Article
Computer Science, Information Systems
Marc Serramia, Maite Lopez-Sanchez, Stefano Moretti, Juan A. Rodriguez-Aguilar
Summary: Decision makers face challenges in comparing and ranking elements based on multiple criteria and personal preferences. This study introduces a new decision-making framework and presents a new method for ranking single elements. It is also proven that the contributions of this study generalize recent results in the field of social choice. The findings are illustrated through a case study on ethical decision-making.
INFORMATION SCIENCES
(2023)
Article
Health Care Sciences & Services
Daniele Piovani, Rozeta Sokou, Andreas G. Tsantes, Alfonso Stefano Vitello, Stefanos Bonovas
Summary: A large number of prediction models are published to support personalized decision making in diagnosis or prognosis. Conventional statistical measures are not suitable for assessing the clinical value of scores or biomarkers. Decision curve analysis is a popular technique used to assess the clinical utility of a prognostic or diagnostic score/rule, or a biomarker. It is a powerful tool for judging whether newly published or existing scores may truly benefit patients, representing a significant advancement in improving transparent clinical decision making.
Review
Construction & Building Technology
Surayyn Uthaya Selvan, Soultana Tanya Saroglou, Jens Joschinski, Mariasole Calbi, Verena Vogler, Shany Barath, Yasha Jacob Grobman
Summary: Rapid urbanization has negative impacts on the built and biotic environment, requiring interdisciplinary mitigation strategies. Current nature-based solutions integrated into building envelope design have proven beneficial, but they often overlook the potential to support other living organisms. A multi-species approach is envisioned to facilitate more holistic envelope-design solutions.
BUILDING AND ENVIRONMENT
(2023)
Article
Public, Environmental & Occupational Health
Kara Morgan, Zachary A. Collier, Elisabeth Gilmore, Ketra Schmitt
Summary: Emerging risks are characterized by a lack of data, rapidly changing information, and the absence of existing predictive models. Effective decision-making for these risks requires scoping the decision context and shared responsibility between analysts and decision-makers in rapidly evolving situations. Simplified analytical approaches may be more suitable for emerging risks, providing increased transparency, ease of explanation, and the ability to conduct new analyses quickly. Continued dialogue and discussion among decision and risk analysis communities can enhance the credibility and usefulness of models for emerging risks.
Article
Biodiversity Conservation
Rita Beigaite, Hui Tang, Anders Bryn, Olav Skarpaas, Frode Stordal, Jarle W. Bjerke, Indre Zliobaite
Summary: This study uses machine learning to analyze the relationship between vegetation and climatic characteristics. It finds that climate extremes can more accurately describe the distribution of vegetation types and their eco-climatological space compared to averaged climate variables. By combining climate extremes and averaged climate variables, the predictions of future vegetation changes are less prominent but in better agreement with dynamic global vegetation models. The study highlights the importance of considering climate extremes in determining the geographic distributions of different vegetation types.
GLOBAL CHANGE BIOLOGY
(2022)
Article
Plant Sciences
Qiuran Wang, Silvia Guerra, Bianca Bonato, Valentina Simonetti, Maria Bulgheroni, Umberto Castiello
Summary: Finding a suitable support is crucial for climbing plants, as it affects their performance and fitness. Previous studies have focused on the mechanistic details of support-searching and attachment, while fewer have considered the ecological significance and influencing factors. This study investigates the influence of support diameter on pea plants' movement and reveals a preference for thinner supports. These findings shed further light on how climbing plants make decisions regarding support-searching and demonstrate their ability to adapt to environmental scenarios.
Review
Pharmacology & Pharmacy
Elodie Baumfeld Andre, Nate Carrington, Flora S. Siami, Jo Carol Hiatt, Carly McWilliams, Carolyn Hiller, Andy Surinach, Alejandro Zamorano, Chris L. Pashos, Wade L. Schulz
Summary: Real-world data and real-world evidence play a crucial role in regulatory decision making in healthcare. However, there are challenges in applying them to in vitro diagnostic products and clinical decision support systems. This review examines current regulatory practices in IVD product development and discusses the use of CDSS in analyzing patient data to support complex decisions. It also explores how RWD can enhance regulatory understanding and improve the safety, quality, and efficiency of healthcare practices.
CLINICAL PHARMACOLOGY & THERAPEUTICS
(2022)
Article
Public, Environmental & Occupational Health
Jeffrey M. Keisler, Igor Linkov
Summary: Recent guidelines for risk-informed decision making provide a standard for incorporating probabilistic risk models with other considerations, but quantifying risk is difficult when threats, vulnerabilities, and consequences are highly uncertain. Decision making informed by risk (DMIR) can be used as a flexible approach that combines risk and decision analytics. Multi-criteria decision analysis (MCDA) is commonly used as a basis for DMIR to accommodate varying levels of analytical detail.
