Analysis of vertical differentiation of vegetation in Taishan World Heritage site based on cloud model

While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan's deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model-Expectation (EX), Entropy (EN), and Hyper-entropy (HE)-we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan's forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82-39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79-7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan's vegetation vertical differentiation.

Evaluation of land resources carrying capacity based on entropy weight and cloud similarity

Land is the foundation of human life and development, which is also the most important part of a country. The study of land carrying capacity is one of the important contents of land management, wherein the evaluation of land resource carrying capacity (LRCC) is an important reference for land resource planning. Aiming at the information fuzziness and uncertainty in the evaluation of LRCC, firstly, a comprehensive evaluation model based on entropy weight and normal cloud similarity was proposed, which is based on cloud model theory and combined with normal cloud similarity measurement method and entropy weight method. Secondly, taking the asphalt pavement experiment as an example for empirical analysis, the experimental results are consistent with the actual situation, which proves the feasibility and effectiveness of the proposed model. Finally, taking China's Chongqing city as the research area, the proposed evaluation model is used to study LRCC. The research results indicate that the comprehensive carrying capacity and average carrying capacity of various systems in China's Chongqing have been improved in the past decade. Among them, the comprehensive carrying capacity rose from the second level during the "12th Five-Year Plan" period to the third level during the "13th Five-Year Plan" period. In the future, it is necessary to focus on the improvement of soil and water resources system and economic and technological system. This conclusion reflects LRCC of Chongqing in China objectively and has a reference value for Chongqing's land planning.

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.

Research on safety evaluation of collapse risk in highway tunnel construction based on intelligent fusion

To solve the problems of untimely and low accuracy of tunnel project collapse risk prediction, this study proposes a method of multi-source information fusion. The method uses the PSO-SVM model to predict the surrounding rock displacement. With the prediction index as the benchmark, the Cloud Model (CM) is used to calculate the basic probability assignment value. At the same time, the improved D-S theory is used to fuse the monitoring data, the advanced geological forecast, and the tripartite information indicators of site inspection patrol. This method is applied to the risk assessment of Jinzhupa Tunnel, and the decision-makers adjust the risk factors in time according to the prediction level. In the end, the tunnel did not collapse on a large scale.

Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

The comprehensive risk assessment of the Tangjiashan landslide dam incident, China

Risk assessment for landslide dams is very important to avoid unanticipated landslide failure and calamity. Recognition of the risk of landslide dams associated with changing influencing factors is to identify the risk grade and provide early warning of oncoming failure, while quantitative risk analysis of landslide dams due to many influencing factors changing in spatiotemporal domain is currently lacking. We applied the model to analyze the risk level of the Tangjiashan landslide dam caused by the Wenchuan Ms 8.0 earthquake. The risk evaluation, obtained according to the analysis of the influencing factors located in the risk assessment grade criteria, clearly shows that the risk reaches a higher level at that moment. Our analysis shows that the risk level of landslide dams can be quantitatively analyzed with our assessment method. Our results suggest that the risk assessment system can be an effective measure to dynamically predict the risk level and provide a sufficient early warning of the oncoming hazard by analyzing the variables of influencing factors at different times.

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.

A new method for subsea tunnel site selection based on environmental bearing capacity

Engineering site selection is an essential and systematic work in the early engineering construction stage. At present, the subsea tunnel site selection mainly depends on manual experience. There is still a lack of subsea tunnel site selection systems based on environmental impact. This study develops a comprehensive site selection evaluation system based on the analytic hierarchy process (AHP) and fuzzy evaluation method for the subsea tunnel site selection. It is a multi-indicator mathematical model evaluation system. On this basis, the ecological site selection method of the subsea tunnel is further studied, an indicator system for evaluating the environmental carrying capacity of the island is established, and the site selection results of the subsea tunnel based on the environmental indicators are obtained. We compared the site selection results of the two methods. The results show that the conventional method and the ecological site selection method based on environmental indicators can well carry out the site selection of subsea tunnels. The two methods take into account both the overall and local optimum of the subsea tunnel route and organically combine the overall and local objectives. This way provides a reference for the design and construction of the subsea tunnel in the future and points out the direction for the site selection of other large-scale projects with significant environmental impact.

