bayesian belief networks
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2021 ◽  
pp. 118721
Author(s):  
Giulia Leone ◽  
Ana I. Catarino ◽  
Ine Pauwels ◽  
Thomas Mani ◽  
Michelle Tishler ◽  
...  

Author(s):  
Nguyễn Đăng Trình ◽  
Lê Thanh Tân ◽  
Phan Duy Lai ◽  
Trần Hoàng Gia ◽  
Trần Đức Học

Lựa chọn giá dự thầu hợp lý và xác định khả năng thắng thầu là một trong những vấn đề mang tính sống còn đối với các nhà thầu. Đây là một vấn đề rất khó vì trong hoạt động đấu thầu có sự đa dạng và phức tạp. Các nghiên cứu trước chỉ dựa vào dữ liệu quá khứ để đưa ra quyết định; tuy nhiên, dữ liệu quá khứ chỉ là một nhân tố ảnh hưởng đến quyết định tham gia dự thầu cần thêm các dữ liệu thông tin tổng hợp khác. Bài báo này trình bày phương pháp sử dụng mạng bayesian belief networks (BBNs) và lý thuyết trò chơi để xác định khả năng thắng thầu trong đấu thầu cạnh tranh. Để thực hiện các mục tiêu trên, Kết quả nghiên cứu đã tiến hành thu thập dữ liệu và phân tích, để đưa ra các nhân tố ảnh hưởng có thể đánh giá được bản thân và các đối thủ sẽ có xu hướng lựa chọn giá dự thầu. Đồng thời, mô hình cũng đánh giá sự tương tác của nhà thầu với các đối thủ tiềm năng để xác định giá dự thầu hợp lý, cũng như xác suất thắng thầu lớn nhất.


2021 ◽  
Vol 12 ◽  
Author(s):  
Shuang Zhou ◽  
Li Peng

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.


Author(s):  
Lusine Mkrtchyan ◽  
Ulrich Straub ◽  
Massimo Giachino ◽  
Thomas Kocher ◽  
Giovanni Sansavini

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6607
Author(s):  
Ali Behravan ◽  
Bahareh Kiamanesh ◽  
Roman Obermaisser

The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.


2021 ◽  
Author(s):  
Gerald Singh ◽  
Jonathan Rhodes ◽  
Even McDonald-Madden ◽  
Hugh Possingham ◽  
Edd Hammill ◽  
...  

Determining where environmental management is best applied, either through regulating single sectors of human activities or across sectors, is complicated by interactions between human impacts and the environment. In this article, we show how an explicit representation of human-environment interactions can help, via "impact networks" including activities (e.g. shipping), stressors (e.g. ship strikes), species (e.g. humpback whales) or ecosystem services (e.g. marine recreation). Impact networks can enable the identification of "leverage nodes", which, if present, can direct managers to the activities and stressors crucial for reducing risk to important ecosystem components. Exploring an impact network for a coastal ecosystem in British Columbia, Canada, we seek to identify these leverage nodes using a new approach employing Bayesian Belief Networks of risks to ecosystems. In so doing, we address three key questions: (1) Do leverage nodes exist? (2) Do management plans for species correctly identify leverage nodes? (3) Does the management of leverage nodes for certain species realize benefits for other species and ecosystem services? We show that there are several leverage nodes across all species investigated, and show that preconceptions about the regulation of risk to species can misidentify leverage nodes, potentially leading to ineffective management. Notably, we show that managing fisheries does not reduce overall risk to herring whereas managing diverse cumulative impacts including nutrient runoff, oil spills, and marine debris can reduce risk to herring, additional species, and related ecosystem services. Thus, by targeting leverage nodes, managers can efficiently mitigate risks for whole communities, ecosystems, and ecosystem services.


2021 ◽  
pp. 116039
Author(s):  
Cláudio Roberto do Rosário ◽  
Fernando Gonçalves Amaral ◽  
Fernando Jose Malmann Kuffel ◽  
Liane Mahlmann Kipper ◽  
Rejane Frozza

2021 ◽  
Author(s):  
Mads Troldborg ◽  
Zisis Gagkas ◽  
Andy Vinten ◽  
Allan Lilly ◽  
Miriam Glendell

Abstract. Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian Belief Networks (BBN) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small drinking water catchment (3.1 km2) with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; temporal variability of climatic and hydrological processes as well as uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (temperature, rainfall, evapotranspiration, overland and subsurface flow), soil properties (texture, organic matter content, hydrological properties), topography (slope, distance to surface water/depth to groundwater), land cover and agronomic practices, pesticide properties and usage. The effectiveness of mitigation measures such as delayed timing of pesticide application; 10 %, 25 % and 50 % reduction in application rate; field buffers; and presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, land use, presence of buffers, field slope and distance as the most important risk factors, alongside several additional influential variables. Pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, while groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of 50 % reduced pesticide application rate, management of plough pan, delayed application timing and field buffer installation notably reduced the probability of high-risk from overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of the BBN facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of ‘critical source areas’ of pesticide pollution in time and space in a data scarce catchment, with explicit representation of uncertainties.


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