Effective Risk Mitigation Strategies Research Articles

Multivariate Analysis of Compound Hail, Wind and Rainfall Extremes in Alberta’s Hail Alley

Hailstorms are severe weather events with the potential for devastating impacts. The consequences can be significantly worsened when hail events are accompanied by strong winds, intensifying both hail momentum and damage to property sidings and windows. Additionally, rainfall extremes during hailstorms can disrupt the drainage systems, potentially leading to flash flooding. Therefore, understanding the inter-dependencies and joint behaviour of these hazards is crucial for developing effective risk mitigation strategies. In this study, we conduct a multivariate probabilistic assessment of concurrent hail, wind, and rainfall extremes over the Alberta’s “hail alley” using radar and ground-based observations. The analysis comprehensively explores individual hazards, as well as bivariate and trivariate scenarios using a vine copula approach. We quantify individual, conditional, and joint return periods (JRPs) for the various scenarios. Findings indicate that in both wind-driven hail and hail-rainfall extreme hazards, the joint occurrences based on JRP, can be underestimated by 30% and 70% when assuming independence, respectively, which has substantial implications for risk assessment and management, as well as infrastructure design and maintenance. The analysis of the trivariate case suggests the potential for the concurrent occurrence of multiple hazards in the region. Furthermore, results show that Archimedean copula families outperform elliptical copulas in simulating extreme variables related to compound events associated with hailstorms. The study stresses the importance of assessing the joint behaviour of these hazard components in hailstorms, with the objective of mitigating potential impacts on vulnerable regions.

Risk Mitigation Strategies for Sustainable Poultry Supply Chain Management

The livestock sector is an important pillar in providing animal protein and sustaining the rural economy. However, the sector faces major challenges from environmental and socio-economic issues, such as climate change and environmental degradation, which can threaten its sustainability. Negative impacts such as environmental contamination can reduce production quality and quantity and increase supply chain operational costs. This study aims to identify effective risk mitigation strategies to reduce these negative impacts and improve the sustainability of supply chain management. Data were collected from laying duck farms and analyzed using the House of Risk (HOR) method with a Phase 1 and 2 approach. This approach allows the identification of the most critical risks and risk agents and mapping mitigation priorities. Key findings indicate that providing drugs or vaccines to prevent animal virus outbreaks is the highest priority mitigation strategy, while strategic policy decision-making has the lowest priority. Overall, 15 risks and 21 risk agents were identified. This study implies that the implementation of effective mitigation strategies can significantly reduce operational risks, strengthen the resilience of the livestock sector, and support the sustainability of supply chain management as a whole.

Identifying the risk factors of patient safety in internet hospitals: A mixed methods study

ObjectiveThe purpose of this study was to identify key risk factors and their interrelationships for patient safety in internet hospitals from a system perspective, using mixed methods of qualitative and quantitative analysis. MethodsThis study constructed a comprehensive indicator system of patient safety risk factors in internet hospitals by qualitative analysis using the Patient Safety Systems (SEIPS) model as a framework. Risk factors were initially identified through a literature review and subsequently refined using a Delphi survey involving 24 experts related to internet hospitals in China. The identified indicators were quantitatively analyzed to determine key risk factors and their influencing mechanism using the Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. ResultsThe qualitative analysis established a patient safety risk factor indicator system for internet hospitals, comprising 23 elements across six dimensions. Quantitative analysis employing the DEMATEL-ISM approach revealed that risk management has the highest centrality. Among cause factors, task complexity exerts the most significant impact on other factors, while network information security exhibits the highest absolute value among result factors. Risk factors are categorized into three levels: surface, deep, and root factors, with task complexity, legal and regulatory, and guidance policy being the root factors at the foundation of the system. ConclusionsOur study offered a systemic perspective on analyzing risk factors for patient safety in internet hospitals. Policymakers and managers of internet hospitals should take advantage of the interrelationships among these factors to mitigate patient safety risks by effectively controlling key factors. Public Interest SummaryIn the rapidly evolving landscape of internet hospitals, ensuring patient safety is paramount. This study aimed to comprehensively identify and understand key risk factors influencing patient safety within these digital healthcare platforms. Using mixed methods of qualitative and quantitative analysis, the study examined the intricate interplay of factors affecting patient safety. Our methodology involved constructing a risk factors indicator system based on the Patient Safety Systems (SEIPS) model. By employing the integrated Decision-Making Trial and Evaluation Laboratory along with the Interpretive Structural Modeling method, we unveiled the core risk factors and their intricate relationships. Recognizing the interconnectivity of these factors allows us to develop effective risk mitigation strategies that enhance patient safety in internet hospitals. This study encourages stakeholders to leverage the dynamic relationships among these factors to ensure safer online healthcare experiences for patients.

A comprehensive approach to evaluate risk mitigation strategies in offshore wind farms using spherical fuzzy decision making analysis

The escalating impact of global warming, primarily exacerbated by carbon emissions from conventional energy sources, has prompted significant advancements in offshore wind energy as a pivotal avenue for sustainable development. Amid the surging interest in renewable energy, offshore wind-farms have emerged as a promising solution. To ensure their efficient and effective operation, it becomes imperative to systematically identify and mitigate potential risks. This study addresses the critical need to systematically prioritize risk reduction strategies for offshore wind farms, a problem that has not been comprehensively explored in the literature. A multi-criteria analysis has been adopted to simultaneously evaluate the contradictory criteria in the evaluation process. The novelty of this study lies in its integration of spherical fuzzy sets with multi-criteria decision-making (MCDM) techniques to handle uncertainties and evaluate contradictory criteria simultaneously. As a result of all this analysis, the most critical risks for offshore wind-farms and risk mitigation strategies that can be adopted within the determined risks have been revealed. The main findings reveal that turbine underperformance, disruption of habitats, and grid connection issues are the most critical risks. Furthermore, a combination of robust design and engineering and collaboration and partnerships emerged as the most effective risk mitigation strategies.

Risk analysis of petroleum storage tank based on uncertain data incorporated into mapped Bow-tie to Bayesian network

The petroleum industry has an important impact on the environment, which is why risk analysis for a petroleum storage tank is required in the process industry. However, conducting an exhaustive study is frequently complicated due to the incomplete data based on historical system information, feedback, or test results. The main goal of this study is to develop an innovative framework that integrates the Bow-tie method with MIMAH (Methodology for the Identification of Major Accident Hazards). This approach specifically aims to identify the most critical accident scenario and comprehensively list all realistic causes (basic events) associated with petroleum storage tanks. Acquiring historical data on basic events poses challenges due to the lack, insufficiency, and imprecision of failure data. To address this obstacle and predict the system’s evolution, a combination of established methods for handling uncertainty and multi-criteria decision-making has been proposed. This combination incorporates a modified Fuzzy Analytical Hierarchy Process with Chang’s Extent Analysis Method (FAHP-EAM), fuzzy set theory, and expert elicitation, contributing to an extensive approach for managing uncertainty and making informed decisions in the absence of comprehensive historical data. Furthermore, the Bow-tie model is converted into a dynamic Bayesian network to improve risk analysis by integrating probabilistic reasoning and capturing the dynamic interactions among multiple risk factors. This integrated approach is validated on real scenarios. The utilization of dynamic Bayesian networks further facilitates the suggestion of scenarios to assess the contribution of basic events to the critical event and evaluate the resulting environmental impacts. Finally, the prediction of threat zones for various hazardous effects, such as toxicity, thermal radiation, and overpressure linked to chemical releases following the occurrence of the critical event, is carried out. The risk analysis outcomes from this study offer valuable data for assessment, enabling managers to improve the quality of risk management and formulate effective risk mitigation strategies.

Linking local and global: Assessing water scarcity risk through nested trade networks

AbstractAs water scarcity escalates globally, there is a growing focus on assessing its economic impact, both locally and through trade networks, defined as water scarcity risk (WSR). Given China's pivotal role as a top importer and exporter, coupled with significant spatial disparities in water scarcity across its provinces, understanding the spread of water scarcity risks within China and their interplay with global water dynamics is imperative yet relatively unexplored. This study constructs a nested Multi‐Regional Input‐Output (MRIO) model to evaluate water scarcity risk, connecting Chinese provinces with global counterparts. Notably, we integrate the spatial heterogeneity of environmental flow requirement as a vital criterion for long‐term sustainable water management in our assessment. Our findings reveal substantial economic impacts caused by water scarcity, with a global WSR valued at $11.1 trillion, of which $6.2 trillion is attributed to trade. Critical regions, sectors, and region‐sectors are identified, offering valuable insights for targeted interventions. This study presents a unique perspective on global water scarcity risk assessment at a higher resolution scale, laying the groundwork for the formulation of effective water scarcity risk mitigation strategies aligned with sustainable development goals, particularly SDG 6 and 8.

Erosion, deposition and breach evolution of landslide dams composed of various dam material types based on flume tests

The accurate and rapid prediction of breach outflow rates of landslide dams is crucial for effective risk assessment and mitigation strategies. However, challenges arise due to the complexities associated with breaching mechanisms. In this study, 12 flume tests were conducted on landslide dams composed of various material types, including fine-grained, coarse-grained, well-graded, and gap-graded materials, each with two dry densities. This study comprehensively investigated erosion, deposition, and breach evolution, providing the foundation for the development of breach evolution models. A comprehensive logic diagram, focusing on shear stress and grain size distribution, was proposed to forecast the failure modes of landslide dams. The study also suggested a method to assess the longitudinal breach evolution based on the prediction of the Erosion Point (EP) and Deposition Point (DP), and to assess the lateral breach evolution by considering both steep and gentle breach side slopes. Our findings indicate that fine-grained materials undergo layer erosion, coarse-grained materials remain stable, well-graded materials develop erosion pits, and gap-graded materials are prone to piping. The existence of an interface can result in piping channels, even when the overall hydraulic gradient is insufficient to initiate piping. Incorporating the longitudinal slope angle and its influence on both driving and resisting forces into the traditional erosion rate equation can significantly enhance its accuracy, especially when coarse particles are present. Coarse particles play a pivotal role in erosion and deposition behavior and reshaping the breach evolution as the initiation of coarse particles is dominated by rolling instead of slipping in fine particles. Deposition on the downstream slope of a dam can significantly influence the longitudinal breach process, leading to changes in the downstream topography and grain size distribution. The proposed method for assessing longitudinal breach evolution unifies the previous methodologies by predicting the positions of EP and DP based on erosion and deposition.

Comprehensive risk assessment of Pakistan railway network: A semi-quantitative risk matrix approach

Pakistan Railway (PR) is vital to the country's transportation infrastructure, facilitating passenger and freight transportation. However, the growing number of accidents associated with PR has raised concerns about its overall safety. In addition, there have been limited research efforts to investigate PR accidents and their underlying causes The present study conducts a comprehensive risk assessment and safety management of PR using a semi-quantitative risk matrix approach. The study combines historical accident data and expert evaluations to assess the likelihood and consequences of different railway accident types and potential contributing factors. The descriptive statistics analysis has revealed varying degrees of severity for different types of railway accidents in Pakistan. For instance, accidents like passenger and goods train derailments and collisions at unmanned level crossings were identified as extreme and intolerable, whereas train fire accidents were categorized as high and undesirable. Moreover, accidents attributed to human error are classified as extreme and intolerable, while those caused by negligence of road users and track defects are classified as high and undesirable. The study utilized the risk matrix approach and identified critical risk areas that can help the decision-makers prioritize effective risk mitigation strategies. In light of the present study's findings, policy implications, such as investment in infrastructure to mitigate risks associated with aging or deteriorating tracks, bridges, and tunnels, and human resource development for railway personnel to improve their skills, decision-making abilities, and awareness of safety protocols, are recommended.

The heterogeneity of human flood adaptation characteristics in Central Asia based on human-flood distance

The Central Asian region is subject to frequent seasonal floods, resulting in substantial losses. These recurrent floods have induced certain changes in human flood adaptation characteristics in Central Asia, an area that is currently under-researched. This study, grounded in meticulously simulated flood inundation outcomes, multisource population distribution data, and flood protection infrastructure data, introduces an improved approach to calculating the distance between human and flood (HFD) and investigates human flood adaptation characteristics in Central Asia. Our findings indicate that residents in Central Asia get close to flood-prone areas from 1970 to 2007, with the exceptions of Turkmenistan (TKM) and Kyrgyzstan (KGZ). Notably, this trend is particularly pronounced in regions such as Xinjiang, China, and Tajikistan (TJK). An intricate exponential increase is observed in the population exposed to floods as the HFD decreases. Under future scenarios, KGZ exhibits the most significant trend of staying away from flood-prone areas, while TJK presents the opposite trend, and TKM experiences minimal changes. Additionally, compared with urban areas, rural areas of Central Asia exhibit a trend towards approaching floods. This study underscores the optimum nature of human flood adaptation when the HFD reaches 4.23 km, showing the potential for substantial benefits by implementing population evacuation within this threshold buffer zone. These insights highlight the complex interplay between human behaviours and floods and can inform the formulation of effective flood risk mitigation strategies.

Advanced risk management solutions for mitigating credit risk in financial operations

Advanced risk management solutions are essential for mitigating credit risk in financial operations, particularly in today's volatile economic environment. This Review explores the innovative approaches and technologies being utilized to enhance credit risk management and safeguard financial institutions against potential losses. Credit risk, the possibility that a borrower will default on their obligations, poses a significant threat to financial stability. Traditional methods of assessing and managing credit risk, such as credit scoring and historical data analysis, are no longer sufficient to address the complexities of modern financial markets. Advanced risk management solutions offer more robust and dynamic tools for identifying, assessing, and mitigating credit risk. One of the key advancements in this field is the integration of big data and machine learning algorithms. By analyzing vast amounts of data from diverse sources, financial institutions can gain deeper insights into borrower behavior, market trends, and economic indicators. Machine learning models can predict default probabilities with greater accuracy, allowing for proactive risk mitigation strategies. Additionally, real-time risk monitoring systems have become increasingly prevalent. These systems provide continuous oversight of credit portfolios, enabling financial institutions to detect early warning signs of potential defaults and take timely action. Advanced analytics and visualization tools facilitate the identification of emerging risks and the implementation of targeted interventions. Another crucial component of advanced risk management solutions is the use of stress testing and scenario analysis. These techniques simulate various economic conditions and assess the impact on credit portfolios, helping financial institutions prepare for adverse scenarios and develop contingency plans. Furthermore, blockchain technology is emerging as a promising tool for enhancing transparency and reducing fraud in credit transactions. By providing a secure and immutable record of credit histories and transactions, blockchain can improve trust and reliability in credit assessments. In conclusion, advanced risk management solutions are transforming the way financial institutions mitigate credit risk. By leveraging big data, machine learning, real-time monitoring, stress testing, and blockchain technology, financial institutions can achieve more accurate risk assessments and implement more effective risk mitigation strategies. These innovations not only enhance the stability and resilience of financial operations but also contribute to a more secure and trustworthy financial system.

Effective credit risk mitigation strategies: Solutions for reducing exposure in financial institutions

Credit risk remains a critical concern for financial institutions, especially in the context of economic uncertainties and volatile market conditions. This paper examines the importance of credit risk mitigation strategies and presents solutions for reducing exposure in financial institutions. The research focuses on various strategies employed by banks and other financial institutions to manage credit risk effectively. The paper begins with an overview of credit risk and its significance in the banking industry. It discusses the various types of credit risk faced by financial institutions, including default risk, concentration risk, and systemic risk. The paper then highlights the importance of credit risk mitigation strategies in reducing the impact of these risks on financial institutions' balance sheets and profitability. Next, the paper explores different credit risk mitigation strategies used by financial institutions. These strategies include diversification of credit portfolios, collateralization of loans, credit derivatives, and credit insurance. The paper discusses the advantages and disadvantages of each strategy and provides examples of how these strategies have been implemented successfully in the banking industry. Furthermore, the paper examines the role of regulatory authorities in overseeing credit risk management practices in financial institutions. It discusses the regulatory framework for credit risk management and highlights the importance of compliance with regulatory requirements in mitigating credit risk. Finally, the paper concludes with a discussion on the future of credit risk mitigation strategies in financial institutions. It emphasizes the need for continuous innovation and adaptation to new market conditions and regulatory requirements. The paper also stresses the importance of integrating credit risk management into overall risk management frameworks to ensure a comprehensive and effective approach to managing risk in financial institutions. Overall, this paper provides valuable insights into the importance of credit risk mitigation strategies in financial institutions and offers practical solutions for reducing exposure to credit risk. It serves as a useful resource for financial professionals, regulators, and policymakers seeking to enhance their understanding of credit risk management practices in the banking industry.

