Resource Decisions Research Articles

Earth data assimilation in hydrologic models: recent advances

ABSTRACT Hydrologic model forecasts have inherent uncertainties from input errors, lack of physical representation, and parameter equifinality. Accurate modelling results with reduced uncertainty are necessary for water resources management and decision-making, especially in a changing climate scenario. To this end, the hydrologic modelling community widely accepts the assimilation of satellite remote sensing data. This paper reviews the recent developments in hydrologic data assimilation (DA) focusing on progress in the role of satellite remote sensing data in reducing model uncertainty.

Towards Intangible Freshwater Cultural Ecosystem Services: Informing Sustainable Water Resources Management

Rivers provide a range of Cultural Ecosystem Services (CES) such as aesthetic values, sense of place and inspiration, which remain insufficiently studied due to challenges associated with the assessment of their subjective and intangible attributes. However, the understanding of CES remains important as they are strongly linked to human wellbeing. This study utilizes a questionnaire-based survey to capture views from two villages along the mainstream of the Beas River in India, to identify the CES it provides, to assess how local communities appreciate their importance and how they relate to river flows. In total, 62 respondents were interviewed. Findings show that the Beas River provides several CES but among these, spiritual/religious ceremonies and rituals, aesthetic values and inspiration benefits were indicated as absolutely essential to the local communities. Results also demonstrate that people’s perception of the quality of CES is sometimes linked to river flows. It can be concluded that the Beas River is crucial in the functioning and livelihoods of local communities as it lies within the core of their cultural, religious and spiritual practices. This study reinforces the need to consider the full suite of ecosystem service categories in sustainable water resources development, planning and decision making.

The development and validation of a resource consumption score of an emergency department consultation

BackgroundEmergency Department (ED) visits and health care costs are increasing globally, but little is known about contributing factors of ED resource consumption. This study aims to analyse and to predict the total ED resource consumption out of the patient and consultation characteristics in order to execute performance analysis and evaluate quality improvements.MethodsCharacteristics of ED visits of a large Swiss university hospital were summarized according to acute patient condition factors (e.g. chief complaint, resuscitation bay use, vital parameter deviations), chronic patient conditions (e.g. age, comorbidities, drug intake), and contextual factors (e.g. night-time admission). Univariable and multivariable linear regression analyses were conducted with the total ED resource consumption as the dependent variable.ResultsIn total, 164,729 visits were included in the analysis. Physician resources accounted for the largest proportion (54.8%), followed by radiology (19.2%), and laboratory work-up (16.2%). In the multivariable final model, chief complaint had the highest impact on the total ED resource consumption, followed by resuscitation bay use and admission by ambulance. The impact of age group was small. The multivariable final model was validated (R2 of 0.54) and a scoring system was derived out of the predictors.ConclusionsMore than half of the variation in total ED resource consumption can be predicted by our suggested model in the internal validation, but further studies are needed for external validation. The score developed can be used to calculate benchmarks of an ED and provides leaders in emergency care with a tool that allows them to evaluate resource decisions and to estimate effects of organizational changes.

A review of 20-year applications of multi-attribute decision-making in environmental and water resources planning and management

Multi-attribute decision-making (MADM) is an umbrella term to describe a series of mathematical frameworks that, in essence, enable the decision-makers to evaluate the desirability of any feasible solution by considering a set of, often, conflicting evaluation criteria. Given their vast capabilities, the MADM methods are especially helpful for water resources planning and management. This review attempts to shed light on the roles of the MADM methods in the field of water resources decision-making during the last two decades. More than 320 scholarly published articles from 57 different countries were analyzed based on the geographic factors, the number of decision-makers involved, the type of MADM methods, the number of alternatives and criteria, the weight assignment methods, the viewpoints on uncertainty, and the subject of these studies. The results clearly revealed the ever-rising applications of the MADM methods for water resources management. It was also found that some regions, such as the Middle East, more frequently resort to these methods to address their water crisis problems. This review indicated that the analytical hierarchy process (AHP) was the most popular MADM method. Based on the reviewed studies and their results, three major recommendations can be made herein: (1) It is wise to address the uncertainty associated with the applications of the MADM methods in the field of water resources by conducting sensitivity analysis and/or resorting to probabilistic/fuzzy frameworks; (2) given the identified tendency toward subjective MADM, it is crucial to employ checking frameworks, such as the Delphi methods, to assess the consistency of the subjective evaluations; and (3) it is necessary to consider not only the technical criteria but the economic, environmental, legislative, and political evaluation criteria as well to account for the multidimensional nature of water resources problems.

Research note: Age, retirement, and intertemporal resource decision ability

AbstractPopulation studies show the importance of active aging for maintaining cognitive health, but much of the research has focused on episodic memory and verbal skills. Aging and ability to make rational intertemporal financial decisions is less understood, despite its critical role in consumer finance. This study describes the relationship between age, retirement, and intertemporal decision ability. A survey of adults examines the interplay of aging and retirement related to completing simple calculations with time trade‐offs in benefits. A negative association between intertemporal skills and age is indicated, with a sharp downward inflection around age 66. Regression analysis results in a significant, negative retirement‐age interaction term. This research has implications for policy and consumer decision‐making. The negative relationship of age and retirement status may suggest limits to career extension, or alternatively, that extending some types of careers could provide cognitive benefits. Further research is needed to understand the direction of causality.

Mandarin version of dementia and driving decision aid (DDDA): Development and stakeholder evaluation in Taiwan

Dementia causes cognitive and memory difficulties which can reduce the driving safety of the individuals. The decision-making process for driving retirement is challenging, and yet limited guidance is available. This article reports the development of the Taiwanese version of dementia and driving decision aid (DDDA) and the evaluation from stakeholders through a dementia and driving education programme. A multi-method approach was adopted using a pre-test, post-test survey and focus group interviews. A total of 154 healthcare professionals, family caregivers and people with dementia participated education programme, and 12 experts attended the focus group discussion. The survey included demographics, knowledge, confidence, competence and awareness of using DDDA. Participants completed a survey prior and immediately after the education programme. We translated a 32-page interactive DDDA booklet from the original English version to Mandarin. The education programme consisted of three-hour dementia and driving education module delivered both face-to-face and online. The majority of participants described the booklet as balanced (91.7%) with the information presented in a 'good' or 'excellent' manner (93.4%). Most participants (85.3%) felt that DDDA helps them in making decisions about driving. Five themes were extracted from the focus group interview: (1) approach targeted to people with dementia, (2) specific content and additional information, (3) culturally appropriate modification, (4) having the right to drive and (5) booklet dissemination. The knowledge, confidence, competence and awareness of using the DDDA increased significantly (p<0.001) after the education programmes. We anticipate that use of the DDDA booklet will raise awareness of this social and health issue among the general public and facilitate collaborations with clinicians, municipalities and related organisations in providing a decision-making resource material for those with people living with dementia and their families. This study was not a clinical trial and the focus of this study was development and evaluation of the DDDA booklet. As mentioned in the methods section, participants were invited to attend the education program and provided their thoughts on the DDDA booklet based on their satisfaction level. Moreover, the education program was a one-day, workshop type program. This study was neither "prospectively assigns human participants or groups of humans to one or more health-related interventions" nor "to evaluate the effects on health outcomes", according to the definition of clinical trial by WHO. Therefore, we did not consider this study was a clinical trial. There is an urgent need for supporting people with dementia and their families to negotiate the complex decision-making involved in deciding to change their approach to driving. The DDDA booklet can fill an important gap in service delivery to people with dementia who are adjusting to life without driving.

Tracing the global tin flow network: highly concentrated production and consumption

Tin is closely associated with daily life and scientific and technological development, being widely applied in electronic products (e.g., computers, mobile phones) and high-end manufacturing, but the sustainability of tin resources has aroused global concern. To understand the global tin trade and its implications, a material flow analysis framework is established in this study, to analyze the tin flow among countries during 1999–2018. Tin production is concentrated in nine countries, which account for more than 90% of global tin production; while tin consumption is concentrated in ten countries, which account for some 80% of total tin consumption. In addition, the level of economic development measured in GDP/capita, is found to be highly correlated with the role of a country in the global tin trade. The countries with low per capita GDP are usually principal tin producers and suppliers; while eight of the world's top ten refined tin consumers (accounting for about 70% of global consumption) are those with high per capita GDP. In addition, a significant correlation between refined tin consumption and GDP per capita is observed in developing countries, but this correlation weakens in developed countries. This study not only fills the data gap in global tin flow but also provides a reference and basis for tin resource decision making and for follow-up research on tin material flow.

