Acciani, G., D'Orazio, A., Delmedico, V., De Sario, M., Gramegna, T. and Petruzzelli, V. (2003). Radiometric profiling of temperature using algorithm based on neural networks. Electron. Letter, 39, 1261-1263.
Al-Rabeh, A.H., Cekirge, H.M. and Gunay, N. (1992). Modeling the fate and transport of the Al-Ahmadi spill. Water, Air and Soil Pollution, 65, 257-279.
Bar, N., Bandypodhyay, T.K., Biswas, M.N. and Das, S.K. (2010). Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components. Journal of Petroleum and Engineering, 71, 187-194.
Barios, D. A. J. (2016). Numerical simulation of oil spills: Application to a coastal zone. Madrid, Spain: Universidad PoliTe'cnica De Madrid, Master’s thesis, 57p.
Booty, W.G., Lam, D.C.L., Wong, I.W.S. and Siconolfi, P. (2001). Design and implementation of an environmental decision support system. Environmental Modelling & Software, 16, 453–458.
Chao, X.B., Shankar, N.J. and Cheong H.F. (2001). Two and three dimensional oil spill model for coastal waters. Ocean Engineering, 28, 1557-1573.
Chao, X.B., Shankar, N.J. and Wang, S.S.Y. (2003). Development and Application of Oil Spill Model for Singapore Coastal Waters. Journal of Hydraulic Engineering, 129, 495-503.
Dean, R.G. and Dalrymple, R.A. (1991). Water wave mechanics for engineers and scientists. Advanced Series on Ocean Engineering: World Scientific, 368p. Den Boer, S., Azevedo, A., Vaz, L., Costa, R., Fortunato, A.B., Oliveira, A., Tomás, L.M., Dias, J.M. and Rodrigues, M. (2014). Development of an oil spill hazard scenarios database for risk assessment. Proceedings 13th International Coastal Symposium (Durban, South Africa). Journal of Coastal Research, Special Issue No. 70, pp. 539–544.
De Dominicis, M., Bruciaferri, D., Gerin, R., Pinardi, N., Poulain, P.M., Garreau, P., Zodiatis, G., Perivoliotis, L., Fazioli, L., Sorgente R. and Manganiello C. (2016). A multi-model assessment of the impact of currents, waves and wind in modelling surface drifters and oil spill. Deep Sea Research Part II: Topical Studies in Oceanography.133, 21-38
De Dominicis, M., Pinardi, N., Zodiatis, G. and Lardner, R. (2013). MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting - Part 1: Theory. Geoscientific Model Development, 6, 1851–1869, doi: 10.5194/gmd-6-1851-2013.
Janati, M., et al.
424
Fay, J.A. (1971). Physical processes in the spread of oil on a water surface. Proceedings of Conference of Prevention and Control of Oil Spills. Washington, DC: American Petroleum Institute, 463-467.
Gallego, A.J., Gil, P., Pertusa A. and Fisher, R. B. (2018). Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders. Sensors, 18(797), doi:10.3390/s18030797.
Golshan, R., Boufadel, M. C., Rodriguez, V. A., Geng, X., Gao F., King T., Robinson, B. and Tejada-Martínez A. E. (2018). Oil Droplet Transport under Non-Breaking Waves: An Eulerian RANS Approach Combined with a Lagrangian Particle Dispersion Model. Journal of Marine Science and Engineering, 6(7), doi:10.3390/jmse6010007.
Gumrah, F., Oz, B., Guler, B. and Evin, S. (2000). The application of artificial neural networks for the prediction of water quality of polluted aquifer. Water, Air, and Soil Pollution, 119, 275-294.
Huang, K., Dai, L. and Huang, S. (2010). Wind Prediction Based on Improved BP Artificial Neural Network in Wind Farm. (ICECE) 10th Proceedings of the 2010 International Conference on Electrical and Control Engineering. IEEE Computer Society Washington, DC, USA. 2548-2551.
Imanian, H., Kolahdoozan, M. and Zarrati, A.R. (2011). Waves Simulation in Viscous Waters Using MPS. International Conference on Computer and Communication Devices. Indonesia, 2, 264-268.
Imanian, H., Kolahdoozan, M. and Zarrati, A.R. (2017). Vertical Dispersion in Oil Spill Fate and Transport Models. Journal of Hydrosciences and Environment, 1(2); 21-33.
Khataee, A.R. and Kasiri, M.B. (2010). Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis. Journal of Molecular Catalysis A: Chemical, 331, 86-100.
