Urban Stormwater Resilience Assessment Method Based on Cloud Model and TOPSIS.
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ABSTRACT:
SUBMITTER: Qiao H
PROVIDER: S-EPMC8751000 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cities are important barriers to protect people's lives and property in the face of natural disasters, economic fluctuations and epidemic diseases. The evaluation of urban resilience is a hybrid multiple attribute group decision-making problem involving both crisp and fuzzy indicators. In order to evaluate the urban resilience reasonably and quantitatively, an urban resilience evaluation index system is established, including four primary indicators of ecological environment, municipal facilities, economic development and social development, and 28 secondary indicators. An evaluation model based on the theory of intuitionistic fuzzy set and TOPSIS method is proposed. The intuitionistic trapezoidal fuzzy number is used to quantify the fuzzy index and determine the weights of experts. The weight of each index is determined based on the maximizing deviation method. The relevant data of Dalian City from 2013 to 2017 are collected to evaluate the city resilience, and a sensitivity analysis is carried out based on the proposed model. The results may provide insights for the further urban resilience promotion.
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Project description:To effectively control air pollution, it is necessary to obtain a preliminary assessment of air quality. The purpose of this study was to introduce a cloud model method in air pollution assessment. First, the standard cloud models of air pollution indicators were obtained, and the calculating process of numerical characteristics employed by the standard cloud model was explained. Second, the levels of air pollution indicators were presented based on the qualitative and quantitative analysis of cloud models, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account. Air quality assessment results including SO2, NO2, CO, O3, PM10 and PM2.5 were analysed. Third, the cloud model adopted in the assessment process of air quality was validated by grey relational analysis, and the results confirmed the validity of cloud model assessment. Fourth, the air pollution level of the air quality index (AQI) was determined, and the fuzziness and randomness of the assessment results were thoroughly analysed by taking entropy and hyper entropy into consideration. Fifth, seasonal variations in different air pollution indicators were analysed to proffer a series of recommendations for government policy decision-makers and travellers. The cloud model provided a new method for air quality assessment.
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Project description:This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.
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Project description:Risk events frequently occur in "complex urban public spaces" (CUPSs) and cause serious economic losses and casualties. To reduce the risks and enhance the system resilience, this paper formulates a theoretical framework to assess the resilience of CUPSs. Resilience is defined as the ratio of preparedness to vulnerability, according to the implication of the concept. Three-level practical indicator systems were established for these two dimensions, respectively. Furthermore, a hybrid approach combining the Analytic Network Process (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was adopted. The Chongqing West Railway Station (the Station (W)) and the Lianglukou Rail Transit Station (the Station (L)) were used for a case study. The results showed that the Chongqing West Railway Station was more resilient to risks than the Lianglukou Rail Transit Station. Therefore, the proposed theoretical framework could be applied in assessing the resilience level of CUPSs. Resilience improvement strategies can be formulated according to the assessment results. Furthermore, the practical indicators could also provide references for urban disaster management.
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Project description:Urban floods caused by expanding impervious areas due to urban development and short-term heavy precipitation adversely affect many coastal cities. Notably, Seoul, one of the coastal cities that experiences acute urban floods, suffers annually from urban floods during the rainfall season. Consequently, to mitigate the impacts of urban floods in Seoul, we established flood-vulnerable areas as target areas where green infrastructure planning was applied using the Stormwater Runoff Reduction Module (SRRM). We selected the Gangdong, Gangbuk, and Dobong districts in Seoul, Korea, all of which demonstrate high flood vulnerability. Analyses in reducing the runoff amount and peak time delay effect were estimated by model simulation using the SRRM. The reduction in peak discharge for the whole area occurred in the following order: Gangdong district, then Gangbuk district, and lastly Dobong district. In contrast, the reduction in peak discharge per unit area was most prominent in Gangbuk district, followed by Dobong and Gangdong districts. However, the delay effect was almost identical in all target areas. Based on the simulation results in this study, we planned green infrastructure, including green roofs, infiltration storage facilities, and porous pavement. We believe that the results of this study can significantly enhance the efficiency of urban flood restoration and green infrastructure planning in coastal cities.Supplementary informationThe online version contains supplementary material available at 10.1007/s11069-022-05477-7.
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Project description:Due to the problems related to the numerous factors affecting the spontaneous combustion of goaf coal, such as sudden, uncertain, and dynamic changes, and the fact that the weight of the indexes in the prediction model of the spontaneous combustion risk is difficult to determine, an improved Criteria Importance Through Inter-criteria Correlation (CRITIC) modified Technique for Order of Preference by Similarity to Ideal Solution G2-(TOPSIS) dynamic prediction model of goaf spontaneous combustion was developed. An optimal decision-making model was established by introducing the Euclidean distance function, and the function-driven type G2 weighting method was modified using the differential-driven type weighting method of the CRITIC. In addition, the comprehensive weights of each index were obtained. An update factor was introduced to obtain the dynamic weight, and the primary-secondary relationship of the risk factors affecting the spontaneous combustion of goaf was evaluated. Based on the G2 weighting method, which approximates the driving function principle of the ideal solution ranking method (TOPSIS), a G2-TOPSIS goaf spontaneous combustion risk assessment model was established. The degree of closeness was analyzed and the risk grade of the goaf spontaneous combustion was finally predicted. The sub-model was applied to the goaf of working face 1303 in the Jinniu Coal Mine. It was concluded that the air leakage duration was the dominant factor inducing the risk of the spontaneous combustion of the goaf. The risk grade of spontaneous combustion of the goaf is Class III, and the predicted results are consistent with the actual situation. The improved CRITIC-G2-TOPSIS dynamic model was demonstrated to be scientific in predicting the goaf spontaneous combustion risk, and these research results have important popularization and application value.
