A cloud model-based approach for water quality assessment

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Groundwater chemical evolution characteristics and human health risk assessment in Shicheng County, Jiangxi Province

Groundwater plays a pivotal role in the water resources of Shicheng County; however, the issue of excessive fluoride content in groundwater and its associated health risks often goes unnoticed. Groundwater assumes a crucial role in the hydrological dynamics of Shicheng County; nevertheless, the matter concerning elevated levels of fluoride within groundwater and its accompanying health hazards frequently evades attention. The hydrogeochemical analysis, obscure comprehensive water quality assessment based on cloud model, and probabilistic human health risk assessment using Monte Carlo simulation were conducted on 34 collected water samples. The findings indicate that the predominant groundwater hydrochemical types are SO4·Cl-Na and HCO3-Na. The processes of rock weathering and cation exchange play crucial roles in influencing water chemistry. Groundwater samples generally exhibit elevated concentrations of F-, surpassing the drinking water standard, primarily attributed to mineral dissolution. The concentrations of F- in more than 52.94% and 23.53% of the groundwater samples exceeded the acceptable non-carcinogenic risk limits for children and adults, respectively. Considering the inherent uncertainty in model parameters, it is anticipated that both children and adults will have a probability exceeding 49.36% and 30.50%, respectively, of being exposed to elevated levels of F ions in groundwater. The utilization of stochastic simulations, in contrast to deterministic methods, enables a more precise depiction of health risks. The outcomes derived from this investigation possess the potential to assist policymakers in formulating strategies aimed at ensuring the provision of secure domestic water supplies.

Innovative soil fluoride estimation method: dual polarimetric saline-associated fluoride for agricultural patches

Fluoride and its constituents in soil affect plant growth and public health. In this study, soil fluoride was measured for the semi-arid regions in southern India, using Sentinel-1 data in conjunction with the dual polarimetric saline-associated fluoride model (also known as fluoride model). A loss angle was estimated from laboratory-based dielectric components of soil samples with strong electrical conductivity under high and low fluoride conditions. The conductivity loss angle and real and imaginary dielectric constants were used to study fluoride salt's dielectric behavior. The imaginary dielectric component sensitive to dielectric loss could predict fluoride across large areas over time. This was statistically analyzed with R2 = 0.86, RMSE = 1.90, and bias = 0.35 showing a promising depiction that C-band SAR data can distinguish fluoride levels over varied clay soil and soil with varying vegetation development. Moreover, the association between biomass and simulated fluoride helped to identify fluoride-tolerant and non-tolerant crops. The study found that Sorghum and Oryza sativa tolerate saline-associated fluoride, whereas Peanut and Allium do not. Furthermore, the model successfully retrieves fluoride from saline salts based on tangent loss.

Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.

Risk assessment of debris flow disaster based on the cloud model-Probability fusion method

This paper proposes a new debris flow risk assessment method based on the Monte Carlo Simulation and an Improved Cloud Model. The new method tests the consistency of coupling weights according to the characteristics of the Cloud Model firstly, so as to determine the weight boundary of each evaluation index. Considering the uncertain characteristics of weights, the Monte Carlo Simulation is used to converge the weights in a minimal fuzzy interval, then the final weight value of each evaluation index is obtained. Finally, a hierarchical comprehensive cloud is established by the Improving Cloud Model, which is used to input the comprehensive expectation composed of weights to obtain the risk level of debris flow. Through statistical analysis, this paper selects Debris flow scale (X1), Basin area (X2), Drainage density (X3), Basin relative relief (X4), Main channel length (X5), Maximum rainfall (X6) as evaluation indexes. A total of 20 debris flow gullies were selected as study cases (8 debris flow gullies as model test, 12 debris flow gullies in reservoir area as example study). The comparison of the final evaluation results with those of other methods shows that the method proposed in this paper is a more reliable evaluation method for debris flow prevention and control.

