Environmental Decision Research Articles

Enhancing AI-CDSS with U-AnoGAN: Tackling data imbalance

Background and ObjectiveClinical Decision Support Systems (CDSS) have substantially evolved, aiding healthcare professionals in informed patient care decision-making. The integration of AI, encompassing machine learning and natural language processing, has notably enhanced the capabilities of CDSS. However, a significant challenge remains in addressing data imbalance and the black box nature of AI algorithms, particularly for rare diseases or underrepresented demographic groups. This study aims to propose a model, U-AnoGAN, designed to overcome these hurdles and augment the diagnostic accuracy of AI-integrated CDSS. MethodsThe U-AnoGAN was trained using masks derived from normal data, focusing on the Covid-19 and pneumonia datasets. Anomaly scores were calculated to assess the model's performance compared to existing AnoGAN-related algorithms. The study also evaluated the model's interpretability through the visualization of abnormal regions. ResultsThe results indicated that U-AnoGAN surpassed its counterparts in performance and interpretability. It effectively addressed the data imbalance problem by necessitating only normal data and showcased enhanced diagnostic accuracy. Precision, sensitivity, and specificity values reflected U-AnoGAN's superior capability in accurate disease prediction, diagnosis, treatment recommendations, and adverse event detection. ConclusionsU-AnoGAN significantly bolsters the predictive power of AI-integrated CDSS, enabling more precise and timely diagnoses while providing better visualization to potentially overcome the black box problem. This model presents tremendous potential in elevating patient care with advanced AI tools and fostering more accurate and effective decision-making in healthcare environments. As the healthcare sector grapples with escalating data complexity and volume, the importance of models like U-AnoGAN in enhancing CDSS cannot be overstated.

Surface Measure to Depth (SMeTD): a new low-budget system for 3D water temperature measurements for combining with UAV-based thermal infrared imagery

Characterising spatial patterns in water temperature is important for monitoring aquatic habitats and understanding physical and biogeochemical processes to support environmental management decisions. As freshwater bodies exhibit high spatial and temporal variability, high-resolution 3D temperature data are essential to understand local anomalies. The acquisition of simultaneously high spatial and temporal datasets in the field has so far been limited by costs and/or workload associated with commonly used monitoring systems.We present a new, low-cost, spatially and temporally flexible 3D water temperature monitoring system, Surface Measures to Depth (SMeTD). SMeTD can be used to provide information on the relation of water surface temperature to changes with depth, characterise water temperature in 3D and ground truth remotely sensed thermal infrared data. The systems performance was tested under laboratory conditions and under controlled conditions in the field. This revealed an accuracy comparable to established but more expensive monitoring systems. Field testing of SMeTD involved 1-min data collection of 3D water temperature for a full diurnal cycle in a lake. The 3D temperature patterns were supported by a thermal infrared image of the lakes surface. The field dataset demonstrated higher water temperatures and higher water temperature variation at the surface compared to deeper layers. SMeTD can be used to observe a broad range of hydrological processes in natural and artificial aquatic environments and help to understand processes involved with energy budgets, infiltration, limnology, or groundwater surface water exchange.

The importance of recognizing Buffer Zones to lands being developed, restored, or remediated: on planning for protection of ecological resources

ABSTRACT Environmental management relies on many types of information before making decisions regarding remediation, restoration, or other land use decisions, including ecological data, such as risks to species, populations, communities, and ecosystems. The aim of this investigation was to describe the ecological information required within the context of making environmental decisions and providing visual communication tools for regulators, conservationists, and the public to understand the risk to ecological resources on- and off-site. It is suggested that ecological information used in environmental decisions is required to be transparent throughout the planning and execution of a project, which needs to include: 1) ecological information and evaluations within development areas or units (in this case, watersheds), and 2) resources in adjacent areas (Buffer Zones) that might be affected. The Melton Valley administrative watershed (Oak Ridge Reservation, TN) is used as a case study because this site still has active facility development and environmental remediation, and there are important ecological resources on and off-site. Data indicate that although there are important resources on Melton Valley administrative watershed, there are also significant resources in the Buffer Zone around the watershed. Compared to the Melton Valley administrative watershed, the Buffer Zone contains more Interior (and Buffer) Forest and greater value resources. The point is made that when remediation, restoration, or development occurs, it is equally important to consider resources that are adjacent to the site in a Buffer Zone, particularly when remediation and development might continue for many years or decades.