Article
Computer Science, Information Systems
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Enrique Herrera-Viedma
Summary: This work presents a novel consensus-based decision model that utilizes a dynamic group decision-based feedback mechanism and operational tools for Z-numbers to aggregate assessments of multiple groups of decision makers. The adaptive level of consensus is determined through graph analytics, and a fusion model is constructed to aggregate the assessments using an optimal and dynamic weight-assigning mechanism.
INFORMATION SCIENCES
(2022)
Article
Economics
Paavo Jarvensivu, Helmi Raisanen, Janne I. Hukkinen
Summary: Urban policymakers in the 2020s are facing wicked socio-ecological disruptions and must make decisions under deep uncertainty. The Policy Operations Room (POR) simulation exercise provides a unique way for policymakers and researchers to practice decision-making and critically reflect on science-based scenarios. Social-political complexity in scenarios and practitioner involvement in the design process are areas that deserve further attention.
Article
Mathematics
Gerardo Minguela-Castro, Ruben Heradio, Carlos Cerrada
Summary: The paper presents an innovative high-level decision-making model for studying battle casualties, with empirical evidence from the Battle of Crete. The model uses an adaptive and predictive control architecture for high-level command decision support.
Article
Clinical Neurology
Christos Lazaridis, Ali Mansour, Manasvini Singh
Summary: DC is effective in relieving intracranial hypertension, but controversy exists regarding patient selection, intracranial pressure threshold, timing, and long-term functional outcomes. Recommendations based on DECRA and RESCUEicp trials have updated guidelines, but personalized decision-making requires consideration of individual patient preferences through shared decision-making.
WORLD NEUROSURGERY
(2022)
Article
Computer Science, Artificial Intelligence
Yuzhu Wu, Yuan Gao, Bowen Zhang, Witold Pedrycz
Summary: This paper proposes a minimum information-loss transformation framework to support the useful fusion of heterogeneous distributed information in linguistic group decision making. By defining distributed linguistic distance measurements, the information loss among heterogeneous distributed linguistic preference information can be measured, and several minimum information-loss transformation models are proposed. The flexibility of distributed linguistic information is studied through numerical examples and comparative analyses to justify the effectiveness of the proposed models.
INFORMATION FUSION
(2023)
Article
Meteorology & Atmospheric Sciences
Kripa Jagannathan, Andrew D. Jones, Isha Ray
Summary: This paper examines negotiations and outcomes from Project Hyperion, where scientists and water managers jointly developed decision-relevant climatic metrics, highlighting the successful co-production strategies required for effective interaction between scientists and managers. The study identifies four indirect methods for extracting tacitly held knowledge and enabling shared learning, ultimately providing insights into advancing adaptation-relevant climate science in the water sector.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2021)
Article
Chemistry, Analytical
Ming Ye, Lei Pu, Pan Li, Xiangwei Lu, Yonggang Liu
Summary: In recent years, autonomous driving technology has shifted towards vehicle adapting to humans. A personalized lane change decision model based on time-series data is proposed to improve the adaptability of autonomous driving systems. By identifying driving styles and considering interaction between vehicles, the model accurately predicts lane change behavior and enables personalized driving.
Article
Engineering, Environmental
Rebecca Smith, Edith Zagona, Joseph Kasprzyk, Nathan Bonham, Elliot Alexander, Alan Butler, James Prairie, Carly Jerla
Summary: Deep uncertainty refers to planning contexts where likelihood of future conditions cannot be determined, conflicting objectives and unpredictable outcomes exist. Evidence of its relevance in the Colorado River Basin is seen in severe and unexpected drought, diverse stakeholders and viewpoints. Decision Making under Deep Uncertainty (DMDU) aims to address these challenges. Reclamation has explored DMDU since 2012, using adaptation, vulnerability and robustness concepts to design strategies for supply-demand imbalance. This article presents the basis for continued exploration of DMDU techniques and how ongoing studies can contribute to future planning efforts in the Colorado River Basin.
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Nathan Bonham, Joseph Kasprzyk, Edith Zagona
Summary: This paper introduces a decision-support framework called post-MORDM, which addresses the challenges of generating a large number of policies and disagreements among decision-makers in Many Objective Robust Decision Making (MORDM). It uses the Self-Organizing Map (SOM) technology to cluster policies, discover salient characteristics, and assess cause-effect relationships, aiming to create a structured platform that encourages negotiation and compromise.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Multidisciplinary Sciences
Fangfang Yao, Ben Livneh, Balaji Rajagopalan, Jida Wang, Jean-Francois Cretaux, Yoshihide Wada, Muriel Berge-Nguyen
Summary: This study finds that over the past few decades, approximately 53% of the largest 1,972 global lakes have experienced significant declines in water volume. The volume loss in natural lakes is primarily attributed to climate warming, increasing evaporative demand, and human water consumption, while sedimentation dominates storage losses in reservoirs. It is estimated that around one-quarter of the world's population resides in a basin of a drying lake, emphasizing the importance of incorporating climate change and sedimentation impacts into sustainable water resources management.