Digital Divide’s Impact on Accounting Information Systems Risks Faced by Financial Auditors: A Jordanian Case Study

The study emphasizes the importance of organizational structure, human capital, and legal controls in reducing accounting system risks. Three research questions explore the impact of the organizational structure, the role of human capital, and the significance of legal controls in risk reduction. Using statistical analysis, 104 questionnaires were distributed, and 76 were collected for the statistical analysis, including simple linear regression. The study reveals significant findings. The organizational structure plays a positive and influential role in establishing sound internal control systems that reduce accounting system risks. Qualified human capital contributes to technical progress, enhancing the accuracy and efficiency of financial audit activities. Robust legal controls effectively identify deviations, ensuring data integrity and compliance with international standards. Recommendations for the JDRC include establishing a clear organizational structure, investing in qualified human capital, and implementing comprehensive legal controls to mitigate accounting system risks. This study contributes to the literature by addressing unique variables and highlighting the risks and limitations of accounting systems within the JDRC. The findings inform organizations in similar contexts, guiding effective risk mitigation strategies and enhancing accounting system performance.

The role of uncertainties in the seismic assessment of masonry churches affected by compound rocking failure mechanism: Macro-block limit analysis investigations

Façade overturning is one of the most common failure mechanisms observed in single-nave masonry churches when subjected to seismic action. Several factors, including geometry, masonry patterns, mechanical properties, and loading conditions, influence the force and displacement capacities of these masonry churches. This study aims to assess the impact of various model parameters on the seismic assessment of single-nave masonry churches subjected to the compound rocking failure mechanism by adopting macro-block limit analysis. The analysis of variance (ANOVA) is employed to investigate the influence of geometrical and mechanical parameters under the assumption of complete knowledge of the structure. Furthermore, probabilistic analysis explores how incomplete knowledge may affect the structural assessment of the compound rocking failure mechanism in single-nave masonry churches. The findings of this study highlight the importance of accurate modelling and surveying in evaluating the seismic vulnerability of single-nave masonry churches and can be useful for developing effective seismic risk mitigation strategies.

Boosting the Organizational Performance: Ethical Climate as a Moderator for Optimizing Risk Management Practices in the Saudi Public Sector

Purpose: Drawing upon contingency theory, this study aims to investigate the relationship between risk management practices (RMPs), ethical climate (EC), and organizational performance (OP) in public organizations within the Kingdom of Saudi Arabia (KSA). Specifically, the study explores the moderating effect of EC in shaping the relationship between RMPs and OP. Design/methodology/approach: Survey data were collected from a convenience sampling of 392 senior government directors within public organizations in the KSA through an online questionnaire. The relationships between study constructs were analyzed using PLS-SEM with the bootstrapping technique. Findings: The study's findings reveal a significant positive impact of both RMPs and EC on organizational performance. Notably, the research demonstrates that ethical climate significantly moderates the positive relationship between RMPs and OP, highlighting the critical importance of ethical conduct in maximizing the effectiveness of risk mitigation strategies. Research limitations/implications: The study's focus on the Saudi public sector and its use of a convenience sampling technique, primarily comprising senior government directors, are significant limitations when interpreting this study's findings. Expanding research to incorporate diverse organizational settings could significantly enhance this work's generalizability and practical implications. Additionally, focusing solely on EC as a moderator in the RMP-OP relationship overlooks other potentially relevant factors. Future research could delve deeper into the mechanisms through which ethical climate strengthens the impact of RMPs on performance, providing valuable guidance for organizational interventions. Originality/value: This study's originality lies in its first-time examination of ethical climate as a moderator in the relationship between RMPs and organizational performance within Saudi public organizations. Further, study findings expanded the scope of contingency factors demonstrating that a strong ethical climate significantly amplifies the positive impact of RMPs on performance, providing compelling evidence for considering ethical climate as a crucial contingency factor for public sector success.

