1. bookVolume 18 (2017): Issue 2 (June 2017)
Journal Details
License
Format
Journal
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
access type Open Access

Models and Algorithms for Estimation and Minimization of the Risks Associated with Dredging

Published Online: 26 Apr 2017
Page range: 139 - 145
Journal Details
License
Format
Journal
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English

There are a lot of models and algorithms to minimize risks during dredging operations and they are not without drawbacks. The paper describes the authors’ approach to solving this problem. Mathematical models are proposed and on their basis software is developed. Methods of the risk theory are used to minimize the risks. In this paper a consequence of influence refers to the deviation from the goal expressed in the expected results and the deviation of certain criterion factors. In this case, we mean any measure of quality. In its turn, risk factors reduce criterion factors. These factors are divided into categories - general transportation risks and risks of transporting ground. In these categories, one may derive the following risks - incidents at transport resulting from the impact of a set of random factors including the human one. For risk analysis and management, in addition to identifying critical chains of risk situations, the stochastic model for evaluating the chains is set forth. In order to implement this algorithm, the mathematical package Maple is used, which allows for conducting the required calculations with a software package including the Graph Theory. The paper presents fragments of the code listing.

Keywords

1. Chen, D. and Cheng, L. (2014) Fast linearized alternating direction minimization algorithm with adaptive parameter selection for multiplicative noise removal. Journal of Computational and Applied Mathematics, 257, 29-45.Search in Google Scholar

2. Chernyi, S. (2016a) Use of Information Intelligent Components for the Analysis of Complex Processes of Marine Energy Systems,Transport and Telecommunication, 17(3), 202-211. DOI: 10.1515/ttj-2016-0018Search in Google Scholar

3. Chernyi, S. (2016b) Techniques for selecting topology and implementing the distributed control system network. IOP Conf. Ser.: Mater. Sci. Eng., 124, 012048.Search in Google Scholar

4. Chernyi, S. (2016c) Analysis of the energy reliability component for offshore drilling platforms within the Black Sea. Neftyanoe Khozyaystvo - Oil Industry, 2, 106-110.Search in Google Scholar

5. Cutroneo, L., Castellano, M., Carbone, C., Consani, S., Gaino, F., Tucci, S., Magrì, S., Povero, P., Bertolotto, R., Canepa, G. and Capello, M. (2015) Evaluation of the boundary condition influence on PAH concentrations in the water column during the sediment dredging of a port. Marine Pollution Bulletin, 101(2), 583-593.Search in Google Scholar

6. Khoshgoftaar, T. and Seliya, N. (2003) Software Quality Classification Modelling Using the SPRINT Decision Tree Algorithm. International Journal on Artificial Intelligence Tools, 12(03), 207-225.Search in Google Scholar

7. Malioutov, D., Corum, A. and Cetin, M. (2016) Covariance Matrix Estimation for Interest-Rate Risk Modeling via Smooth and Monotone Regularization. IEEE J. Sel. Top. Signal Process., 10(6), 1006-1014.Search in Google Scholar

8. Nyrkov, A., Sokolov, S., Belousov, A. (2015) Algorithmic Support of Optimization of Multicast Data Transmission in Networks with Dynamic Routing. Modern Applied Science, 9(5), 162-76. DOI: 10.5539/mas.v9n5p162.Search in Google Scholar

9. Park, C. (2005) Parameter estimation of incomplete data in competing risks using the EM algorithm. IEEE Transactions on Reliability, 54(2), 282-290.Search in Google Scholar

10. Rood, B. and Lewis, M. (2009) Grid Resource Availability Prediction-Based Scheduling and Task Replication. J Grid Computing, 7(4), 479-500.Search in Google Scholar

11. Ruffio, E., Saury, D. and Petit, D. (2012) Robust experiment design for the estimation of thermo physical parameters using stochastic algorithms. International Journal of Heat and Mass Transfer, 55(11-12), 2901-2915.Search in Google Scholar

12. Russell, M. (2014) Strategic scoping report and dredging effects. Marine Pollution Bulletin, 86(1-2), 594.Search in Google Scholar

13. Shi, Y. and Ji, H. (2014) Smooth approximation method for non-smooth empirical risk minimization based distance metric learning. Neurocomputing, 127, pp.135-143.Search in Google Scholar

14. Spearman, J. (2015) A review of the physical impacts of sediment dispersion from aggregate dredging. Marine Pollution Bulletin, 94(1-2), 260-277.Search in Google Scholar

15. Tuan, Salwani Awang (2013) Enhancing Students’ Understanding in Integral Calculus through the Integration of Maple in Learning. Procedia -Social and Behavioural Sciences, 102(22), 204-211.Search in Google Scholar

16. Upadhya, K., Seelamantula, C. and Hari, K. (2016) A risk minimization framework for channel estimation in OFDM systems. Signal Processing, 128, 78-87.Search in Google Scholar

17. Vieira, G., Neto, F. and Ribeiro, J. (2015) The Rationalization of Port Logistics Activities: A Study at Port of Santos (Brazil). International Journal of e-Navigation and Maritime Economy, 2, pp.73-86.Search in Google Scholar

18. Walther, L., Rizvanolli, A., Wendebourg, M. and Jahn, C. (2016) Modelling and Optimization Algorithms in Ship Weather Routing. International Journal of e-Navigation and Maritime Economy, 4, 31-45.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo