Risk Management Plan Research Articles

The Assessment of Risk Management in Human Resources Practices in the Public Sector

HR-related risks can disrupt operations and cause significant problems for organizations. Effective HR risk management is essential for mitigating these risks. A strong HR risk management plan ensures adherence to labour laws, prevents costly legal issues, and safeguards public trust. Public sector organisations can proactively address potential HR risks and attract and retain top talent, improve employee morale, and achieve their goals more effectively. The primary purpose of this research was to critically investigate and analyze current HR risk management practices—justification of the choice of the research object. A questionnaire survey was conducted with 30 Erongo Regional Council staff members to explore HR risk management practices in a public sector organization. Justification of the choice of the research and research period: Informational and statistical basis of research. A descriptive research design was used to collect and analyze data. Descriptive statistics, hypothesis testing, and regression analysis were employed. A questionnaire survey was used to collect data from 30 Erongo Regional Council staff members. The main results of the research. The study found that the respondents understand HR risk management practices and the organization manages HR risks. A positive correlation was found between HR risk management and organizational performance. However, the study also revealed challenges in HR risk management implementation. Questionnaires are cost-effective and can reach many respondents within a short time frame. It allows you to gather data from a more comprehensive range of public sector organizations and get a broader perspective. This study recommends increased employee awareness of how HR risks can disrupt operations. By fostering awareness, organizations can minimize the likelihood of overlooking potential risks. These programs can equip them with the knowledge and skills to implement effective risk management strategies.

Human activities are intensifying the spatial variation of landslides in the Yellow River Basin

Human activities are a triggering factor for landslides in the Yellow River Basin (YRB, China). However, the extent to which the spatial distribution of landslides is affected by human activities is unclear. We constructed a human activity intensity index (HAII) based on nighttime light data and land cover data. Regression and dominance analyses were used to compare the effects of the HAII, precipitation, distance to river, distance to fault, topographic relief and slope on the landslides spatial density (LSD). The results showed that in the YRB, the HAII, as a dominance influencing factor, had a significant positive influence on the LSD. Moreover, regional differences in the human disturbance of nature intensify the spatial variation of LSD. To quantify the intensity of human disturbance to nature, a human-nature conflict index (HNCI) is constructed by quantifying the difference between the slope distributions of artificial and natural landscapes. The results show that in the middle section of the YRB, humans are developing more steep mountainous areas, leading to more dense landslides. This study provides a reference for landslide risk management and land use planning in the YRB.

SANITATION-SAFETY- PLANNING (SSP), THE NEXUS FOR CLIMATE RESILENCE, SANITION AND HEALTH IN BAYELSA STATE

In recorded human history, climate-related hazardous events have always existed; however, climate change has proved to have the potential to increase their severity and the likelihood of creating public health risks. In Bayelsa State, a sizable portion of the population has been reported to be suffering from gastrointestinal disorders particularly diarrhea and a number of children have been diagnosed of gastroenteritis and helminthes infections in the year 2021. Again, preventing exposure and contact to human waste, especially in dense urban settings requires the safe management of the entire sanitation service chain involving multiple actors and exposed groups in the collection, transport, treatment, disposal and end use of sanitation waste. To that end, a review of scientific literature, particularly the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (IPCC, 2014), the WHO discussion Paper on Climate, Sanitation and Health (WHO, 2019) and the Bayelsa State SSP zero draft of 2021, is undertaken to ensure that climate change outcomes are considered within the Sanitation Safety Planning (SSP) risk assessment, planning and management processes. The SSP is a WHO risk based management tool for the safe use and disposal of grey water, wastewater and excreta developed in 2006 because in 2018 the WHO concluded that sanitation interventions have shown lower than expected health outcomes. Evidently therefore, the Bayelsa State SSP zero draft of 2021 reports that between March and April 2021, over 200 cases were reported from over 30 communities arising from the outbreak of acute-gastroenteritis suspected to be of cholera, and casualty figures were over 26. Hopefully, the first comprehensive Guidelines on Sanitation and Health which aim to provide evidence-based recommendations and offer guidance to ensure international, national and local sanitation policies and programmes that effectively protect public health, was launched by the WHO in 2018. It is a step-by-step risk based approach to implementing local level risks assessment and a management tool for the entire sanitation service chain covering containment, conveyance, treatment and end use of disposal. Consequently, the SSP is hereby recommended for adoption in Bayelsa State and Niger Delta Region.

A High‐Performance Coupled Human And Natural Systems (CHANS) Model for Flood Risk Assessment and Reduction

AbstractIn recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high‐performance hydrodynamic model with an agent‐based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real‐time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood‐human dynamics and assess flood impact at the household‐level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short‐term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.