Species-specific eDNA assay development for enhanced box jellyfish risk management in coastal environments

Human interaction with marine creatures holds both positive and negative dimensions. Coastal communities benefit from marine environments, relying on them for sustenance and livelihoods. Fishing activities support economies, and marine biodiversity contributes to overall ecosystem health. However, challenges like overfishing, habitat destruction, and pollution pose threats to both marine life and human communities. Recently, there has been widespread concern regarding the potential increase in jellyfish populations across global marine ecosystems, attributed mainly to environmental factors such as climate drivers and anthropogenic forces, or their complex interactions. Encounters with hazardous marine species, such as box jellyfish, exemplify the dangers associated with coastal activities. Unintended interactions may lead to stings, injuries, and even fatalities, necessitating proactive measures and advanced technologies. This study addresses the inadequacies of existing measures in preventing box jellyfish incidents by introducing environmental DNA (eDNA) assays for detecting the deadly Chiropsoides buitendijki and focuses on developing qPCR and dPCR-based eDNA assays. Emphasising prevention over treatment, the study establishes a proactive system to assess C. buitendijki distribution across 63 tourist beaches in the Gulf of Thailand. Comparative analysis highlights the superior performance of dPCR over qPCR and traditional surveys. The dPCR experiment yielded positive results for all eDNA samples collected at sites where C. buitendijki had previously been identified. Remarkably, the eDNA testing also detected positive results in 16 additional sample locations where no physical specimens were collected, despite reported jellyfish stings at some of these sites. These findings underscore the precision and efficacy of the proposed eDNA detection technology in the early detection and assessment of box jellyfish distribution. This advancement therefore not only aids ecological research but also serves as a valuable tool for safeguarding public health, providing an early warning system for potential jellyfish encounters. Balancing positive human-marine interactions with effective risk mitigation strategies is crucial for sustainable coexistence, the preservation of marine ecosystems, and human well-being.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Evaluating the crash risk of powered two-wheelers in urban mixed traffic environments: a conflict threshold perspective

The study investigates the crash risk of powered two-wheelers (PTWs) involved in multiple conflict types, with different vehicle classes constituting a mixed traffic stream. This study uses the extreme value theory to estimate the crash risk after establishing the conflict thresholds for potential rear-end and side-swipe conflicts by accounting for interacting vehicle types. The study considers four vehicle pairs involving PTWs: PTW-PTW, PTW-MThW (motorized three-wheeler), PTW-Car, and PTW-Bus. The study found that the conflict thresholds corresponding to rear-end and side-swipe types increase with the interacting vehicle size. The crash risk is lowest for the PTW-PTW pair (0.315%) in rear-end conflicts, whereas the risk is the highest for the PTW-MThW pair (3.7%) in side-swipe conflicts. The crash risk corresponding to the PTW interacting with other vehicle types is higher than that of the PTW-PTW pair. Hence, the implementation of exclusive PTW lanes could be an effective risk mitigation strategy for PTW-dominant mixed traffic environments.

Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

Earthquakes pose significant risks globally, necessitating effective seismic risk mitigation strategies like earthquake early warning (EEW) systems. However, developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations. This study examines a deep-learning-based on-site EEW framework known as ROSERS (Real-time On-Site Estimation of Response Spectra) proposed by the authors, which constructs response spectra from early recorded ground motion waveforms at a target site. This study has three primary goals: (1) evaluating the effectiveness and applicability of ROSERS to subduction seismic sources; (2) providing a detailed interpretation of the trained deep neural network (DNN) and surrogate latent variables (LVs) implemented in ROSERS; and (3) analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations. ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions. Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database, especially given the peculiarities of the subduction seismic environment. The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships. Finally, the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity. The results indicate that on-site predictions can be considered reliable within a 2–9 km radius, varying based on the magnitude and distance from the earthquake source. This information can assist end-users in strategically placing sensors, minimizing blind spots, and reducing errors from regional extrapolation.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

Anticipating a risky future: long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open-source basis globally, and implement machine learning models tailored for time series analysis, i.e., based on long short-term memory (LSTM) networks, for prediction of future time steps. Specifically, we harvest WorldPop population data and teach LSTM and convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms, which are derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and random forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data; the most accurate prediction obtained with an LSTM equipped with a bidirectional learning scheme features a root-mean-squared error of 3.63 people per 100 × 100 m grid cell while maintaining an excellent model fit (R2= 0.995). We deploy this model for anticipation of population along a 3-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 cm s−2, the population is anticipated to experience growth of almost 30 % over the forecasted time span, which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area is anticipated to grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.