Environmental Decision Support System Research Articles

Empowering Eco-Friendly Choices: An Environmental Impact Assessment Decision Support System for Textiles and Clothing

The textile and clothing industry is one of the industries with the highest environmental impact, with a huge amount of waste during the product life cycle. However, there are few tools available for companies to assess the environmental impact of their production process. In this way, a decision support system was developed so that producers can calculate the environmental impact associated with the textile and clothing production process, with the main objective of helping producers and, consequently, consumers to make environmentally conscious decisions given the increasingly demanding market. The methodology of this study integrated the accounting of a set of indicators, allowing producers to calculate the environmental impact associated with the textile and clothing production process. The decision support system returns, depending on the results of the calculations of the indicators, different ecolabels that allow consumers to compare different products based on their environmental performance. In short, the study in question allows us to contribute to environmentally conscious decision making, both for the producer and the consumer, to promote sustainable practices in the textile and clothing industry. The decision support system is flexible and adaptable for different companies and industries in order to meet their needs and improve their environmental performance.

Environmental decision support system development for natural distribution prediction of Festuca ovina in restoration of degraded lands

AbstractAnthropogenic activities, species invasions, and ecological factors are driving rapid changes in rangeland ecosystems. For ensuring the richness and sustainability of plant species habitats, there is a pressing need for a reliable prediction model that can accurately forecast and map species distribution under varying ecological conditions. We aimed to compare the performance of three widely used machine learning methods: Multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) in predicting the distribution of Festuca ovina in mountainous protected rangelands. We conducted our investigation by analyzing the distribution of F. ovina in 305 randomly selected plant sample plots. In each plot, we recorded 10 ecological variables. Three machine learning models were developed to predict the likelihood of F. ovina distribution. Our results demonstrated that the RBF model had a higher number of misclassifications (11 samples) compared to MLP and SVM models (10 samples), indicating that MLP and SVM models were more accurate for distribution modeling. Additionally, MLP showed a higher R‐squared value (0.87) compared to SVM (0.85), suggesting that MLP was the most accurate model for restoring degraded lands. Hence, we developed the Festuca ovina Distribution Model (FODM) using the MLP model. Sensitivity analyses revealed that soil texture, soil depth, electrical conductivity (EC), pH, and vegetation density significantly influenced F. ovina distribution, with respective sensitivity coefficients of 0.48, 0.47, 0.45, 0.41, and 0.41. Based on the finalized FODM, we designed an Environmental Decision Support System (EDSS) tool to assist rangeland managers in mapping F. ovina distribution. By applying the EDSS tool, we demonstrated its practicality in using the FODM for effective decision‐making and land management. The EDSS tool serves as a valuable resource for rangeland managers, enabling them to make informed decisions regarding F. ovina restoration and effectively use the predictive capabilities of the FODM in real‐world applications.

Suitability evaluation of rural sewage treatment facilities in China considering lifcycle environmental impacts and regional differences

Centralized vs. decentralized sewage treatment is one of the key issues in the planning of rural sewage treatment (RST) in China due to the country's diverse regional characteristics. There are very limited comprehensive evaluation models for selecting regionally suitable schemes and facilities, particularly for national or provisional scale planning. As a scenario-based multi-attribute decision-making (MADM) issue, this paper develops a novel RST suitability evaluation model by integrating the multi-attribute analytic hierarchy process (AHP) with the technique for order preference by similarity to an ideal solution (TOPSIS). The suitability evaluation model sets up 3 small-centralized and 4 decentralized RST facilities as candidates and includes 12 evaluation indicators that cover economic cost, life cycle environmental impacts, technical features and operations management. Eight generic scenarios are classified for Chinese rural areas based on differences in three major characteristic factors, i.e., population density (PD), the economic development level (EDL), and topographic slope (TS). The universal evaluation results show that a centralized sewage treatment scheme is more suitable for areas with a high PD/high EDL/low TS, while a decentralized scheme is more suitable for areas with a low PD/low EDL/high TS. Sensitivity analysis shows that in regions with a high PD/low EDL, the indicator weight of the construction investment cost in the model has a great influence on the facility suitability ranking. However, in regions with a high PD/high EDL, the ranking is the most sensitive to the indicator weights of the global warming potential and sewage treatment effect. Furthermore, as a spatial decision issue, an RST suitability map of Hunan Province in China is produced at the county level of resolution, and the map is generally consistent with our field knowledge of several counties in Hunan Province. The presented evaluation framework can be integrated into environmental decision support systems in the future to help local and central governments, water utilities, design institutes and other stakeholders scientifically plan RST projects.