Landslide susceptibility mapping for the Red Sea Mountains: A multi-criteria decision analysis approach

The Earth's climate system is clearly warming. Unquestionably, climate change has an impact on landslides by affecting the stability of both naturally occurring and artificially constructed slopes. Less certain are the type, extent, degree, and direction of the changes in stability conditions as well as the location, abundance, activity, and frequency of landslides in response to the expected climate changes. Climate change threatens global ecosystems, causing severe human and economic losses through sea level rise, hurricanes, flooding, landslides, and heat waves. Landslides in high mountains could become more common as a result of these changes. The high-speed electric train project in Egypt seeks to provide sustainable transportation, lessen climate change, and enhance public health. The study area was conducted along a 70-km track in the Eastern Desert, Egypt, across steep and rugged basement rocks of the Red Sea mountain chain (RSM). The geographic pinpointing of landslide susceptibility zones plays a significant role in promoting hazards mitigation in mountainous areas, such as RSM. The aim of this research project is to create a landslide susceptibility map (LSM) utilizing multi-criteria decision analysis (MCDA) in the region using information value (IV) and analytical hierarchy process (AHP) approaches that encompass the third proposed track of the high-speed electric railroad Qena-Hurghada between the RSM. The landslide inventory map, which has a total of 12 single landslide locations, was created based on fieldwork using hand-held GPS and Landsat 8 images. The two decision-making techniques were used to contrast the LSM factors based on the knowledge of experts using the qualitative method of AHP with the statistical approach that depends on the field data IV. Ten landslide-influencing parameters taken into consideration are slope, lithology, rainfall distribution, distance to fault, distance to track, seismicity of area, distance to epicenter, land cover, distance to stream, and aspect. The parameters were obtained using LANDSAT8 and SRTM DEM of a 30 m resolution, vectorized lithological structure units from a 1:2000, 000 map, and data between (1913–2000) for yearly precipitation and (1997–2018) for seismic activity of the research area. A GIS-based method produces a themed layer map for each geoenvironmental element, weighing each parameter after examination. A LSM was produced using a combination of thematic layers that were then separated into four grades: low, moderate, high, and very high, based on the intensity of the landslide vulnerability. According to AUC data, the IV model has a better success rate 0.85 than the AHP model 0.82. The study's models, especially when applied to the IV method, showed good accuracy in forecasting landslide vulnerability. The method shows rising landslide percentages with susceptibility and delivers accurate results that are compatible with the landslide inventory map. Environmental decisions often lead to unintended consequences due to lost information, incorrect factor selection, limited expert expertise, and ignoring uncertainty. This investigation emphasizes the importance of LSM as a tool for decision-makers to minimize landslide losses and damage.

MultiBEATS: Blocks of eigenvalues algorithm for multivariate time series dimensionality reduction

Multivariate Time Series are sequences of observations taken from multiple sources. The proliferation of environments in which data is collected by means of sensors and the adoption of data-based services reveals the importance of their efficient analysis. In smart environments, the analysis of the raw Multivariate Time Series is cumbersome. The algorithms are heavy, slow, and fail to extract all the knowledge available. In that sense, representation techniques reduce the data points but maintain the inner patterns so that the information present in data persists. There exist several approaches to represent Univariate Time Series, but for the multivariate case they are scarce. We have adapted the existent univariate representation algorithm BEATS, which is based on Discrete Cosine transformation and the extraction of eigenvalues to multivariate scenarios and we have called it MultiBEATS. Our method allows flexibility with regard to data reduction and does not require labelled data. To investigate the efficacy of MultiBEATS, we selected 8 open multivariate time series datasets and compared the running time and accuracy of their classification using raw data and MultiBEATS transformed data. We have also compared our results with the representation algorithm Seq2VAR, based on autoencoders, for having a baseline. The MultiBEATS transformation reduces the execution time of classification algorithms, has improved the results in accuracy in 2 of the datasets and matches a third one. With regards to the baseline, MultiBEATS is faster than Seq2VAR and more accurate in 7 out of the 8 datasets. In conclusion, MultiBEATS is a promising approach for efficiently reducing multivariate time series data, therefore helping decision-making in dynamic smart environments.

Uncovering heterogeneous effects in computational models for sustainable decision-making

Computational modeling is frequently incorporated into environmental decision-making in order to capture inherently complex relationships and system dynamics. The complexity of such models often lies in various heterogeneous effects that arise due to the interaction of different input factors or due to designed structural variation in the model. In the past, various sensitivity analysis approaches have been implemented in attempts to identify essential decision factors. However, existing sensitivity analysis methods fail to capture critical information in the presence of heterogeneous effects. In this paper, the recently introduced simulation decomposition (SimDec) visualization method is extended to include quantitative sensitivity analysis. The framework is tested on several decision-making problems and is shown to capture heterogeneous behavior. A formal definition and classification of heterogeneous effects for computational models is introduced. The framework is open-sourced in a variety of scientific programming languages.