An evaluation model for automobile intelligent cockpit comfort based on improved combination weighting-cloud model

Aiming at the comfort evaluation of automobile intelligent cockpit, an evaluation model based on improved combination weighting-cloud model is established. By consulting relevant literature, 4 first-class indexes and 15 second-class indexes, including noise and vibration, light environment, thermal environment and human-computer interaction, are selected to establish a comfort evaluation system. Later the subjective and objective weights obtained by improved Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are combined by Game Theory. Considering the fuzziness and randomness of the index system, the combination weights obtained by Game Theory are combined with the cloud model. The floating cloud algorithms is used to determine the first-class and second-class index clouds and the comprehensive evaluation cloud parameters. Improvements were made in two commonly used similarity calculation methods, the expectation curve method (ECM) and the maximum boundary curve method (MCM). A new similarity calculation method is defined to optimize the evaluation results and determine the final comfort evaluation grade. Lastly, a 2021 Audi intelligent car under a certain working condition was selected to verify the correctness and rationality of the model using the fuzzy evaluation method. The results show that the cockpit comfort evaluation model based on the improved combination weighting-cloud model can better reflect the comprehensive comfort of automobile cockpit.

Safety Performance Assessment of Construction Sites under the Influence of Psychological Factors: An Analysis Based on the Extension Cloud Model

Psychological hazards within organizational structures of construction sites are difficult to detect and can have significant negative impacts on safety performances when such hazards erupt. At present, most safety performance assessment models for construction sites ignore psychological factors. Therefore, in order to reveal psychological hazards within construction site organizations and to avoid damage caused by psychological hazards to safety performances, this paper evaluates the safety performances of construction sites by focusing on leader-member exchange ambivalence as the main trigger point. The evaluation system and evaluation criteria are established through three aspects: building scale, emotional orientation, and stability factors. The hierarchical analysis method, game theory, and extension cloud model are combined to make evaluation results more objective and credible. Moreover, a construction project with high technical requirements, high investment, and complex construction conditions (defined as a complex project) and an ordinary construction project with low technical difficulty and simple construction conditions (defined as a general project) were selected for analysis. The evaluation results indicate that both complex projects and general projects have safety hazards regarding psychological orientations. Finally, this paper makes some suggestions from three aspects: management system and corporate culture, building site intelligence, and social opinion to improve the safety performances of construction sites. The evaluation results are the same as actual operation results, which verify that models proposed in this paper can be used for safety performance evaluations of actual construction projects and provide help for managers to grasp overall safety levels.

Rapid migration of mainland China's coastal erosion vulnerability due to anthropogenic changes

With the global rise in sea levels caused by climate change and frequent extreme weather processes, high-density population aggregation and human development activities to enhance coastal areas vulnerability, populations, resources, and the ecological environment are facing huge pressure. Natural coastlines are being destroyed, and increasingly serious problems, such as coastal erosion and ecological fragility, have become disasters in coastal zones. The coastal vulnerability changed by climatic variables has created a major concern at regional, national and global scales. By comparing the data of two periods in the past 40 years, coastline vulnerability of coastal erosion in mainland China were evaluated by use of reverse cloud model and AHP with 10 indicators, including natural, anthropogenic, social and economic factors, etc. The main factors controlling coastal erosion included the proportion of Quaternary strata, the gradual reclamation of marine areas as land areas (in kilometres) and the percentage decrease in coastal sediment entering the sea. The secondary impact factors included the high proportion of artificial coastlines and the impacts of waves and storm surges under the influence of relative sea level changes. Human activities could further influence coastal vulnerability, making the erosion risk a considerable concern. Legislation, coordinated management system and technology are proposed to improve the quality of the marine ecological environment.

Hydrodynamic and ecological 3D modeling in tropical lakes

Conservation and improvement of water quality in water bodies is an important matter to maintain all of its uses as well as other human necessities like microclimate regulation and leisure. Lakes and reservoirs have a complex circulation behavior with vertical temperature profiles changes along the time, resulting in differences in water density and a vertical stratification condition. This characteristic can directly affect the water quality conditions perturbing its main indicators. This study aims to evaluate the quasi-3D models' capacity to represent the hydrodynamic behavior of a tropical lake and its effects on the main variables that characterize its water quality. To achieve this objective, high-frequency monitoring data were collected, the lake was represented in a quasi-3D model, and the accuracy of the result was evaluated by applying statistical indices. The evaluation showed good agreement between field measures and simulated results when compared with other applications. The connections between hydrodynamic behavior and water quality were seen with the simulations results analysis, which showed that mixing events and long stratification periods perturb the water quality, the first with re-suspended bed material and the second blocking the surface and bottom exchanges. The application of a 3D model gives the capacity to reproduce the reservoir spatial variability and its vertical profiles, which is necessary to study the constituents' distributions across the water column. Therefore, the hydrodynamic and water quality behavior of lakes was accurately represented by the model, as well as the importance of improving high-frequency monitoring techniques.