Spatial variability and risk assessment of groundwater pollution in El-Outaya region, Algeria

Groundwater contaminations’ spatial distribution and risk assessment are among the most important issues for human consumption, water resources managers and decision makers. For this purpose, stochastic and deterministic methods are employed in this paper based on the empirical Bayesian Kriging (EBK), ordinary kriging (OK), and radial basis function (RBF) with multi-quadratic function, which help to predict the spatial distribution and risk assessments. These methods are based on the contamination data from 68 wells in El-Outaya plain (Biskra, southeastern Algeria. Nitrate-nitrogen (NO3–N) is the most important contamination in this region. Among the methods indicator Kriging (IK) is used to elaborate probability maps based on three thresholds, namely, [NO3–N] > 3 mg/l, [NO3–N] > 8.9 mg/l and [NO3–N] > 11.3 mg/l. The probability maps are then transformed to categories using binary maps of two of exceeding thresholds levels as (P ≥ 0.5 and P ≥ 0.9). The determination coefficient (R2), the root mean square error (RMSE) and the average standard error (ASE) of the obtained raster maps are used to compare the different interpolation methods and to select the best one. The results show that all the used interpolation techniques have accurately predicted the spatial distribution of [NO3–N], which provides opportunity to predict the contamination values even at unsampled locations in the region. The raster maps indicate the same overall spatial distribution of groundwater quality categories and their measurement errors indicate the superiority of RBF over EBK and OK. RBF is an accurate interpolation technique to identify groundwater pollution in El-Outaya plain. The transformation of probability maps to categorical maps based on IK is important for decision makers to identify regions with high nitrate-N contamination probability and high risk to human health.

Digital forest information platform as service innovation: Finnish Metsaan.fi service use, users and utilisation

Abundance of information characterises decision-making about forest use. This challenges forest owners. Digitalisation and diverse e-services may enhance the delivery of forest resource information and decision support. However, little is known on how e-services engage forest owners. This study explores the Finnish state-funded Metsaan.fi e-service portal as a service innovation. We use web-based survey data about forest owners' views on aforementioned e-service (N = 5170) and register data about Finnish forest owners. The identified factors explain the respondents' activity in using the service. Theories of innovation diffusion and e-service quality framed the content analysis of the open-ended questions of the survey. According to the quantitative analysis, owners with timber production objectives and multi-objective owners were the most active users. Forest owners who were compliant with the service's recommendations of silviculture and harvesting operations, used the service actively. This implies that these forest owners had found the service useful. The qualitative analysis shows that independence from time and place and the ease-of-use make the service more inviting. The lack of forest inventory data or its perceived low quality detract forest owners from using it. Many forest owners expect the service to replace forest management plans as decision support tool but the information content there does not fully fulfil those expectations. The study produced knowledge for developing further the online platform and its services. Furthermore, it created understanding about e-government services in the management of natural resources. Specifically, the results argue for noticing attitudinal patterns of intended users when designing governmental e-services. Further research is suggested to integrate innovation adoption theory and service-research theory to dig deeper into the value creation and service needs of different user groups.

Skill assessment of TIGGE precipitation forecasts in the four basins of Iran

In atmospheric sciences, forecast assessment, the process of examining the quality of forecasts, is very important. For this reason, we carried out a comprehensive evaluation of the most important global numerical weather forecast (NWP) models for the Iranian basins of Karkheh, Karun, Urmia, and Sefidrood. This study aims to evaluate the output of six different NWP models from the TIGGE database in important basins of Iran, to determine the most appropriate model for each basin. The assessment period took place between 2008 and 2018 and evaluated probabilistic and non-probabilistic methods. In the Karun Basin, the global NWP models were shown to perform better than in the other three basins. Overall, the UKMO (United Kingdom Met Office) and ECMWF (European Medium-Range Weather Forecast) performed best, while the Korean Meteorological Administration (KMA) showed the poorest performance among the models in the four basins. In the 25 mm threshold, most models underestimated precipitation events. The ECMWF model showed reliable and sharp forecasts and was better able to discriminate between low, medium, and high precipitation events compared to other models. Compared to those of other models, the UKMO model demonstrated greater brier skill scores. In general, the ECMWF, UKMO, ECCC (Environment and Climate Change Canada), and National Center for Environmental Forecast (NCEP) models performed relatively satisfactory in all four basins, although they forecasted better in basins with higher mean precipitation. These models are recommended for flood warning systems, and water resource decision making in Iran. However, all forecasts will need post-processing for operational applications.

AI-Driven Collaborative Resource Allocation for Task Execution in 6G-Enabled Massive IoT

In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

A comparative study to assess surface water's anthropic and natural vulnerability using the DKPR and RUSLE approaches. Case of the watershed of the Joumouaa dam (Coastal basin of Ghiss, North East Morocco)

The salinization of surface water in a coastal context leads to a qualitative degradation of this resource by various sources of anthropogenic and natural pollution. In this context, we present the results of a comparative study using "DKPR" and "RUSLE" models to evaluate the degree of surface water vulnerability against pollution, especially in the sub-watershed of the Joumouaa dam, a hydraulic infrastructure providing drinking water for the Targuist city. The "DKPR" model adopted as a qualitative approach involves four parameters: Accessibility of the aquatic environment (D), Water functioning of the soil and subsoil (K), Physiography watershed (P), Rainfall erosivity (R). The final result is a resource vulnerability map obtained by combining index maps of these four parameters using remote sensing and GIS. The "RUSLE" model applied as a quantitative approach integrates five factors in a multiplying function, namely: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and soil conservation practices (P) in a remote sensing and GIS environment. The analysis of the final vulnerability maps of the approaches mentioned above will be helpful support for water resource managers and decision-makers better identify areas of high risk and their protection.

Using Machine Learning to Predict Outcomes of Accident Cases in Moroccan Courts

Intelligent prediction systems help a lot in decision-making. Various studies have dealt with the application of these systems in the field of justice. In Morocco, the courts make many judgments every day. These judgments, once processed, can be very useful for judges, lawyers, and citizens. In this study, we focus on accident cases. We collected a dataset that comprises 514 legal judgments from the Errachidia Court in Morocco. Next, this dataset has been processed in order to extract important characteristics that can further control the outcomes of such a type of case. We believe that the compiled dataset is a valuable resource for decision-making. Later, different classification algorithms, such as linear regression, vector support machines, and decision trees, will be applied to the compiled dataset and general conclusions on the performances will be drawn.

Solar Energy in Utility Integrated Resource Plans: Factors That Can Impact Customer Clean Energy Goals

Many electric utilities utilize Integrated Resource Plans (IRPs) to develop and communicate a long-term vision for their resource development. As such, IRPs play a significant role in solar development and in how customers achieve their clean energy goals. For large-scale energy customers, including corporations and local governments, understanding how IRP processes impact resource decisions—and how this relates to achieving their clean energy targets—can influence their engagement with utilities and regulators. A range of barriers can limit solar energy in IRPs, including outdated or unfounded solar technology assumptions and modeling practices that do not enable solar to compete fairly with other resources in the process. These barriers can reduce the amount of solar in the grid mix or available to customers through utility programs, impacting the ability of customers to meet their clean energy targets. This working paper aims to raise awareness among large-scale customers, utilities, and regulators of some of the current barriers that limit solar energy in utility IRPs.

Business Analysis on Sustainable Competitive Advantages

With the rapid economic growth and globalization, sustainability begins to play a more and more important role in the strategic management of a company. This paper explains what the sustainability competitive advantages are, and what factors are related to sustainability. It also briefly introduces the sustainable competitive advantages from institutional and resource-based views. In addition, this paper presents that sustainable competitive advantages largely depend on the company’s capability to manage the institutional context of resource decision. Sustainable competitive advantages are not only related to internal context, but also related to external environment. This paper explains the link of sustainable competitive advantages in the internal and external environment. In conclusion, it presents the necessary understanding for entrepreneurs to concentrate on factors which influence corporate sustainability beyond numbers in the financial analysis.

Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness

This study aims to evaluate the impact of artificial intelligence (AI) on the efficiency and fairness of human resources (HR) decision-making. The key goals are to determine how artificial intelligence improves decision-making efficiency, investigate the fairness issues involved in AI-driven human resource practices, and make policy suggestions for engaging in ethical HR practices. The approach utilized is known as secondary data analysis. It is used to synthesize insights and patterns by pulling upon previously published literature and empirical investigations; even though artificial intelligence technologies present an opportunity to optimize human resource operations and improve organizational performance, significant findings demonstrate that these technologies also create ethical problems connected to algorithmic biases and an absence of transparency. Regulatory oversight, ethical standards, data governance, diversity and inclusion programs, and constant monitoring and assessment are some of the policy implications that should be considered to guarantee responsible deployment of artificial intelligence in human resource contexts. When it comes to human resource decision-making, companies can embrace the revolutionary potential of artificial intelligence (AI) while maintaining ethical standards if they prioritize justice, openness, and accountability.

An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

In South Korea, the risk of debris-flow is relatively high due to the country's vast mountainous topographical features and intense continuous rainfall during the summer. Debris-flows can result in the loss of human life and severe property damage, which can be made worse due to the poor spatiotemporal predictability of such hazards. Therefore, it is essential to research the preemptive prediction and mitigation of debris-flow hazards. For this purpose, this study developed an ANN model to predict the debris-flow volume based on 63 historical events. By considering the morphology, rainfall, and geology characteristics of the studied area in central South Korea, the data of 15 debris-flow predisposing factors were obtained. Among these data, four predisposing factors (watershed area, channel length, watershed relief, and rainfall data) were selected based on Pearson's correlation analysis to check for significant correlations with the debris-flow volume. To determine the best performing ANN model, a validation testing was carried out involving ten-fold cross-validation with MSE and R2 using both training and validation datasets, which were randomly split into a 7:3 ratio. The model performance validation results showed that an ANN model with two hidden neurons (4×2×1 architecture) had the highest R2 value (0.828) and the lowest MSE (0.022). In addition, in a comparative study with other existing regression models, the ANN model showed better results in terms of adjusted R2 value (0.911) using all datasets. Furthermore, 94% of the observed debris-flow volumes from the ANN model were within 1:2 and 2:1 lines of the predicted volumes. The results of this study have shown the potentiality of the developed ANN model to be a useful resource for decision-making and designing barriers in areas prone to debris-flows in South Korea.

A Multiple Stakeholder Perspective for Evaluating Community-Based Dementia Care

For holistic interventions and research on dementia, it is fundamental to understand care experiences from the perspectives of carers, care recipients, and care professionals. While research on care dyads and triads have highlighted the effects of communication and interactional aspects on care relationships, there is a lack of knowledge on how individual-contextual and relational factors shape the provision and receipt of care in terms of decision-making processes, resource allocation, and expectations of care outcomes. Thus, this paper sheds light on (i) how carers negotiate care provision with other important life domains such as employment, household/family roles and conflicts, as well as their own health problems, life goals, values, and aspirations for ageing; (ii) how older adults with dementia perceive support and those who provide it; (iii) the structural constraints faced by care professionals in delivering a team-based mode of dementia care; and, taken together, (iv) how community-based dementia care is impeded by barriers at the individual, relational, and institutional levels. Findings were derived from semi-structured interviews and observational data from fieldwork conducted with 20 persons with dementia (median age = 82), 20 of their carers (median age = 60), and 4 professional care providers. All respondents were clients and staff of a multidisciplinary and community-based dementia care system in Singapore. Our analysis indicates the impact of dementia care is strongly mediated by the interplay between institutional/familial contexts of care provision and the various ‘orientations’ to cognitive impairment and seeking support, which we characterised as ‘denial/acceptance’, ‘obligated’, ‘overprotective’, and ‘precariously vulnerable’.

Modelling plant health for policy.

Plant health is relatively poorly funded compared with animal and human health issues. However, we contend it is at least as complex and likely more so given the number of pests and hosts and that outbreaks occur in poorly monitored open systems. Modelling is often suggested as a method to better consider the threats to plant health to aid resource and time poor decision makers in their prioritisation of responses. However, like other areas of science, the modelling community has not always provided accessible and relevant solutions. We describe some potential solutions to developing plant health models in conjunction with decision makers based upon a recent example and illustrate how an increased emphasis on plant health is slowly expanding the potential role of modelling in decision making. We place the research in the Credibility, Relevance and Legitimacy (CRELE) framework and discuss the implications for future developments in co-construction of policy-linked models.