Article
Environmental Sciences
Kaitlyn Bishay, Nels R. R. Bjarke, Parthkumar Modi, Justin M. M. Pflug, Ben Livneh
Summary: Understanding the relationship between remotely sensed snow disappearance and seasonal water supply is important for supplementing limited ground based measurements in a changing climate. A study investigated this relationship for 15 snow dominated basins across the western U.S. using satellite-derived Day of Snow Disappearance (DSD) and April-July total streamflow volume. The study found a significant relationship between DSD and water supply, with satellite-based models showing better prediction skill than in-situ-based models.
Article
Environmental Sciences
Katherine E. Hale, Keith S. Jennings, Keith N. Musselman, Ben Livneh, Noah P. Molotch
Summary: Mountain snowpacks serve as natural water storage, but the Snow Storage Index (SSI) has shown a decrease in western North America since 1950 due to earlier snowmelt, spring rains, and reduced winter precipitation. The SSI measures the delay in water input from the timing of a melting snowpack, offering insights into hydrologic sensitivity to climate change and its implications for water resources and ecosystems.
COMMUNICATIONS EARTH & ENVIRONMENT
(2023)
Article
Geosciences, Multidisciplinary
Fangfang Yao, J. Toby Minear, Balaji Rajagopalan, Chao Wang, Kehan Yang, Ben Livneh
Summary: In nearly all reservoirs, storage capacity is lost due to sediment accumulation, and the sedimentation rates are poorly understood. In this study, a novel approach is proposed to estimate reservoir sedimentation rates and storage capacity losses using satellite images and water level data. The approach is validated on eight reservoirs in the United States and shows good accuracy in estimating the bathymetry and sedimentation rates.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Editorial Material
Engineering, Civil
Joseph Kasprzyk, Margaret Garcia
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)
Article
Environmental Sciences
K. E. Hale, K. N. Musselman, A. J. Newman, B. Livneh, N. P. Molotch
Summary: In the mountainous western United States, a warming climate is causing a decrease in the fraction of precipitation falling as snow and a shift in the timing of snowmelt, leading to uncertain impacts on the distribution of water between evapotranspiration and streamflow. By using a Snow Storage Index (SSI) and a Budyko-based framework, this study found that greater snow water storage was associated with greater hydrologic partitioning to streamflow in several mountainous areas. The retention and release of stored snow water during the summer months plays a significant role in water distribution. If SSI decreases with future warming, it will have substantial implications for ecosystems and water supplies in the western U.S.
WATER RESOURCES RESEARCH
(2023)
Article
Multidisciplinary Sciences
Nels Bjarke, Joseph Barsugli, Ben Livneh
Summary: Assessing changes in future aridity requires an understanding of variations in the atmospheric demand for water. Here we describe the development and validation of a dataset of global monthly estimates of ET0, ETP, and vapor pressure deficit from CMIP6 projections. Overall, evaporative demand is projected to increase across all emissions scenarios, with the largest increases over polar regions and a larger contribution from advection in regions with higher baseline ET0.
Article
Geosciences, Multidisciplinary
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, Ben Livneh
Summary: Thresholds can be useful for interpreting environmental data, but they may vary when applied to different datasets or time periods. This study examines the impact of different spatial discretizations of snow on estimates of wolverine denning opportunities. The results show that snow thresholds are important but may not capture the full variability in snow-adapted wildlife denning opportunities.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, Kristy F. Tiampo
Summary: Wildfires change hydrologic and geomorphic response, leading to additional hazards and challenges. This study evaluates the trigger characteristics of post-wildfire mass movement by comparing precipitation before events in burned and unburned locations. The results show that mass movements in burned sites are preceded by less precipitation, supporting the hypothesis that fire increases rainfall-driven mass movement hazards. Additionally, there are differences in the seasonality of mass movements between burned and unburned locations.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2023)
Article
Environmental Sciences
Melanie Holland, Ben Livneh, Evan Thomas
Summary: This study develops a groundwater abstraction forecast model using in situ groundwater abstraction data, hydrologic, and climatic data. Artificial neural network models outperform other model algorithms and can reliably predict groundwater abstraction. The model performs best on a monthly time step and forecasts are reliable within a two-month lead time. Regional heterogeneity may introduce error into the model.
Article
Geosciences, Multidisciplinary
Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, Toby Minear
Summary: Accurate representation of channel properties is crucial for forecasting in hydrologic models. However, there is considerable uncertainty in the parameterization of channel geometry and hydraulic roughness in the NOAA National Water Model due to data scarcity. This study aims to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry dataset.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Jacob Kravits, Joseph R. Kasprzyk, Kyri Baker, Ashlynn S. Stillwell
Summary: This paper presents a novel formulation of the optimal power flow problem, aiming to minimize cost, water withdrawal, and water consumption. The formulation is applied in a realistic case study using global mapping and ranking sensitivity analyses, providing insights into vulnerabilities and potential issues in power systems.
ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY
(2022)