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

IntroductionMicrobes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning.MethodsIn this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods.ResultsSequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing.DiscussionWe conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.

Often wrong, never in doubt: Mitigating leadership overconfidence in decision-making

Leaders are often celebrated for quick and decisive actions. Such actions include the ability to cut through the chaff and make rapid decisions in fast-paced environments. However, while decisiveness is admirable, poor decision-making is not. And an increasing amount of research informs us that leaders tend to be far too overconfident about their decision-making ability. First, this article details several ways that leaders’unconscious cognitive biases can cloud their decision-making ability. These biases such as attribution bias, the Dunning-Kruger effect, the planning fallacy, and jumping to faulty conclusions are particularly dangerous because everyone is infected by them—yet, because of the bias blind spot, leaders tend to naturally believe they are immune. Second, this article details ways that leaders can “mistake proof” their decision-making process. By exercising activities like pre-mortems, speed-accuracy tradeoffs, reference class forecasting, and improving reflective capacity, leaders can impose systems and methods to help protect their decision-making against their greatest potential nemesis—themselves.

Board Ancestral Diversity and Voluntary Greenhouse Gas Emission Disclosure

AbstractThis paper examines the relationship between board diversity and firms’ decisions to voluntarily disclose information about their greenhouse gas (GHG) emissions. We focus on board ancestral diversity as a relatively new dimension of (deep‐level) board structure and document that it has a positive and statistically significant effect on a firm's scope and quality of voluntary GHG emission disclosure. The effect goes beyond the impact of more common (surface‐level) dimensions of board diversity and remains robust after addressing endogeneity concerns. In line with the theoretical conjecture that diversity enhances a board's advising and monitoring capacity, we find that the impact of diverse boards is stronger in more complex firms and in firms with low levels of institutional ownership. Overall, our findings provide evidence for board diversity being a relevant governance factor in corporate environmental decision making.

Environment, Ethics, and Entity Economics, Economia Aziendale. Historical and Ontological Aspects

The paper stresses the failure of ‘economics’ to predict and prevent repeated economic crises and the most critical topic of the environmental failure and ecological crises, founded also on economic theories and misconceived choices. The implication is a search for alternatives through an ontological-evolutionary-institutional approach and empirical investigations.
 The search for political ‘normative economics’, entity economics, accounting and information science copes with social and ecological-environmental problems, and systems science; it is necessary to keep always in mind these interactions for fully understand and correctly interpret the present ‘social-economic reality’.
 The problem concerning environmental decisions goes beyond ontology and belongs to ethics. It must underline the debates on ‘evolutionary ethics’, not only related with ‘environmental and ecological economics’, the management of environment and reporting, but also with ‘evolutionary ontology’; the new perspective affirms that ‘existence’, for instance, of a moral behavioral attitude, can reveal itself as a ‘norm’ thus relating ethics to ontology.
 General economists, entity economics theorists, as well as accountants and information scientists should be familiar with the potentials and limits of ‘ontology’, also for the ongoing expansion of computerization of scientific research in most of the scientific domains. The renewed interest, particularly in ontology, arouse specifically from systems scientists in constructing a wide variety of artificial intelligent systems. For its analysis, the study has been structured into two periods of time: until the middle of the 20th century and from that moment on. The paper ends by showing the new developments in information systems and their ontological approach

Biodiversity Impact Assessment Considering Land Use Intensities and Fragmentation.

Land use is a major threat to terrestrial biodiversity. Life cycle assessment is a tool that can assess such threats and thereby support environmental decision-making. Within the Global Guidance for Life Cycle Impact Assessment (GLAM) project, the Life Cycle Initiative hosted by UN Environment aims to create a life cycle impact assessment method across multiple impact categories, including land use impacts on ecosystem quality represented by regional and global species richness. A working group of the GLAM project focused on such land use impacts and developed new characterization factors to combine the strengths of two separate recent advancements in the field: the consideration of land use intensities and land fragmentation. The data sets to parametrize the underlying model are also updated from previous models. The new characterization factors cover five species groups (plants, amphibians, birds, mammals, and reptiles) and five broad land use types (cropland, pasture, plantations, managed forests, and urban land) at three intensity levels (minimal, light, and intense). They are available at the level of terrestrial ecoregions and countries. This paper documents the development of the characterization factors, provides practical guidance for their use, and critically assesses the strengths and remaining shortcomings.