PROACTIVE RISK IDENTIFICATION IN THE CHANGING AUTOMOTIVE INDUSTRY: A COMPREHENSIVE FRAMEWORK

Advancements in technology, shifts in consumer preferences, and revised regulations are all propelling rapid growth in the automotive industry. Traditional approaches to risk management are insufficient when confronted with the emerging risks presented by this transition. Adopting a proactive approach to detecting hazards is essential for successfully navigating this constantly evolving world. The present work presents a comprehensive framework for the proactive identification and management of risks within the ever-changing automotive sector. The essay commences with providing an overview of the industry's historical context and current advancements, followed by an analysis of the inadequacy of conventional risk management methods facing emerging risks. Furthermore, it proposes a complete framework specifically tailored to tackle the unique challenges faced by the automotive sector. Implementing a comprehensive approach to risk management, this framework integrates assessments of technology, markets, legislation, cybersecurity, and supply chain vulnerabilities. The analysis proceeds to discuss potential issues associated with implementing this methodology and offers remedies to overcome them. In order to enhance their decision-making and resilience in the face of industry-wide complexity, stakeholders in the automobile sector might adopt a pre-emptive approach to risk management. This study contributes to our knowledge of proactive risk management strategies and enhances our understanding of future risk identification in the automobile industry. Methodology: By employing this approach, these enterprises can enhance their ability to make well-informed judgements on their risk management plans. The technique facilitates the identification of critical vulnerabilities, therefore enabling the implementation of preventive measures to mitigate the effects of specific risks on net income. Moreover, the model possesses the capability to undertake data analysis from multiple companies within the same industry, thereby offering potential insights that can be extrapolated to other similar organisations. This has the potential to result in enhanced risk management strategies within India's automobile industry.

Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics

In the financial industry, the ability to predict financial metrics accurately and in a timely manner can significantly impact investment decisions, risk management, and strategic planning. In recent years, machine learning has emerged as a powerful tool for such predictions. This study aims to explore the heretofore underexplored predictive potential of ChatGPT by predicting positive or negative changes in quarterly financial metrics, such as revenue and sales numbers, using textual data from social media. The performance of ChatGPT is compared against Long Short-Term Memory (LSTM) neural network models developed as part of this research. The methodology involves preprocessing large datasets from Twitter concerning major companies such as Amazon, Google, and Tesla, training LSTM models, and prompt engineering for ChatGPT-based predictions. Initial findings indicate that LSTM models can predict quarterly financial metric changes with up to 87% accuracy, significantly outperforming ChatGPT, which achieves a maximum accuracy of 67%. Therefore, at the current time, ChatGPT cannot be considered a reliable predictive tool for changes in quarterly financial metrics.

BEYOND CERTIFICATION: UNDERSTANDING RISKS IN HALAL ANIMAL FEED SUPPLY CHAINS

Animal feed is a crucial point of control in the production of halal animal-based food. Improper feeding practices repeatedly reported in the media have contributed to the issuance of Fatwas on the impermissibility of animals fed with non-halal feed or impurities in 2007. Hence, feeding animals with Halal feed would be a judicious effort to solve these problems. Nevertheless, in the production and supply chain of halal animal feed, numerous risks must be considered concerning the feeds safety, quality, and halal integrity. The use of non-halal ingredients in animal feed can render the animals non-halal for consumption, as stated by the Malaysia National Fatwa. Additionally, even if the feed is initially halal, it can become non-halal through contamination with non-halal additives, equipment, or utensils. Therefore, a halal risk assessment is necessary to identify and analyze potential contamination risks during feed production. This study focuses on analyzing halal risks in animal feed production in Malaysia, employing qualitative research methodology by utilising a library research approach to identify these risks. In addition, the identification of activities involved in the feed supply chain will also help to analyze the risks. Developing a halal risk analysis and risk ranking matrix is essential for creating a Halal Risk Management Plan for feed production. This framework will benefit feed manufacturers by preventing contamination and assisting Halal authorities in establishing specific standards for halal feed production.

