Design concept evaluation based on rough number and information entropy theory

Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes

Integrating Social Impact Assessment and Environmental Conflict Analysis on a Hydrocarbon Exploration Project in Spain

[EN] Social impact assessment (SIA) has become a key factor for environmental conflicts prevention, which makes necessary to integrate SIA and environmental conflict analysis (ECA). In this article, we integrate SIA and ECA using a method based on grey systems and Shannon entropy. A case study was conducted on a hydrocarbon exploration project located in the Sea of the Gulf of Valencia, Spain. Four stakeholder groups and four evaluation criteria were identified. The results revealed that for group of specialists the project would have negative social impact; and contrary perceptions were found between the group ofprimary activities populationand the group of retirees. It was also noted that the criteria most likely to generate environmental conflicts were the percentage of unemployment and the GDP per capita. These results could help central and community governments to make the best decision on the project. The method showed interesting results and could be apply to manage other projects or programs from point of view of social factors.Delgado-Villanueva, KA.; Romero Gil, I. (2016). Integrating Social Impact Assessment and Environmental Conflict Analysis on a Hydrocarbon Exploration Project in Spain. International Journal of Engineering and Technology (Online). 8(6):2403-2417. doi:10.21817/ijet/2016/v8i6/160806402S240324178

Determine of Surface Water Quality Index in Iran

In modeling complex of environmental problems, researchers often fail to define precise statements about input and outcomes of contaminants, but fuzzy logic could help to dominate this logical indecision. The goal of this work is to propose a new river water quality indicator using fuzzy logic. The proposed index combines six indicators, and not only does it exhibit a tool that accounts for the discrepancy between the two base indices, but also provides a quantifiable score for the determined water quality. These classifications with a membership grade can be of a sound support for decision-making, and can help assign each section of a river a gradual quality sub-objective to be reached. To show the applicability of the proposed approach, the new indicator was used to classify water quality in a number of stations along the basins of Qarah-chai and Siminehrood. The obtained classifications were then compared to the conventional physicochemical water quality indicator currently in use in Iran. The results revealed that the fuzzy indicator provided stringent classifications compared to the conventional index in 38% and 44% of the cases for the two basins respectively. These noted exceptions are mainly due to the big disagreement between the different quality thresholds in the two standards, especially for fecal coliform and total phosphorus. These large disparities put forward an argument for the Iranian water quality law to be upgraded. Keywords: Fuzzy logic; Qarah-chai basin; Siminehrood; Water quality inde

Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index (NDVI), landuse, rainfall, distance to road, distance to river, distance to fault, plan curvature, and profile curvature, were analyzed. Chi-square feature selection method was adopted to compare the significance of each factor with landslide occurence. The GWR model was compared with two well-known models, namely, logistic regression (LR) and support vcector machine (SVM). Results of chi-square feature selection indicated that lithology and slope are the most influencial factors, whereas SPI was found statistically insignificant. Four landslide susceptibility maps were generated by GWR, SGD-LR, SGD-SVM, and SVM models. The GWR model exhibited the highest performance in terms of success rate and prediction accuracy, with values of 0.789 and 0.819, respectively. The SVM model exhibited slightly lower AUC values than that of the GWR model. Validation result of the four models indicates that GWR is a better model than other widely used models

A systematic review of application of multi-criteria decision analysis for aging-dam management

[EN] Decisions for aging-dam management requires a transparent process to prevent the dam failure, thus to avoid severe consequences in socio-economic and environmental terms. Multiple criteria analysis arose to model complex problems like this. This paper reviews specific problems, applications and Multi-Criteria Decision Making techniques for dam management. Multi-Attribute Decision Making techniques had a major presence under the single approach, specially the Analytic Hierarchy Process, and its combination with Technique for Order of Preference by Similarity to Ideal Solution was prominent under the hybrid approach; while a high variety of complementary techniques was identified. A growing hybridization and fuzzification are the two most relevant trends observed. The integration of stakeholders within the decision making process and the inclusion of trade-offs and interactions between components within the evaluation model must receive a deeper exploration. Despite the progressive consolidation of Multi-Criteria Decision Making in dam management, further research is required to differentiate between rational and intuitive decision processes. Additionally, the need to address benefits, opportunities, costs and risks related to repair, upgrading or removal measures in aging dams suggests the Analytic Network Process, not yet explored under this approach, as an interesting path worth investigating.This research was funded by the Spanish Ministry of Economy and Competitiveness along with FEDER funding (Projects BIA201456574-R and ECO2015-66673-R).Zamarrón-Mieza, I.; Yepes, V.; Moreno-Jiménez, JM. (2017). A systematic review of application of multi-criteria decision analysis for aging-dam management. Journal of Cleaner Production. 147:217-230. https://doi.org/10.1016/j.jclepro.2017.01.092S21723014

Machine Learning-Based Method for Urban Lifeline System Resilience Assessment in GIS*

System resilience, the capability of a system to sustain and recover from deliberate attacks, accidents, or naturally occurring threats or incidents, is a key property to measure the degree of robustness and coupling effect of complex system. The systems of waste disposal, urban water supply, and electricity transmission are typical systems with complex and high coupling features. In this chapter, a methodology for measuring the system resilience of such systems is proposed. It is a process of integrated decision-making which contains two aspects: (1) a five-dimensional indicator framework of system resilience which includes attributes in infrastructural, economic, and social sectors and (2) a hybrid K-means algorithm, which combines entropy theory, bootstrapping, and analytic network process. Through utilizing real data, the methodology can assist to identify and classify the level of system resilience for different geographical regions which are sustained by lifeline systems. The calculation of algorithm, visualization of processed data, and classification of resilience level can be finally realized in geographic information system. Through utilizing by regional governments and local communities, the final result can serve to provide guideline for resource allocation and the prevention of huge economic loss in disasters

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas