasting PM2.5 is crucial for state ag...
Time-series forecasting is the core of the prognostics and health management (PHM) of turbomachinery. However, missing data often occurs due to several reasons, such as the failure of sensors. These partially missing and irregular data greatly affect...
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabi...
The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power syst...
Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and...
Currently, Industry 4.0 creates new opportunities for analyzing data on production processes and extracting knowledge from them. With the Internet of Things, data is continuously collected from machine sensors to analyze machine health. Thanks to art...
Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study dea...
Diesel engine failures are one reason for delays and breakdowns on the UK rail network, resulting in significant fines and related financial penalties for a train operating company. Preventing such failures is the ultimate goal, but forecasting or pr...
Thunderstorms endanger life and infrastructure. The accurate and precise prediction of thunderstorms is therefore helpful to enable protection measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm...
Wind power forecasting is critical to renewable energy generation, as accurate predictions are essential for the efficient and reliable operation of power systems. However, wind power output is inherently unstable and is strongly affected by meteorol...
This work aims at defining in a probabilistic manner objectives and constraints typically considered in route optimization systems. Information about weather-related uncertainties is introduced by adopting ensemble forecast results. Classical reliabi...
The development of appropriate methods by which to assess the functioning of gas pipelines helps limits the consequences of disasters, in that way protecting users better. Assessment methods of this kind are presented in this paper, which advocates t...
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