Kohonen, T. (1989). Self-Organization and Associative Memory. Berlin: Springer- Verlag, 312p.
Koshizuka, S., Nobe, A. and Oka, Y. (1998). Numerical analysis of breaking waves using the Moving Particle Semi-implicit method. International Journal of Numerical Methods in Fluids, 26, 751-769.
Koshizuka, S. and Oka, Y. (1996). Moving particle semi-implicit method for fragmentation of incompressible fluid. Nuclear Engineering Science, 123, 421–434.
Lee S. (2018). Application of Artificial Neural Networks in Geo-informatics. Applied Science, Special issue, 8(55), doi:10.3390/app8010055.
Li, X., Maier, H.R. and Zecchin, A.C. (2015). Improved PMI‐based input variable selection approach for artificial neural network and other data driven environmental and water resource models. Environmental modelling and software, 65, 15-29.
Liao, Z., Wang, B., Xia, X. and Hannam, P.M. (2012). Environmental emergency decision support system based on Artificial Neural Network. Safety Science, 50, 150–163.
Liu, Y., Macfadyen, A., Ji, Z.G., Weisberg, R. (2011). Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise, American Geophysical Union: Washington, DC, USA.
Maier, H.R., Jain, A., Dandy, G.C. and Sudheer, K.P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental modelling and software, 25(8), 891-909.
Monaghan, J.J. (1994). Simulating free surface flows with SPH. Journal of computational physics, 110, 399-406.
Nagheeby, M. and Kolahdoozan, M. (2010). Numerical modeling of two-phase fluid flow and oil slick transport in estuarine water. International Journal of Environmental Science and Technology, 7(4); 771-784.
Palani, S., Liong, S.Y. and Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56, 1586-1597.
Rajasekaran, S. and Bharadwaj, A. (2012). Enhancing Artificial Neural Network: DSS Framework Pertaining to Oil Spill Response Management. CiiT- International Journal of Artificial Intelligent Systems and Machine Learning. 4(2); 77-81.
Ranga Raju, K.G. (1993). Flow through Open Channels. Tata McGraw-Hill, 428p.
Rowshan, G.R., Mohammadi, H., Nasrabadi, T., Hoveidi, H. and Baghvand, A. (2007). The role of climate study in analyzing flood forming potential of water basins. International Journal of Environment Research, 1, 231-236.
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning representations by back propagating errors. Nature, 323, 533-536.
Rutherford R., Moulitsas I., Snow B. J., Kolios A. J. and De Dominicis M. (2015). CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods. Geoscientific Model Development, 8, 3365-3377. doi:10.5194/gmd-8-3365-2015.
Pollution, 6(2): 409-425, Spring 2020
Pollution is licensed under a "Creative Commons Attribution 4.0 International (CC-BY 4.0)"
425
Sarhadizadeh, E. and Hejazi, K. (2012). Eulerian Oil Spills Model Using Finite-Volume Method with Moving Boundary and Wet-Dry Front. Modelling and Simulation in Engineering. doi:10.1155/2012/398387.
Singha, S., Bellerby, T. J. and Olaf Trieschmann (2013). Satellite Oil Spill Detection Using Artificial Neural Networks. IEEE Journal of selected topics in applied earth observations and remote sensing. 6(6); 2355-2363.
Song, D., Ding, Y., Li, X., Zhang, B. and Xu, M. (2017). Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network. Remote Sensing. 9(8); 799. doi ,10.3390/rs9080799.
Verma, A. (2016). Application of computational transport Analysis oil spill dynamics. The University at Buffalo, State University of New York. Master’s thesis, 51p.
Wang, S.D., Shen, Y.M. and Zheng, Y.H. (2005). Two-dimensional numerical simulation for transport and fate of oil spills in seas. Ocean Engineering, 32, 1556-1571.
Weng, L. (2017). Prediction of droplet size distribution from subsurface oil releases with and without chemical dispersants application. Halifax, Nova Scotia: Dalhousie University, Master’s thesis, 119p.
Xu, Q., Zheng, J., Cheng, Y., Zhang, S., Chen, M. and Huang, Q. (2016). Detection of Marine Oil Spills from SAR Images Using Artificial Neural Networks. 26th International Ocean and Polar Engineering Conference. Rhodes, Greece. ISOPE-I-16-624.
Zhang, Y., Qiao J., Wu, B., Jiang, W., Xu, X. and Hu, G. (2015). Simulation of oil spill using ANN and CA models. 23rd International Conference on Geoinformatics. Wuhan, China. doi:10.1109/Geoinformatics.2015.7378560
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