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

Analysis of The Implementation of The Occupational Safety and Health Management System at Bhayangkara Kendari Hospital

Background: The occupational safety and health management system is part of the overall management system in the context of controlling work-related risks in order to create a safe, efficient and productive workplace. The aim of this research is to analyze the implementation of the occupational safety and health management system in hospitals. According to the management report of the Occupational Safety and Health committee at Bhayangkara Kendari Hospital in 2023, it was stated that the hospital had not carried out occupational risk management as part of the hospital’s occupational safety and health planning. Method: This research is a qualitative descriptive research method. There were 11 informants in the research. The sampling technique used was purposive sampling. Results: The results of the research show that Bhayangkara Kendari Hospital has implemented an Occupational Safety and Health Policy and Occupational Safety and Health risk management planning, but the results obtained in the Implementation of Occupational Safety and Health, Monitoring and Evaluation in the Hospital’s Occupational Safety and Health program do not meet standards. Optimal Occupational Safety and Health. Conclusion: Bhayangkara Kendari Hospital has implemented the Hospital Occupational Safety and Health policy in accordance with Minister of Health Regulation No. 66 of 2016 with a commitment to implementing Occupational Safety and Health, but is hampered by the unavailability of special health services. There are still health workers who provide health services without using personal protective equipment, and there are still those who violate the KTR and there are no monthly and annual evaluation reports on Hospital Occupational Safety and Health. This research can be used as reference material for hospitals to optimally implement Hospital Occupational Safety and Health to increase the knowledge of health workers in implementing occupational safety and health by familiarizing themselves with occupational safety and health cultural behavior.

Application of Financial Mathematical Models Combined with Root Algorithms in Finance

Investors in the financial markets must deal with various hazards, for which they must create prudent investment portfolios and risk management plans. A multi-objective optimisation approach is proposed using the root algorithm to create a multi-objective root system growth model based on many clusters. An investment risk management optimisation model based on root system growth is built into the study using distributed decision-making. To create a multi-objective root algorithm-based portfolio optimisation model, the Markowitz mean-variance model and a multi-objective root algorithm are employed. Accordingto the findings, the multi-group multi-objective root system method has a real Pareto frontier solution that is more accessible, has a faster convergence rate, and has lower fitness values. The root algorithm’s solutions are workable, and the final risk values of 0.0105, 0.0082, and 0.4623 for the 2, 4, and 6 investment objectives are all in the low-risk class range. The optimal set of solutions discovered by the algorithm had better distributivity and convergence. The Hypervolume values for the multi-group multi-objective root algorithm were 5.5298 and 3.9628 for the dual-objective portfolio and the tri-objective portfolio of investment return costs, respectively. The findings of this study can guide the development of portfolio and risk management strategies.

Good Islamic Boarding School Foundation Management as Risk Mitigation for Organizational Performance Management & GCG

The fundamentals of sound foundation management can assist businesses in reducing the risks associated with performance management. Principles like accountability, transparency, responsibility, fairness, and justice are all part of the GCG in practice. Businesses can lower the risk of fraud and data manipulation by putting GCG principles into practice and ensuring that the decision- making process is conducted in an open, responsible, and fair manner. Establishing a code of ethics and strong foundation management standards, as well as boosting the openness of financial reports, are some GCG measures that can aid in reducing the risks associated with organisational performance management. However, organisations must also take into account other elements including effective risk management, the application of ethical standards, and the creation of a strong organisational culture in order to mitigate the risks associated with organisational performance management. Good corporate governance is not the only solution to this problem. In actuality, including GCG into a comprehensive risk management plan can assist businesses in improving performance and gaining the confidence of stakeholders. Therefore, in order to enhance performance and reduce risks associated with financial, ethical, and governance difficulties, businesses must pay attention to GCG techniques while mitigating organisational performance management risks.

CRISIS COMMUNICATIONS STRATEGIES DURING WAR

The article substantiates the relevance of the research of crisis communication strategies, outlines possible ways of their organization, which will ensure stability and successful activity in an uncertain business environment. The peculiarities of communication during a crisis have been determined. Reasoned role and functions of internal communications. An example of a communication-oriented risk management plan is given. The characteristics of corporate messengers, which are used by a large number of companies of various sizes and, especially, during remote work, are given. It has been proven that a necessary aspect of successful management during a crisis is the presence of strong and affective leadership. It was emphasized that in order to improve the internal climate and communications in the team, it is advisable to appoint a specialist responsible for team spirit, who will distribute functions so that each team member realizes himself as a part of the whole, makes his important contribution, on which the final result of the project depends. Communications in crisis situations can both warn, mobilize help, reduce negative consequences, and spread panic, strengthen myths and misinformation, and divide society for years to come. It will depend on the correctness and clarity of establishing crisis communications, the choice of specific communication technologies and the communication strategy for exiting a specific crisis. For this, the authorities must develop a plan for managing crisis communications in conditions of uncertainty. Therefore, the crisis is a complex phenomenon that contains both potential and real threats and the danger of losses, as well as new opportunities for renewal and transformation in organizations, creating new modern approaches to interaction. Martial law can affect the quality of governance due to restrictions on freedom of action and decisions, extraordinary circumstances and instability of the political and economic situation