Assessing progress towards sustainable development goals for Chinese urban land use: A new cloud model approach

Rapid urbanization poses great challenges to China's urban land use sustainability (ULUS). Land is the essential space to achieve the Sustainable Development Goals (SDGs) of the United Nations, so SDGs provide a new guide to evaluate land use sustainability. However, there is still a lack of SDGs-oriented assessment of urban land use at national level. Moreover, there is still a need to address the problems about the randomness and fuzziness within evaluation, which tends to cause more uncertainties. Here we developed a SDGs-oriented evaluation framework based on the cloud model and derived the spatial and temporal patterns of urban land use sustainability for China at the prefecture-level from 2004 to 2019. Then, we used the McKinsey matrix to classify the types of urban land use sustainability, and examined their main drivers using the Geodetector method. The results showed that the development level of ULUS in China was high in the east and low in the west. High-value hotspots were mainly distributed in primary and secondary urban agglomerations in China. From 2004 to 2019, the development level of ULUS in China gradually increased, but the growth rate slowed down. In 2009 the value of central China exceeded that of the northeast. In contrast, the coordination level of ULUS had declined in more than 50% of Chinese cities during the study period. The high values were in southern China, northeast China, and Chengdu-Chongqing urban agglomeration, while the low values were in central and southern Liaoning and the urban agglomeration in the Central Plains. The development level was mainly controlled by anthropogenic activities and urban development, while natural conditions constrained the improvement of the coordination level. Combining the development and coordination, we found that cities with higher development level often had a wide range of coordination level, and suggestions were put forward for different regions to achieve sustainable land use. Our research provides scientific guidance for China's territory planning and sustainable urban development.

Resilience assessment of metro stations against rainstorm disaster based on cloud model: a case study in Chongqing, China

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations' resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations' resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

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.

Uncertainty prediction of mining safety production situation

In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R, and Up three granular parameter sequences, according to the triangular fuzzy number; then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R, and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining, and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R, and Up sequences were 9.472 %, 16.671 %, and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity, and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.

Evaluating the impacts of small cascade hydropower from a perspective of stream health that integrates eco-environmental and hydrological values

With small cascade hydropower projects (SCHPs) increasingly employed in small and medium rivers, methods to assess changes in health status within the stream system have become essential to river ecological environment management. In this study, we used a cloud based fuzzy evaluation method to synthetically diagnose the health status of a stream, both as a whole and its parts (hydrological regime, riparian landscape, aquatic community, water quality, and social demand), under the impacts of SCHPs. The results indicated that: (1) average maximum and minimum flows decreased by 20% and 10% respectively, since SCHPs were implemented. Furthermore, the 38% increase in low flow frequency indicated that SCHPs might amplify droughts, the opposite of large hydropower projects which have been shown to alleviate drought; (2) implementation of SCHPs enhanced heterogeneity and fragmentation in riparian landscapes and decreased diversity of riparian vegetation, and dominant species were more likely to emerge on the upstream side of dam; (3) diversity of phytoplankton, zooplankton, and benthic animals decreased by 14%, 4%, and 16%, respectively, during high-impact period (HIP); and fish species decreased by 26% with a shift from rapid flow adapted to lentic and slow flow adapted species; and (4) the stream still exhibited a healthy state during HIP, but the degree of certainty belonging to "healthy" decreased from 0.279 to 0.192, indicating that the stream health was nearing a deteriorated state. This evaluation model clarified imperceptible and fuzzy changes in stream health which will be helpful in follow-up management decisions.

Assessment of coal mining land subsidence by using an innovative comprehensive weighted cloud model combined with a PSR conceptual model

Research on land subsidence is a global topic. In recent years, the environmental problems caused by coal mining have received great attention. In particular, mining land subsidence caused damage to villages, buildings, farmland, etc., which seriously threatened the mining area's living environment and ecological environment. This study proposes a pressure-state-response concept model based on mining land subsidence to build an evaluation index system in coal mines. Based on this index system, given the uncertainty in the evaluation process, the cloud model is used to represent the index weight and comprehensive evaluation calculations, which fully consider the randomness and ambiguity in the evaluation process. The mining land subsidence of several mining areas in China was evaluated and classified into three grades (slight-medium-strong). The cloud model assessment results are compared with the result of the probability integration method and the actual situation. The assessment results of the cloud model are closer to the actual situation than the probability integration method. This shows that the established mining land subsidence evaluation method based on the cloud model in this study is reasonable and feasible. The mining width and height ratio, depth and height ratio, and coal seam dip angle affect mining land subsidence. Therefore, improving the mining method to deal with the goaf reasonably and optimizing the mining design to control the influence range of mining are essential measures to reduce mining land subsidence and protect the ecological environment of mining areas.

Lake ecosystem health assessment using a novel hybrid decision-making framework in the Nam Co, Qinghai-Tibet Plateau

Lake health assessment (LHA), a powerful tool for lake ecological protection, provides the foundation for sustainable water environment management. However, existing methods have not yet considered the effects of fuzziness and randomness on LHA. In addition, most of the current studies on LHA focus on the plain areas, lack of quantitative studies in mountain areas, such as the Qinghai-Tibet Plateau. The Pythagorean fuzzy cloud (PFC) integration algorithm drawing on the advantages of Pythagorean fuzzy sets (PFS) and cloud model was proposed. A novel hybrid decision-making framework combining PFC integration algorithm and TOPSIS model was developed to determine the lake health levels with fuzziness and randomness. An indicator system incorporating ecosystem integrity (physical habitat, water quantity and quality, aquatic life) and non-ecological performance (social services) was established. To comprehensively investigate the lake health level in the Qinghai-Tibet Plateau, the Nam Co was selected as study area. Our results confirm that the developed framework in this study can overcome the shortcomings of existing methods and provide a more effective approach for LHA with fuzziness and randomness. In Nam Co, the non-ecological performance was significantly better than the ecosystem integrity. Health levels exhibited a remarkable spatial variation influenced by tourism and grazing, with decreasing health status from the northwestern to southeastern Nam Co. Approximately 85% of the sampling sites were at excellent or healthy levels, 15% were subhealthy, and no sampling sites were unhealthy and sick. Our results highlight that tourism has affected health levels at Nam Co, and effective measures are needed to minimize the impact in ecological fragile areas.

Improving the ecological environmental performance to achieve carbon neutrality: The application of DPSIR-Improved matter-element extension cloud model

Ecological environmental issues are global focus problems. Achieving carbon neutrality is a fundamental measure to protect the ecological environment. Government environmental governance plays a key role in the process of moving towards a carbon neutral vision since the externality of the environment. Therefore, it is of great significance for achieving the goal of carbon neutrality to evaluate the governmental ecological environment performance and propose improved suggestions based on the evaluation results. However, most existing evaluation methods have the disadvantages that it is difficult to avoid information distortion and loss during the evaluation process. To overcome these problems, an evaluation model based on a DPSIR-improved matter-element extension cloud model is proposed in this study, which combines the Drivers-Pressures-State-Impact-Response (DPSIR) model, the entropy weight method, the cloud model, matter element extension theory, and the cloud entropy optimization algorithm. The ecological environmental performance of China in 2019 was evaluated based on the proposed model, exampling as Jiangsu province. The results showed that the cloud digital eigenvalues of ecological environmental performance was (2.1852, 0.2956, 0.1), indicating that the ecological environmental performance was good level. However, the ambient air quality needed to be improved. To achieve carbon neutrality, suggestions including strengthening propagating, increasing investment, optimizing the industrial structure, and building a modern environmental governance system are proposed.

Discriminating three motor imagery states of the same joint for brain-computer interface

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

Assessment on water resources carrying capacity in karst areas by using an innovative DPESBRM concept model and cloud model