From scenario to roadmap: Design and evaluation of a web-based participatory watershed planning system for optimizing multistage implementation plans of management practices under stepwise investment

Planning multistage implementation plans, or roadmaps, based on the spatial distribution of a best management practice (BMP) scenario is essential for achieving watershed management goals under realistic conditions, such as stepwise investment plans that involve multiple stakeholders, including investors, economic and environmental beneficiaries. The state-of-the-art BMP roadmap optimization method can address this need for optimization but is over-specialized and complex to non-expert stakeholders. This study designed a user-friendly web-based participatory watershed planning system to assist a diverse group of stakeholders in reaching a consensus on optimal roadmaps. The participatory process of stakeholders includes iteratively proposing stepwise investment constraints, submitting roadmap optimization tasks, analyzing spatiotemporal results from multiple perspectives, and selecting preferred roadmaps. The proposed system design separates the participatory process of non-expert stakeholders from the specialized modeling process of constructing simulation-optimization tools for BMP roadmaps, which is done in advance by professional modelers and encapsulated as webservices on the server side. The webservices expose a small set of essential parameters to lower barriers to use. The interactive participatory process is presented to stakeholders through web browsers with an easy-to-use interface. The system design was evaluated by implementing an agricultural watershed planning system for soil erosion reduction and conducting a role-playing experiment involving three groups of stakeholders with different standpoints in proposing investment constraints. The experimental results show that the optimal roadmap sets exhibit progressive improvements across three-round optimizations started by different stakeholders, effectively capturing the varying perspectives of stakeholders and facilitating consensus-building among them. The idea of system design and example implementation can serve as a valuable reference for developing related user-friendly environmental decision support systems.

An integrated supervision framework to safeguard the urban river water quality supported by ICT and models

Models and information and communication technology (ICT) can assist in the effective supervision of urban receiving water bodies and drainage systems. Single model-based decision tools, e.g., water quality models and the pollution source identification (PSI) method, have been widely reported in this field. However, a systematic pathway for environmental decision support system (EDSS) construction by integrating advanced single techniques has rarely been reported, impeding engineering applications. This paper presents an integrated supervision framework (UrbanWQEWIS) involving monitoring-early warning-source identification-emergency disposal to safeguard the urban water quality, where the data, model, equipment and knowledge are smoothly and logically linked. The generic architecture, all-in-one equipment and three key model components are introduced. A pilot EDSS is developed and deployed in the Maozhou River, China, with the assistance of environmental Internet of Things (IoT) technology. These key model components are successfully validated via in situ monitoring data and dye tracing experiments. In particular, fluorescence fingerprint-based qualitative PSI and Bayesian-based quantitative PSI methods are effectively coupled, which can largely reduce system costs and enhance flexibility. The presented supervision framework delivers a state-of-the-art management tool in the digital water era. The proposed technical pathway of EDSS development provides a valuable reference for other regions.

An environmental decision support system for manufacturer-retailer within a closed-loop supply chain management using remanufacturing

<abstract> <p>Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.</p> </abstract>

The use of environmental decision support systems for modeling of atmospheric pollution following the chemical accidents

We studied the possibility of the combined application of screening models to assess the characteristics of sources in accidents at storage facilities for hazardous substances with complex models of atmospheric transport as part of modern decision support systems to calculate air pollution in a wide range of spatial and temporal scales. The evaporation time following an emergency spill, estimated by screening models, is used to set the emission intensity and calculate the atmospheric transport by the RODOS nuclear emergency response system. For the accident in Chernihiv on March 23, 2022, it was estimated that the maximum permissible concentration of ammonia 0.2 mg/m3 was exceeded at distances up to 75 km from the source. The dependence of the calculated maximum concentrations on time is close to asymptote cmax ~ t-4.5 up to 15 h after emission, which is consistent with the asymptote σ ~ t3/2 for the time dependence of the sizes of puffs following turbulent dispersion of instantaneous releases.

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10.

Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM2.5 and PM10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM10 and PM2.5. Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

Decision support for corporate sustainability: systems and stakeholders

ABSTRACT Decision support systems for corporate environmental management and ecological sustainability are proliferating, this paper explores their directions in a multifaceted literature via an integrative literature review. Besides delineating the interrelated areas of decision support systems for sustainability, we study the role of internal stakeholders in their implementation. In our review, we examine a relatively mature field (Environmental Decision Support System, EDSS) and an emerging field (Green Information System, Green IS), and their potential interplays. The importance of stakeholder inclusion in general, and the participation of end-users in the design and implementation of decision support systems, in particular, are analysed in the EDSS literature. The Green IS has a prospective future in decision support with its capacity to capture corporate environmental performance. Green IS literature, however, has just recently identified the significance of employee participation: barriers and potentials to engage employees have already been identified but still to be explored.

Environmental Decision Support Systems as a Service: Demonstration on CE-QUAL-W2 Model

An environmental decision support system (EDSS) can be used as an important tool for the rehabilitation and preservation of ecosystems. Nonetheless, high assimilation costs (both money and time) are one of the main reasons these tools are not widely adopted in practice. This work presents a low-cost paradigm of “EDSS as a Service.” This paradigm is demonstrated for developing a water quality EDSS as a service that utilizes the well-known CE-QUAL-W2 model as a kernel for deriving optimized decisions. The paradigm is leveraging new open-source technologies in software development (e.g., Docker, Kubernetes, and Helm) with cloud computing to significantly reduce the assimilation costs of the EDSS for organizations and researchers working on the rehabilitation and preservation of water bodies.

Development of Environmental Decision Support System for Sustainable Smart Cities in India

AbstractIn the era of advanced technologies, information and communication technology (ICT) is the crucial component for developing Smart Cities. The topical studies indicate that ICT is primarily ignored to improve environmental sustainability in Smart Cities. The present research is an initial effort to develop a web‐based Environmental Decision Support System (EDSS) for Sustainable Smart Cities in India (SSCI). The application developed evaluates a city's performance on a set of 24 environmental indicators, which is broadly categorized into four domains: Solid Waste Management (SWM), Water Supply Management (WSM), Sewerage, Sanitation, and Stormwater Management (SSS), and Ambient Environment Condition (AEC). EDSS‐SSCI can serve as a valuable tool for policymakers in benchmarking a city's existing environmental scenario and grade it on a scale range of critically low, poor, fair, good, and excellent. The developed tool gives information about the city's performance in each domain along with three priority indicators which are decided on the basis of critical performance and higher weights given through the Delphi technique. This study presents the application to a real case study in Varanasi city, which identifies that its environmental sustainability smartness score exists in poor range and the focused attention for improvement is AEC domain. The study outcomes of the application developed will be beneficial for policymakers to define and drive the planning action towards developing Environmentally Sustainable Smart Cities in India.

An Integrated Water Quality Model to Support Multiscale Decisions in a Highly Altered Catchment

Decision-making in highly altered catchments occurs at different temporal and spatial scales, requiring integration of various datasets and models. This paper introduces two of the components of an environmental multiscale decision support system (EMDSS) for highly altered catchments, designed to make decisions at different time scales. First, an integrated dynamic flow and water quality model is proposed to analyze the river system, including wastewater discharges and water intakes. This integrated model is capable of representing unsteady flow conditions, allowing analysis at different time scales. Second, three postprocessing tools are presented to support short- (hours to days), medium- (days to months), and long- (years to decades) term operational, management, and planning decisions. The water quality component of the model can represent conventional and toxic determinands to simultaneously analyze domestic and industrial pollution throughout a river system. The first postprocessing tool of the EMDSS is useful in defining concentration limits for wastewater discharges for different water users downstream. The second tool allows the assessment of river water quantity and quality to determine water availability for intake extensions and medium-term wastewater flow augmentation. The third makes it possible to simulate and perform effective operational reservoir releases to improve water quality in the river during short-term pollution incidents. The proposed integrated model and postprocessing tools are applied in the upper Bogotá River stretch in Colombia, one of the most altered catchments and polluted rivers in the world. The results obtained illustrate the utility of the proposed EMDSS for river management and decision making regarding water quality at different time scales.

Environmental decision support system for Plane trees failure prediction: A comparison of multi‐layer perceptron and random forest modeling approaches

AbstractTree risk is the likelihood of property damage or personal injury from a potentially hazardous tree in cities. We aimed to compare multilayer perceptron (MLP) and random forest (RF) model techniques to predict Plane trees failure hazard in urban areas. In this research, 500 Plane trees in different urban structures were selected, and 12 variables were measured for target trees prediction as model independent variables. Tree failure hazard (tree fails along next 3 yr) was modeled by two machine learning techniques: MLP and RF. The RF model represents the higher accuracy in training (100%), test (94.4%), and all data sets (98.4%). The results of variable importance calculation of designed RF model revealed that wind‐protected aspects, soil depth, length of cracks and cavities, and internal decay are the most important variables respectively that influence the classes of Plane tree failure. The urban foresters or designers can easily determine the possibility of Plane trees risk by running the designed graphical user interface and entering 12 variables of Plane tree. As a result, it will help foresters and urban area managers to make better decisions and predict the proper time to decrease harm from Plane tree failure.

Survey of Process of Data Discovery and Environmental Decision Support Systems

The process of data discovery is an approach to extracting knowledge, valid, and usable information from large amounts of data, using automatic or semi-automatic methods. This article is an inventory of the different information extraction processes encountered in the literature for different fields of application and for the development of environmental informatics. Following an analysis between the different models, we can summarize the existing models with a proposal for a process that exploits the strengths of the different processes.

An application of artificial intelligence techniques in prediction of birds soundscape impact on tourists’ mental restoration in natural urban areas

The characteristics of birds' sounds assume a primary role in tourists' mental restoration and stress recovery. The aim of this research is the evaluation of birds' sound composition in mental restoration of urban tourists to develop a decision support system as a practical tool. In this order, the recorded sounds of six birds were composed (57 composed sounds) and the human perception approach was used to assess the impact of sounds on the urban tourist's mental restoration. The MLP (Multi-Layer Perceptron), RBFNN (Radial Basis Function Neural Network) and SVM (Support Vector Machine) models were developed for mental restoration prediction in different birds' sound compositions. The results indicated that RBFNN model output (R2 training = 0.89, and R2 test = 0.85) has the best accuracy compared to the MLP and SVM models in prediction of birds' soundscape score in natural urban areas. According to the sensitivity analysis, the values of White eared Bulbul (Pycononotus leucotis), Great Tit (Parus major), House Sparrow (Passer domesticus), Laughing Dove (Spilopelia senegalensis), White Wagtail (Motacilla alba), and Eurasian Magpie (Pica pica) are prioritized respectively that influence the RBFNN model outputs. In practice, the designed environmental decision support system tool is applied by urban planners, managers, psychoacoustic researchers, and landscape architects to predict the landscape score in different birds' habitats.

Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.

Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

This work presents a simplified method for the application of the Multi-Layer Perceptron (MLP) model that aims to predict the aesthetic quality of the landscape designed by water and plants in different forms and volume. The MLP was prepared by (Rosenblat) in the field of computer science, followed by the application of a MLP in landscape aesthetic quality prediction proposed by (Jahani). In the method of this research, the structure of MLP was structured for aesthetic quality prediction of plants and water in urban park landscapes. The accuracies of designed MLP structures were tested to achieve the most accurate one in aesthetic quality prediction. This method creates an environmental decision support system tool for landscape designers, and it is a platform to predict the quality of environment. In practice, the designed environmental decision support system tool is applied by landscape managers to predict the aesthetic quality of landscape in designing new urban parks.•Applies Multi-Layer Perceptron method in landscape assessment.•Accurate MATLAB extension for landscape aesthetic evaluation.•Defined criteria for aesthetic value of landscape.

Benchmarking empirical models for THMs formation in drinking water systems: An application for decision support in Barcelona, Spain

Drinking water treatment plants (DWTPs) face changes in raw water quality, which affect the formation of disinfection by-products. Several empirical modelling approaches have been reported in the literature, but most of them have been developed with lab-scale data, which may not be representative of real water systems. Therefore, the application of these models for real-time operation of DWTPs might be limited. At the present study, multiple linear regression (MLR) and multi-layer perceptrons (MLP) were benchmarked using field-scale data for predicting the THMs formation in a case-study DWTP in Barcelona, Spain. After fitting the studied models, MLR exhibited good fit with the validation data set (R2 = 0.88 and MAE = 4.0 μg·L−1) and described the most plausible input-output relationships with field-scale data. The MLR predictive model was incorporated into an environmental decision support system (EDSS) for assessing the THMs formation at two critical points of the distribution network. A Monte Carlo scheme was applied for quantifying uncertainty of model predictions at these points, considering low and high water quality scenarios and different degrees of treatment by an electrodialysis reversal process. The results show that the use of the proposed EDSS can help in real operation of complex drinking water systems, which face important changes in water quality throughout the year.

Human Activities Impact Prediction in Vegetation Diversity of Lar National Park in Iran Using Artificial Neural Network Model.

The effects of livestock and tourism on vegetation include loss of biodiversity and in some cases species extinction. To evaluate these stressor-effect relationships and provide a tool for managing them in Iran's Lar National Park, we developed a multilayer perceptron (MLP) artificial neural network model to predict vegetation diversity related to human activities. Recreation and restricted zones were selected as sampling areas with maximum and minimum human impacts. Vegetation diversity was measured as the number of species in 210 sample plots. Twelve landform and soil variables were also recorded and used in model development. Sensitivity analyses identified human intensity class and soil moisture as the most significant inputs influencing the MLP. The MLP was strong with R2 values in training (0.91), validation (0.83), and test data sets (0.88). A graphical user interface was designed to make the MLP model accessible within an environmental decision support system tool for national park managers, thus enabling them to predict effects and develop proactive plans for managing human activities that influence vegetation diversity. Integr Environ Assess Manag 2021;17:42-52. © 2020 SETAC.