scholarly journals Exploring a Flooding-Sensors-Agnostic Prediction of the Damage Consequences Based on Machine Learning

2021 ◽  
Vol 9 (3) ◽  
pp. 271
Author(s):  
Luca Braidotti ◽  
Marko Valčić ◽  
Jasna Prpić-Oršić
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Machine Learning Algorithms ◽  
Damage Scenario ◽  
Passenger Ships ◽  
Bagged Decision Trees ◽  
Emergency Decision ◽  
New Generation

Recently, progressive flooding simulations have been applied onboard to support decisions during emergencies based on the outcomes of flooding sensors. However, only a small part of the existing fleet of passenger ships is equipped with flooding sensors. In order to ease the installation of emergency decision support systems on older vessels, a flooding-sensor-agnostic solution is advisable to reduce retrofit cost. In this work, the machine learning algorithms trained with databases of progressive flooding simulations are employed to assess the main consequences of a damage scenario (final fate, flooded compartments, time-to-flood). Among the others, several classification techniques are here tested using as predictors only the time evolution of the ship floating position (heel, trim and sinkage). The proposed method has been applied to a box-shaped barge showing promising results. The promising results obtained applying the bagged decision trees and weighted k-nearest neighbours suggests that this new approach can be the base for a new generation of onboard decision support systems.

scholarly journals Effect of Database Generation on Damage Consequences’ Assessment Based on Random Forests

2021 ◽  
Vol 9 (11) ◽  
pp. 1303
Author(s):  
Luca Braidotti ◽  
Jasna Prpić-Oršić ◽  
Marko Valčić
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Decision Support ◽  
Decision Support Systems ◽  
Random Forests ◽  
Support Systems ◽  
Probability Distributions ◽  
New Generation ◽  
Main Effects ◽  
Further Development

Recently, the application of machine learning has been explored to assess the main damage consequences without employing flooding sensors. This method can be the base of a new generation of onboard decision support systems to help the master during the progressive flooding of the ship. In particular, the application of random forests has been found suitable to assess the final fate of the ship and the damaged compartments’ set and estimate the time-to-flood. Random forests have to be trained using a database of precalculated progressive flooding simulations. In the present work, multiple options for database generation were tested and compared: three based on Monte Carlo (MC) sampling based on different probability distributions of the damage parameters and a parametric one. The methods were tested on a barge geometry to highlight the main effects on the damage consequences’ assessment in order to ease the further development of flooding-sensor-agnostic decision support systems for flooding emergencies.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2353 ◽  
Author(s):  
Eta S. Berner ◽  
Bunyamin Ozaydin
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

New spatial decision support systems for sustainable urban and regional development

2015 ◽  
Vol 4 (1) ◽  
pp. 45-66 ◽  
Author(s):  
Patrizia Lombardi ◽  
Valentina Ferretti
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Case Studies ◽  
Support Systems ◽  
Spatial Decision Support ◽  
Content Type ◽  
New Generation ◽  
Support Decision Making ◽  
Regeneration Processes

Purpose – Policy makers are frequently challenged by the need to achieve sustainable development in cities and regions. Current decision-making processes are based on evaluation support systems which are unable to tackle the problem as they cannot take a holistic approach or a full account of actors. The purpose of this paper is to present a new generation of evaluation systems to support decision making in planning and regeneration processes which involve expert participation. These systems ensure network representation of the issues involved and visualization of multiple scenarios. Design/methodology/approach – A literature review is used for both revising existing evaluation tools in urban planning and the built environment and highlighting the need to give stakeholders (industry, cities, operators, etc.) new tools for collaborative or individual decisions and to facilitate scaling up solutions. An overview of the new generation of decision support systems, named Multicriteria Spatial Decision Support Systems (MC-SDSS) is provided and real case studies are analyzed to show their ability to tackle the problem. Findings – Recent research findings highlight that decisions in urban planning should be supported by collaborative and inclusive processes. Otherwise, they will fail. The case studies illustrated in this study highlight the usefulness of MC-SDSS for the successful resolution of complex problems, thanks to the visualization facilities and a network representation of the scenarios. Research limitations/implications – The case studies are limited to the Italian context. Practical implications – These SDSS are able to empower planners and decision makers to better understand the interaction between city design, social preferences, economic issues and policy incentives. Therefore, they have been employed in several case studies related to territorial planning and regeneration processes. Originality/value – This study provides three case studies and a review of the new MC-SDSS methodology which involve the Analytic Network Process technique to support decision-making in urban and regional planning.

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