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Project description:Urban sewer systems consist of wastewater and stormwater sewers, of which only wastewater is processed before being discharged. Occasionally, misconnections or damages in the network occur, resulting in untreated wastewater entering natural water bodies via the stormwater system. Cultivation of faecal indicator bacteria (e.g. Escherichia coli; E. coli) is the current standard for tracing wastewater contamination. This method is cheap but has limited specificity and mobility. Here, we compared the E. coli culturing approach with two sequencing-based methodologies (Illumina MiSeq 16S rRNA gene amplicon sequencing and Oxford Nanopore MinION shotgun metagenomic sequencing), analysing 73 stormwater samples collected in Stockholm. High correlations were obtained between E. coli culturing counts and frequencies of human gut microbiome amplicon sequences, indicating E. coli is indeed a good indicator of faecal contamination. However, the amplicon data further holds information on contamination source or alternatively how much time has elapsed since the faecal matter has entered the system. Shotgun metagenomic sequencing on a subset of the samples using a portable real-time sequencer, MinION, correlated well with the amplicon sequencing data. This study demonstrates the use of DNA sequencing to detect human faecal contamination in stormwater systems and the potential of tracing faecal contamination directly in the field.
| S-EPMC6085348 | biostudies-literature
Project description:Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
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Project description:Ensuring urban areas have access to clean drinking water, safe food supply, and uncontaminated water bodies is essential to the good health of millions of urban residents. This paper presents the functionality of Iron Filings-based Green Environmental Media (IFGEM) in terms of nutrient removal efficiencies to support water quality management and urban farming. IFGEM uses recycled materials such as tire crumb and iron filings to help remove nutrients with essential physicochemical properties. In this study, IFGEM were proven effective and sustainable through an isotherm study, a column study of reaction kinetics, and a microstructure examination under various inlet nutrient concentration levels. IFGEMs exhibited over 90% nitrate removal, as well as 50-70% total phosphorus removal, under most inlet conditions. These promising results make IFGEM suitable for treating stormwater runoff, wastewater effluent, and agricultural discharge via varying ex situ treatment units in flexible landscape environments. In addition, the byproduct of ammonia generation permits possible reuse of spent IFGEM as soil amendments in crop land, gardens and yards, and green roofs for urban farming. Findings may help secure urban food supply chains and harmonize nutrients, soil, water, and waste management in different urban environments.
| S-EPMC7287050 | biostudies-literature
Project description:BackgroundIn recent times, the use of health technologies in the diagnosis and treatment of diseases experienced considerable and accelerated growth. The goal of the present study was to describe the designated pilot MCDM (Multiple Criteria Decision Making) model for priority setting of health technology assessment in Iran.MethodsRelevant articles were sought and retrieved from the most appropriate medical databases, including the Cochrane Library, PubMed and Scopus via three separate search strategies, using MESH and free text until March, 2015. Retrieved criteria were questioned from health technology assessment experts in two rounds and the relative weight for valid criteria was finally obtained from paired wise comparison method. After extraction of relative weights based on the aforementioned procedure, TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) priority setting model was designed. The stated model was applied for assessing three technologies (adenosine, tissue plasminogen activator and mechanical thrombectomy) which were available for projects call of Iranian health technology assessment department in order to determine applicability of the model for practical purpose.ResultsNine criteria, including efficiency/effectiveness, safety, population size, vulnerable population size, availability of alternative technologies, cost effectiveness in other countries, budget impact, financial protection, quality of evidence, were extracted by the Iranian health technology assessment experts. The relative weights of these criteria were as follows 0.12, 0.2, 0.06, 0.08, 0.08, 0.13, 0.08, 0.09, and 0.15, respectively. Finally TOPSIS pilot model was designed by three health technologies and nine criteria relative weights. Results showed that, the applicability of the stated model was suitable and as the pilot testing, tissue plasminogen activator was the first priority, adenosine was second and mechanical thrombectomy was third for performing health technology assessment by the Iranian ministry of health and medical education.ConclusionAccording to the results of this study, this model with nine effective criteria and their relative weights and in combination with TOPSIS approach could be used with suitable applicability by health technology assessment department in deputy of curative affairs and food and drug organization for determination of research priorities in health technology assessment.
| S-EPMC4827190 | biostudies-literature