INVESTMENT DECISION ANALYSIS OF INTERNATIONAL MEGAPROJECTS BASED ON COGNITIVE LINGUISTIC CLOUD MODELS

The investment decision analysis of international megaprojects is a major area of interest. The choice of international megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model.

Assessing the Effect of the Chinese River Chief Policy for Water Pollution Control under Uncertainty-Using Chaohu Lake as a Case

The River Chief Policy (RCP) is an innovative water resource management system in China aimed at managing water pollution and improving water quality. Though the RCP has been piloted in some river basins of China, few scholars have studied the effects of the policy. We built a differential game model under random interference factors to compare the water pollution in Chaohu Lake under the RCP and without the RCP, and we explored the conditions to ensure the effectiveness of the RCP. The results showed that: (1) The average effect of water pollution control under the RCP was greater than under non-RCP; (2) the higher the rewarding excellence and punishing inferiority coefficient (θ) was, the better the water pollution control effect under the RCP; (3) the greater the random interference coefficient (σ) and rewarding excellence and punishing inferiority coefficient (θ) were, the bigger the fluctuation of the water pollution control effect was; (4) when using the stochastic differential game, when σ≤0.0403, θ≥0.0063, or σ>0.0403, θ≥0.268, the RCP must be effective for water pollution control. Therefore, we can theoretically adjust the rewarding excellence and punishing inferiority coefficient (θ) and the random interference coefficient (σ) to ensure the effective implementation of the RCP and achieve the purpose of water pollution control.

Eutrophication Assessment Based on the Cloud Matter Element Model

Eutrophication has become one of the most serious problems threatening the lakes/reservoirs in China over 50 years. Evaluation of eutrophication is a multi-criteria decision-making process with uncertainties. In this study, a cloud matter element (CME) model was developed in order to evaluate eutrophication level objectively and scientifically, which incorporated the randomness and fuzziness of eutrophication evaluation process. The elements belonging to each eutrophication level in the CME model were determined by means of certainty degrees through repeated simulations of cloud model with reasonable parameters of expectation E_x, entropy E_n, and hyper-entropy H_e. The weights of evaluation indicators were decided by a combination of entropy technology and analytic hierarchy process method. The neartudes of water samples to each eutrophication level of lakes/reservoirs in the CME model were generated and the eutrophication levels were determined by maximum neartude principal. The proposed CME model was applied to evaluate eutrophication levels of 24 typical lakes/reservoirs in China. The results of the CME model were compared with those of comprehensive index method, matter element model, fuzzy matter element model, and cloud model. Most of the results obtained by the CME model were consistent with the results obtained by other methods, which proved the CME model is an effective tool to evaluate eutrophication.

Assessment of lake eutrophication using a novel multidimensional similarity cloud model

Lake eutrophication is characterized by a variety of indicators, including nitrogen and phosphorus concentrations, chemical oxygen demand, chlorophyll levels, and water transparency. In this study, a multidimensional similarity cloud model (MSCM) is combined with a random weighting method to reduce the impacts of random errors in eutrophication monitoring data and the fuzziness of lake eutrophication definitions on the consistency and reliability of lake eutrophication evaluations. Measured samples are assigned to lake eutrophication levels based on the cosine of the angle between the cloud digital characteristics vectors of each sample and those of each eutrophication grade. To field test this method, the eutrophication level of Nansi Lake in Shandong Province was evaluated based on monitoring data collected in 2009-2016. Results demonstrate that, in 2009 and in 2011-2015, the upper lake of Nansi Lake exhibited moderate eutrophication while the lower lake exhibited mild eutrophication. In 2010, 2016, elevated concentrations of total nitrogen and total phosphorus led to an increase in the eutrophication level of the lower lake, matching that of the upper lake. Based on the results of these field tests, we conclude that the MSCM presented in this study provides a more flexible and effective method for evaluating lake eutrophication data than the existing multidimensional normal cloud model.

Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach

Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chemical oxygen demand, total phosphorus, total nitrogen, and clarity. Firstly, to deal with the uncertainties and fuzziness of data, triangular fuzzy numbers (TFN) were applied to describe the fuzziness of parameters. Secondly, to assess the eutrophication grade of lakes comprehensively, an improved fuzzy matter element (FME) approach was incorporated with TFNs with weights determined by combination of entropy method and analytic hierarchy process (AHP). In addition, the Monte Carlo (MC) approach was applied to easily simulate the arithmetic operations of eutrophication evaluation. The hybrid model of TFN, FME, and MC method is termed as the TFN⁻MC⁻FME model, which can provide more valuable information for decision makers. The developed model was applied to assess the eutrophication levels of 24 typical lakes in China. The evaluation indicators were expressed by TFNs input into the FME model to evaluate eutrophication grade. The results of MC simulation supplied quantitative information of possible intervals, the corresponding probabilities, as well as the comprehensive eutrophication levels. The eutrophication grades obtained for most lakes were identical to the results of the other three methods, which proved the correctness of the model. The presented methodology can be employed to process the data uncertainties and fuzziness by stochastically simulating their distribution characteristics, and obtain a better understanding of eutrophication levels. Moreover, the proposed model can also describe the trend of eutrophication development in lakes, and provide more valuable information for lake management authorities.

A Normal Cloud Model-Based Method for Water Quality Assessment of Springs and Its Application in Jinan

Springs are a source of drinking water and a famous tourist attraction in Jinan, China. In this paper, a multi-index evaluation method was proposed based on a normal cloud model. This model is new graphic model, which could synthetically picture the randomness and fuzziness of concepts. Ten parameters were selected, and water quality was classified into five levels. Three numerical characteristics were calculated, and the weights were assigned by an integrated weighting algorithm. The uncertainty of each spring was calculated by a cloud generator and the integrated certainty grades of water quality were determined. To ensure the accuracy of the normal cloud model, the proposed method was used to assess the water quality of springs in Jinan, China. The results obtained by the proposed method were compared with that of the other four methods. The results obtained by different methods are highly consistent. The proposed cloud model-based method can reflect the water quality level and provides a practical guide for water quality evaluation, as demonstrated in Jinan springs.

Assessment and improvement of wetlands environmental protection plans for achieving sustainable development

Although wetland environmental protection plans are synonymous with wetland management and erosion control plans, the public perceptions of such plans often focus on their impact on the human enjoyment of wetland areas. These plans are affected by many interrelated influence factors, such as human welfare, property, safety, management, operations, maintenance, ecology, environment, artificial structures, climate control, and sustainable development. The purpose of this paper is to probe how to use qualitative and quantitative measurements of wetland environments to create plan indexes using criteria/attributes as well as how to help these indexes for achieving the aspiration levels in each criterion/attribute. Previous studies that attempted to measure environmental evaluations and plans have assumed that these criteria are independent, but this assumption does not hold in real-world applications of real problems. Therefore, in this proof-of-concept study, using an empirical exam among various attributes and to measure and evaluate the real conditions for improving the wetland environmental problems. A DEMATEL technique can be used to construct the INRM, the basic concept of an ANP was modified to determine the influential weights of criteria/dimensions in our research alternative, called DANP (DEMATEL-based ANP). Then we can construct a decision-making model via a hybrid modified VIKOR method to improve wetlands environmental management manager strategy formulation in performance evaluation toward for achieving the aspiration level. Using these techniques, a proposed model appeared, which can be used to explain interdependence and feedback problems. Based on the final results, we can also propose a gap improvement in the development of a sustainable development plan for the environment while taking comfort and safety into account to improve standards and achieve human welfare expectations.

Precondition Cloud and Maximum Entropy Principle Coupling Model-Based Approach for the Comprehensive Assessment of Drought Risk

As a frequently occurring natural disaster, drought will cause great damage to agricultural production and the sustainable development of a social economy, and it is vital to reasonably evaluate the comprehensive risk level of drought for constructing regional drought-resistant strategies. Therefore, to objectively expound the uncertainty of a drought risk system, the precondition cloud and maximum entropy principle coupling model (PCMEP) for drought risk assessment is proposed, which utilizes the principle of maximum entropy to estimate the probability distribution of cloud drops, and the two-dimensional precondition cloud algorithm to determine the certainty degree of drought risk. Moreover, the established PCMEP model is further applied in a drought risk assessment study in Kunming city covering 1956–2011, and the results indicate that (1) the probability of drought events for different levels exhibits a slight increasing trend among the 56 historical years; and (2) both the integrated certainty degree and its component of drought risk are more evident, which will be more beneficial to determine the drought risk level. In general, the proposed PCMEP model provides a new reliable idea to evaluate the comprehensive risk level of drought from a more objective and systematic perspective.

Assessment and uncertainty analysis of groundwater risk

Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment.

Employing SWOT Analysis and Normal Cloud Model for Water Resource Sustainable Utilization Assessment and Strategy Development

Water Resource Sustainable Utilization (WRSU) is becoming increasingly important, given growing water resource shortages and widening gaps between water supply and demand. Most existing studies have focused on WRSU levels without a dedicated strategy-oriented framework. In addition, uncertainties occur in the process of indicator quantification and grading, leading to a lack of accuracy in the assessment results. Therefore, in this study, stemming from water resource, societal, economic, and environmental dimensions, an indicator system with qualitative description was introduced by Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to enable development and selection of sustainable water use strategies. A normal cloud model that is capable of addressing uncertainties was used to determine WRSU levels. The comprehensive evaluation results can both reflect the WRSU levels and select the most suitable strategy. The model’s utility was demonstrated by applying it to the case of Shandong province in China. Based on the results, most areas of Shandong province appear to be facing serious unsustainable issues. Appropriate development strategies based on the WRSU levels were provided for improving sustainable use of water resources. The proposed method offers an efficient means for WRSU assessment and strategy development. Moreover, it has the potential to be applied to other water resource issues.

A comprehensive evaluation of regional water safety systems based on a similarity cloud model

Regional water safety systems are affected by social, economic, ecological, hydrological and other factors, and their effects are complicated and variable. Studying water safety systems is crucial to promoting the coordinated development of regional water safety systems and anthropogenic processes. Thus, a similarity cloud model is developed to simulate the evolution mechanisms of fuzzy and complex regional systems of water security and overcome the uncertainty that is associated with the indices that are used in water safety index systems. This cloud generator is used to reciprocally transform a qualitative cloud image with a quantitative cloud characteristic value, and the stochastic weight assignment method is used to determine the weight of the evaluation indices. The results of case studies show that Jiansanjiang's water safety systems were in a safe state in 2002-2011, but the water safety systems in the arid area of Yinchuan City were in a dangerous state in 2006-2007 because of climate factors and a lack of effective water and soil resource protection. The experimental results are consistent with the research subjects' actual situations, and the proposed model provides a tool for decision makers to better understand the security issues that are associated with regional water safety systems.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder

With increased worldwide awareness of environmental issues, healthcare waste (HCW) management has received much attention from both researchers and practitioners over the past decade. The task of selecting the optimum treatment technology for HCWs is a challenging decision making problem involving conflicting evaluation criteria and multiple stakeholders. In this paper, we develop an integrated decision making framework based on cloud model and MABAC method for evaluating and selecting the best HCW treatment technology from a multiple stakeholder perspective. The introduced framework deals with uncertain linguistic assessments of alternatives by using interval 2-tuple linguistic variables, determines decision makers' relative weights based on the uncertainty and divergence degrees of every decision maker, and obtains the ranking of all HCW disposal alternatives with the aid of an extended MABAC method. Finally, an empirical example from Shanghai, China, is provided to illustrate the feasibility and effectiveness of the proposed approach. Results indicate that the methodology being proposed is more suitable and effective to handle the HCW treatment technology selection problem under vague and uncertain information environment.