Risk Management Plan: Expert Review of Applications for Marketing Authorisation in the EAEU Depending on the Type of Medicinal Product (Review)

INTRODUCTION. A risk management plan (RMP) is an important document for assessing and managing risks when using a medicinal product. An RMP is provided as part of the marketing authorisation application for a medicinal product. The requirements for the need to submit an RMP and the information in its sections depend on the type of medicinal product and marketing authorisation procedure. Marketing authorisation holders will benefit from an expert systematisation of the requirements for RMP submission outlined in the Eurasian Economic Union (EAEU) Rules for Marketing Authorisation and Expert Assessment of Medicinal Products for Human Use and Rules of Good Pharmacovigilance Practice (Eurasian Economic Commission Council Decisions Nos. 78 and 87 dated 03.11.2016, respectively), as this systematisation will contribute to a more accurate drafting of RMPs.AIM. This study aimed to analyse the requirements for submitting parts and modules of an RMP depending on the type of medicinal product being authorised according to the EAEU marketing authorisation procedures.RESULTS. This review analyses and systematises the requirements for the development of an RMP and the submission/non-submission of its main parts (I–VI) and Part II modules (CI–CVIII) depending on the type of medicinal product and marketing authorisation procedure. The article describes the principles for providing information in three main RMP sections: Safety Specifications (Part II), Pharmacovigilance Plan (Part III), and Risk Minimisation Measures (Part V). Flow charts illustrate summarised information on pharmacovigilance activities and risk minimisation measures that is included in an RMP. The article systematises the considerations for the analysis and evaluation of information for the main parts (I–VI) and Part II modules (CI–CVIII) of an RMP.CONCLUSIONS. The systematisation of the requirements of Eurasian Economic Commission Council Decisions Nos. 78 and 87 dated 03.11.2016, as well as the expert comments on considerations for RMP submission depending on the type of medicinal product and marketing authorisation procedure will help to improve the quality of RMPs and, in general, the safety of medicinal products authorised in the EAEU.

Determination of the development of settlements above earthquake susceptibility classes in Atakum district (Samsun/Turkey)

It is not possible to predict and prevent earthquakes in advance. Until now only a few seconds of time can be saved with prediction studies. Therefore, the most logical solution to overcome earthquakes with the least damage is to implement risk management plans. One of the most important studies carried out within the scope of these plans is to determine the earthquake susceptibility of the regions and accordingly, to identify the suitable areas for new settlements. The purpose of the study is to evaluate the extent of earthquake susceptibility in Atakum district and analyse its impact on the developing urban area. To determine the susceptibility, Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHP) were used. For the application of the AHP method, 6 main geographical factors and 28 sub-factors including lithology, slope, distance to fault lines, landforms, maximum ground acceleration and soil permeability were analysed. The rate of weight was calculated for all factors and an earthquake susceptibility map was produced by weighted overlay. Then, the urban development process of Atakum district was determined with satellite images. In order to examine the development of the urban area on earthquake susceptibility classes in the last 23 years, Landsat 7 ETM for 2000 and Landsat 8 OLI/TIRS satellite images for 2013 and 2023 were used. According to the results obtained, the residential areas of Atakum city, especially on the coastline, in the embankment areas and on the alluvial plain floors, are located in the high and very high earthquake susceptibility area.

Defining Inheritance Risk Management as A New Concept Towards Sustainability of Family Businesses

This study investigates the relationship between success, Inheritance Risk Management (IRM), and sustainability in family businesses. This paper is based on a quantitative approach. The population includes family-owned businesses located in Abu Dhabi that have been in operation for at least two generations. The study sample consisted of family-owned business members. However, this paper focuses only on measuring variables and testing hypotheses for the proposed measurement. Due to the complexity of the model, Smart PLS-4 was used. Through an analysis of empirical data and theoretical frameworks, the research explores the direct and mediated relationships between these key variables. The findings underscore the critical role of success in driving effective risk management practices within family enterprises. Furthermore, the study highlights the significance of IRM in promoting the sustainability and resilience of family businesses over time. The results reveal a direct positive correlation between success and sustainability, emphasizing the importance of achieving and maintaining success for long-term viability. Additionally, the mediating role of IRM underscores its crucial contribution to the overall sustainability of family businesses. The study identifies various challenges in managing inheritance risks and ensuring the continuity of family enterprises, including maintaining family control, balancing family and operational responsibilities, succession planning, grooming successors, and respecting family elders. By addressing these challenges and leveraging IRM strategies, family businesses can navigate uncertainties and capitalize on opportunities, ensuring continued success across generations. These findings provide valuable insights for strategic decision-making and resource allocation in family enterprises, guiding efforts to enhance governance structures, succession planning, and risk mitigation practices. In addition, this research contributes to advancing our understanding of the complexities surrounding family businesses and underscores the importance of proactive risk management and strategic planning for long-term sustainability.

Assessing the applicability of rainfall index spatial interpolation in predicting landslide susceptibility: A study in Brunei Darussalam

Abstract Rainfall is crucial in meteorology and hydrology, significantly impacting landslide risk assessment. This study focused on evaluating rainfall’s role in determining landslide susceptibility in Brunei Darussalam’s Jalan Kota Batu-Subok and Jalan Jangsak-Tutong regions. Using monthly rainfall data (2008 – 2018) from four weather stations, three spatial interpolation methods inverse distance weighting (IDW), radial basis function (RBF), and global polynomial interpolation (GPI) were assessed. The RBF proved superior in predicting rainfall distribution, evidenced by lower error metrics and higher correlation coefficients. The landslide susceptibility index (LSI) derived from the RBF’s rainfall interpolation showed high accuracy in identifying landslide-prone areas, with success rates between 89 % and 94.3 %, and prediction rates from 85.2 % to 95.9 % across the two areas studied. These findings suggest that the RBF-derived LSI is a reliable tool for landslide risk assessment. However, the LSI’s stability, irrespective of the rainfall data or interpolation method used, indicates that factors like terrain and human activities might have a more significant impact on landslide risks than rainfall alone. This research highlights the importance of considering various factors in landslide risk management and land-use planning, offering valuable insights for policymakers and local authorities.

Fınancıal sustaınabılıty and rısk in antı-agıng startups

Abstract The anti-aging industry has experienced rapid growth driven by significant advancements in biotechnology, consumer interest in longevity, and substantial investment. This study explores the financial sustainability and risk management practices of anti-aging startups, highlighting key challenges and opportunities. Through case studies, the research examines funding strategies, regulatory hurdles, and market dynamics. Successful startups demonstrate robust financial planning, strategic partnerships, and innovative technological approaches, while unsuccessful ventures reveal the high risks associated with clinical development. The findings underscore the importance of comprehensive risk management and strategic financial planning in achieving long-term sustainability. This research provides valuable insights for entrepreneurs, investors, and policymakers aiming to navigate the complex landscape of the anti-aging market.

Artificial Intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability

This research paper delves into the transformative possibilities of Artificial Intelligence (AI) within corporate finance, specifically focusing on its role in improving efficiency and decision-making processes. Through the utilization of machine learning, natural language processing (NLP), and robotic process automation (RPA), AI introduces innovative methods for enhancing corporate governance and sustainability practices. In the contemporary business landscape, corporations encounter mounting pressure to streamline operations while simultaneously addressing concerns regarding environmental, social, and governance (ESG) issues. Conventional finance methodologies often struggle to efficiently handle large volumes of data and extract actionable insights promptly. However, AI presents a shift in paradigm by enabling automated data analysis, recognizing patterns, and conducting predictive modeling, thus enabling finance professionals to make data-informed decisions swiftly and accurately. Machine learning algorithms play a pivotal role in detecting patterns and correlations within financial data, facilitating proactive risk management and strategic planning. Additionally, NLP technologies facilitate the extraction of valuable insights from unstructured data sources like regulatory filings, news articles, and social media, thereby enabling informed decision-making in corporate governance and sustainability endeavors. Moreover, RPA simplifies repetitive tasks and workflows, thereby reducing operational expenses and freeing up human resources for more strategic pursuits. Through the automation of routine processes such as data entry, reconciliation, and reporting, RPA enhances operational efficiency and ensures adherence to regulatory standards. Through the adoption of AI technologies, corporations can unlock novel avenues for innovation, optimize resource allocation, and promote sustainable growth within today's dynamic business milieu.

Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human and natural ecosystems in the Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing and machine learning techniques is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). Vulnerability is particularly pronounced in low-elevation and low-slope southern downstream areas (Central part of Lao PDR). The results reveal that 36%–53% of the basin’s total area is highly susceptible to flooding, emphasizing the dire need for coordinated floodplain management strategies. This research uses freely accessible remote sensing data, addresses data scarcity in flood studies, and provides valuable insights for disaster risk management and sustainable planning in Lao PDR.

A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.