Environmental Decision-Making in Times of Polarization

Polarization in the United States and around the world is of growing concern. Polarization is about more than just differences in opinions in society. It occurs when groups increasingly diverge in either actual or perceived differences in opinion and can involve both disagreements about issues and negative views of other groups. Since most environmental problems are collective action problems, polarization may interfere with the kinds of deliberation and collaborations needed for effective environmental decision-making. In this review, we examine how polarization influences environmental decision-making and what strategies could be useful for preventing or reducing the negative consequences of polarization. Evidence about the extent of polarization among citizens suggests the current situation may be less severe than is sometimes assumed. The coevolution of individual views, network interactions, and social media that cause polarization is complex and subject to rapid change. However, there are interventions that seem to be effective at reducing polarization.

How smart health leaders make intuitive decisions.

A rational decision-making process enables a leader to process information clearly and logically and thus allows for accurate perception and interpretation of the event. It is believed this process prevents leaders from excessively distorting reality and being impacted by cognitive biases, both of which are possible, particularly under stressful conditions. But what happens when the decision-making environment is rapidly changing and the leader does not have time to deploy a thorough, comprehensive rational decision-making process? In time-compressed decision-making environments, leaders must often make quick, accurate decisions, with incomplete, inaccurate, or rapidly changing information, under extremely stressful conditions. To improve the ability of a leader to make high-stress, time-compressed decisions under rapidly changing conditions, we offer the intuitive decision-making process as an alternative to rational decision-making and discuss five components essential to improve intuitive decision-making outcomes.

Weight Optimization Decision Algorithm in (p,q)-Rung Probabilistic Hesitant Orthopair Fuzzy Environments

Aiming at the fuzzification of a decision environment and the challenge of determining the weights associated with the interaction among decision-makers, this study offers an original method for (p,q)-rung probabilistic hesitant orthopair fuzzy multi-objective group decision-making, which is founded on the weight optimization principle. Firstly, the notion of a probabilistic hesitant fuzzy set is expanded to a (p,q)-rung. Secondly, the determination of subjective and objective weights is accomplished through the utilization of the Analytic Network Process (ANP) and the Entropy Method. According to the degree of deviation and dispersion of each weight, an optimal objective function is constructed, and the neural network is used to iteratively solve for the best scheme of the comprehensive weight. Subsequently, the Elimination Et Choice Translating Reality (ELECTRE) approach was refined and applied to decision-making in the (p,q)-rung probabilistic hesitant orthopair fuzzy environment. Finally, comparative analysis was used to demonstrate the new method’s effectiveness and superiority.

Integrated AHP & TOPSIS methodology using intuitionistic Z-numbers: An application on hydrogen storage technology selection

The reliability of decision makers' judgments together with their degree of hesitancy are often ignored in decision models. The inclusion of judgments with only triangular belonging functions causes a great deal of information loss since non-belongingness functions are ignored in those decision models. In the literature, a method on how to integrate the intuitionistic reliability function into the decision maker's judgment has not been clearly proposed. To handle these issues, we aim at developing a novel intuitionistic Z-AHP (Analytic Hierarchy Process) & Z-TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) methodology in this paper. Z-numbers can be used to express uncertain quantities with different degrees of precision, which can be very useful in practical applications. Our methodology considers both the restriction function and its reliability function under intuitionistic fuzziness based on α-cut approach and uses non-linear intuitionistic belonging and unbelonging functions. We compare our approach with interval-valued fuzzy TOPSIS and conduct a sensitivity analysis to assess the effects of changes in criteria weights on the rankings of the alternatives. Our results show that the proposed methodology can effectively evaluate and rank the existing alternatives, considering the uncertainties and complexities of the decision environment. This research provides valuable insights for decision-makers and can take part in their decision support systems. The proposed methodology has been applied to a hydrogen storage technology selection problem. According to the results, the most favorable hydrogen storage option in the considered case is chemical storage alternative followed by liquid storage, compressed storage, carbon nanostructure storage, and metal–organic framework storage alternatives. The inference of the comparative analysis with intuitionistic Z-AHP and interval-valued fuzzy TOPSIS method is that the best two alternatives are same in both methodologies whereas the third and the fifth alternatives are replaced since a different linearization approach is applied to the non-linear restriction function multiplied by the reliability function.

Conservation, uncertainty and intellectual humility

SummaryInterventions in environmental conservation are intended to make things better, not worse. Yet unintended and unanticipated consequences plague environmental conservation; key is how uncertainty plays out. Insights from the intellectual humility literature offer constructive strategies for coming to terms with uncertainty. Strategies such as self-distancing and self-assessment of causal complexity can be incorporated into conservation decision-making processes. Including reflection on what we know and do not know in the decision-making process potentially reduces unintended and unanticipated consequences of environmental conservation and management decisions. An important caution is not to have intellectual humility legitimate failing to act in the face of uncertainty.

Development of an adverse outcome pathways approach for land resource and environment management at the regional scale

At the regional scale, the complexity of ecological deterioration limits effective decision-making and preventive measures. In this study, an adverse outcome pathway approach was developed and applied to land resource management and environmental decision-making in Daye, a traditional mining city in China. First, adverse outcome pathways for land degradation were hypothesized. Second, a weight of evidence approach was used based on the data strength and causal chain strength of each degradation pathway. Then, the technique for order preference by similarity to an ideal solution (TOPSIS) was used with entropy information and integrated with the geographic information system (GIS) to evaluate the credibility of each pathway. The most likely adverse outcome pathway and the spatial distribution for different sub-regions were obtained. The apparent spatial heterogeneity suggests that land degradation pathways are closely associated with economic production activities in each sub-region. This study is expected to facilitate the identification of key mechanisms underlying ecological degradation and to guide the management of land systems to ensure long-term functioning.

Complexity aversion in risky choices and valuations: Moderators and possible causes

In the age of digitalization and globalization, an abundance of information is available, and our decision environments have become increasingly complex. However, it remains unclear under what circumstances complexity affects risk taking. In two experiments with monetary lotteries (one with a stratified national sample), we investigate behavioral effects and provide a cognitive explanation for the impact of complexity on risk taking. Results show that complexity, defined as the number of possible outcomes of a risky lottery, decreased the choice probability of an option but had a smaller and less consistent effect when evaluating lotteries independently. Importantly, choices of participants who spent more time looking at the complex option were less affected by complexity. A tendency to avoid cognitive effort can explain these effects, as the effort associated with evaluating the complex option can be sidestepped in choice tasks, but less so in valuation tasks. Further, the effect of complexity on valuations was influenced by individual differences in cognitive ability, such that people with higher cognitive ability showed less complexity aversion. Together, the results show that the impact of complexity on risk taking depends on both, decision format and individual differences and we discuss cognitive processes that could give rise to these effects.

Optimization strategy for SAM in nuclear power plants based on NSGA-II

Abstract The Severe Accident Management Guide (SAMG) is an important component of nuclear safety regulations. Many studies are being conducted to optimize severe accident management (SAM) strategies. To ensure the safety of nuclear power plants, decision makers need to monitor multiple parameters with security threats. Therefore, it is particularly important to search optimal SAM strategies under different numbers of mitigation targets. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is an evolutionary algorithm that does not require derivative differentiation and is capable of population search. In this study, a nuclear power plant accident optimization strategy is developed using the Modular Accident Analysis Program (MAAP) in conjunction with NSGA-II. The strategy enables decision makers to consider multiple mitigation objectives in a complex decision environment. Focusing on the CPR1000, this study applies the optimization strategy to automatically search for optimal mitigation strategies for small break loss of coolant accident (SBLOCA) and station blackout hot leg creep rupture accidents (SBOHLCR). Comparing the optimization results with the basic accident sequence, it is found that the reactor pressure vessel (RPV) failure time is delayed from 72,702 s to 128,730 s under SBLOCA and from 23,828 s to 28,363 s under SBOHLCR. This study has also verified that the optimal SAM strategy obtained by the strategy through dual objective optimization has better mitigation effects than a strategy that only considers one objective. This optimization strategy has the potential to be applied to other types of severe accident management studies in the future.

Changing Patterns of Urban Green Spaces in Akure, Nigeria

Greenery and natural landscapes are vital to human well-being, especially in cities. Notably, the majority of people aspire to live in cities in search of opportunities and a better quality of life, which harms urban green spaces, also known as the "lungs of the city." Current debates on urban planning and sustainable development in Nigeria and around the world are fundamentally influenced by the rapid growth of urbanization and the ensuing domination of concrete spaces over natural spaces. This study examined patterns in Akure's urban green areas while taking into account the spatiotemporal pattern of vegetation cover between 1984 and 2022. The collected satellite imagery was analyzed using the ArcGIS application program, and the Normalized Difference Vegetation Index (NDVI) was used to calculate the rate and patterns of urban green space. Over time, changes in the spatial distribution of urban green spaces and the growth of non-green spaces were monitored and estimated. The depletion's implications for planning were addressed. To educate and inform urban residents about the protection, conservation, and preservation of natural landscapes within urban landscapes, it is thought that examining and analyzing the physical planning and environmental consequences of unchecked depletion of urban green spaces will be useful for making sustainable environmental policy decisions in harnessing the resources potentials.