The shortage of water resources in karst areas is mainly caused by the development of karst landforms, poor availability of water resources and the difficulty of utilization. To reasonably evaluate water resources carrying capacity (WRCC) of karst areas, based on characteristics of urban water resources utilization in karst areas, this study put forward DPESBRM (Driver-Pressure-Engineering water shortage-State-Ecological basis-Response-Management) concept model the first time to build an urban evaluation index system of WRCC in karst areas. Based on this index system and in allusion to uncertainties that exist during the evaluation process, a cloud model is used to represent index weights and perform comprehensive evaluation calculations, which fully considers the randomness and ambiguity of evaluation objects. WRCC from 2009 to 2018 were evaluated and were classified as five grades (Serious overload - Overload - Critical - Weak carrying capacity - Strong carrying capacity). Results proved that WRCC had improved year after year, gradually changing from a serious overload state in 2009 to a strong carrying capacity state in 2018. 2009 and 2016 were classified as I grade (serious overload). 2010 and 2011 were classified as II grade (overload). 2012, 2013 and 2015 were classified as IV grade (weak bearing capacity). 2014, 2017 and 2018 were classified as V grade (strong bearing capacity). Cloud model assessment results are compared with that of TOPSIS method, and assessment results are basically unanimous. It shows that the established WRCC evaluation method based on cloud model in this study is reasonable and feasible. Population density, urbanization rate and per capital water consumption are important driving factors affecting WRCC. Hence, strengthening the construction of water conservancy facilities, optimizing the water consumption structure, improving the efficiency of industrial water use, reducing per capital water consumption, and narrowing urban water supply and demand gap are important measures to ensure WRCC.

Comprehensive evaluation on water resource carrying capacity in karst areas using cloud model with combination weighting method: a case study of Guiyang, southwest China

It is important to maintain the sustainable development of water resources. Objective assessment on water resource carrying capacity (WRCC) is beneficial to the formulation of scientific and reasonable water management practices. In view of the problem that evaluation indicators of WRCC cannot describe the fuzziness and randomness, a cloud model was introduced into regional WRCC assessment. This study selected a typical karst area (Guiyang) as the research object to study WRCC by using cloud model with combination weighting method. WRCC was assessed from the following five dimensions: water environment subsystem, social subsystem, economic subsystem, ecological subsystem, and humanities (water resource management and policy regulation) subsystem. In addition, evaluation results after normalizing all of indicators data were also calculated. And these two kinds of evaluation results were compared with that of technique of order preference similarity to the ideal solution (TOPSIS), finding that evaluation results of cloud model were consistent with that of TOPSIS method. The cloud model realizes the transformation from qualitative evaluation to quantitative evaluation, which overcome insufficiencies of traditional evaluation methods in considering fuzziness and randomness. Results showed that during the period of 2008-2017, the state of WRCC in Guiyang was improving year by year, increasing from the serious overload carrying capacity level in 2008 to the strong carrying capacity level in 2017 (serious overload-overload-critical-weak carrying capacity-strong carrying capacity). However, some certain evaluation indicators are still in danger situation, such as population natural growth rate and use of the fertilizer per unit cultivated area, which needs to be further enhanced and improved. Moreover, the contradiction among economic development, population growth, and water resources is becoming increasingly apparent. To ensure the effective utilization of water resources in Guiyang, reasonable policies and measures should be formulated and put into effect. Research results could provide certain reference for the sustainable development of regional water resources.

Dynamic early warning of regional atmospheric environmental carrying capacity

Economic development cannot exceed the maximum amount that the environment can support. Therefore, atmospheric environmental policy should be formulated based on the scientific assessment of regional atmospheric environmental carrying capacity. The establishment of an early warning model of atmospheric environmental carrying capacity can dynamically analyse regional atmospheric environmental carrying capacity, which contributes to discerning the change trend of the regional atmospheric environmental carrying capacity and the risk issue of the regional atmospheric environment. Additionally, it can provide theoretical reference for the formulation of relevant binding and restrictive policies. In this study, according to the daily monitoring data of atmospheric pollutants, we established a dynamic early warning model of regional atmospheric environmental carrying capacity based on the cloud model and Markov chain. The research results show that this model has an excellent early warning capability. Moreover, many regions in China have exceeded the atmospheric environmental carrying capacity, especially in North China and Central China. By 2020, North China and Central China for prediction of region with non-overloading are only 9.09% and 12.50%, respectively. China's regional atmospheric environmental carrying capacity is gradually improving. It is predicted that by 2024, regions with non-overloading in North China and Central China will reach 40.91% and 37.50%, respectively. From the overall aspect, there is currently no risk of serious overload in any region.

An innovative method based on cloud model learning to identify high-risk pollution intervals of storm-flow on an urban catchment scale

Identifying high-risk storm-flow pollution intervals in an urban watershed is critical for watershed pollution control decision-making. High-risk pollution intervals of storm-flow are defined as storm-flow intervals that contribute more than the background pollutant load, and whose load contribution rank in the top 20%. However, the identification of high-risk pollution intervals is difficult due to variations in the flow-concentration relationship among rain events, uncertainty inherent in stormwater quality data, and physically-based stormwater models requiring a substantial number of parameters. A new method for identifying high-risk pollution intervals during different rain events is proposed. A dataset of the urban watershed located in Shenzhen, southern China, was used to demonstrate the proposed method. A "cut-pool" strategy was initially used to pre-process the dataset for maximizing valuable information hidden in existing datasets and to investigate the impact of rainfall on flow-concentration relationships. Gaussian cloud distribution was then introduced to capture the trend, dispersing extent and randomness of stormwater quality data at any flow interval. Interval Overlapping Ratio (IOR) and Load contribution of storm-flow high-risk pollution intervals was used to assess the performance of the method. Results show that storm-flow high-risk Chemical Oxygen Demand (COD) pollution intervals of the Shiyan watershed was 0.5-1.5 mm under light rain (0-13 mm), 1-3 mm under moderate rain (13-27 mm) and 5-7 mm under heavy rain (27-43 mm). The accuracy of the identified high-risk pollution intervals (IOR) was 63-66% under light rain, 64-67% under moderate rain. Moreover, COD load can be reduced by 44-48% with high-risk storm-flow under light rain; 43-49% under moderate rain; 32% under heavy rain. This method is very useful for effectively controlling storm-flow pollution on an urban catchment scale.

The evaluation model of the enterprise energy efficiency based on DPSR

The reasonable evaluation of the enterprise energy efficiency is an important work in order to reduce the energy consumption. In this paper, an effective energy efficiency evaluation index system is proposed based on DPSR (Driving forces-Pressure-State-Response) with the consideration of the actual situation of enterprises. This index system which covers multi-dimensional indexes of the enterprise energy efficiency can reveal the complete causal chain which includes the "driver forces" and "pressure" of the enterprise energy efficiency "state" caused by the internal and external environment, and the ultimate enterprise energy-saving "response" measures. Furthermore, the ANP (Analytic Network Process) and cloud model are used to calculate the weight of each index and evaluate the energy efficiency level. The analysis of BL Company verifies the feasibility of this index system and also provides an effective way to improve the energy efficiency at last.

Multi-criteria decision-making on assessment of proposed tidal barrage schemes in terms of environmental impacts

For tidal range power plants to be sustainable, the environmental impacts caused by the implement of various tidal barrage schemes must be assessed before construction. However, several problems exist in the current researches: firstly, evaluation criteria of the tidal barrage schemes environmental impact assessment (EIA) are not adequate; secondly, uncertainty of criteria information fails to be processed properly; thirdly, correlation among criteria is unreasonably measured. Hence the contributions of this paper are as follows: firstly, an evaluation criteria system is established from three dimensions of hydrodynamic, biological and morphological aspects. Secondly, cloud model is applied to describe the uncertainty of criteria information. Thirdly, Choquet integral with respect to λ-fuzzy measure is introduced to measure the correlation among criteria. On the above bases, a multi-criteria decision-making decision framework for tidal barrage scheme EIA is established to select the optimal scheme. Finally, a case study demonstrates the effectiveness of the proposed framework.

Extraction and representation of common feature from uncertain facial expressions with cloud model

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

A novel water quality data analysis framework based on time-series data mining

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data.

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.

A cloud model-based approach for water quality assessment

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.

Cloud-based decision framework for waste-to-energy plant site selection - A case study from China

Waste-to-energy (WtE) plant site selection is crucially important during the whole life cycle. Currently, the scholars launch some research in the WtE plant site selection. However, there are still two great problems in the present methods. Firstly, the uncertainty of information is not fully described. Secondly, the correlation among criteria lacks rationality, which is mainly manifested in two aspects: one is ignoring the correlation, and the other is measuring unreasonably. Firstly cloud model is introduced to describe the fuzziness and randomness of the information fully and precisely. Secondly, the 2-order additive fuzzy measures based on the Mobius transform and correlation coefficient matrix is introduced for fuzzy measure scientifically and reasonably. Thirdly, Cloud Choquet integral (CCI) operator is constructed to evaluate the alternatives. Finally, a case from China proves the effectiveness.

A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem

A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP.