Simulation of oil spill using ANN and CA models

In this paper, the artificial neural network (ANN) used to obtain transition rules in oil spill CA model. Model parameters are difficult to obtain in many traditional oil spill models, as they cannot meet the requirements of rapid response for oil spills. Therefore, a new oil spill model - ANN oil spill CA model was established in this paper. This model can simulate the change process of oil spill by setting initial image, verification image, and impact factors. Experimental results show that the simulation results have a good performance with overall accuracy of 96.6% and Kappa coefficient of 0.826. It was also found that the consistency of simulation results is proportional to the ratio of training sample. However, the higher the ratio of the training sample, the more computation is need in the ANN training. We also studied the effect of neurons number in the hidden layer. Studies show that the consistency of simulation results becomes better with the increase of neurons number in the initial stage for good fitting rate of training sample. However, the consistency of simulation results get worse for over-fitting of training sample in following stage.

[1]  Ning Li,et al.  Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia , 2008 .

[2]  Wenzhong Shi,et al.  Development of Voronoi-based cellular automata -an integrated dynamic model for Geographical Information Systems , 2000, Int. J. Geogr. Inf. Sci..

[3]  Norio Okada,et al.  Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China , 2006 .

[4]  Mark Brussel,et al.  A cellular automata-based land use and transport interaction model applied to Jeddah, Saudi Arabia , 2013 .

[5]  Ioannis G. Karafyllidis,et al.  A model for the prediction of oil slick movement and spreading using cellular automata , 1997 .

[6]  Xiaoping Liu,et al.  An extended cellular automaton using case‐based reasoning for simulating urban development in a large complex region , 2006 .

[7]  Shoudao Huang,et al.  Wind Prediction Based on Improved BP Artificial Neural Network in Wind Farm , 2010, 2010 International Conference on Electrical and Control Engineering.

[8]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[9]  Luis Mateus Rocha,et al.  Material Representations: From the Genetic Code to the Evolution of Cellular Automata , 2005, Artificial Life.

[10]  N. Jothi Shankar,et al.  Development and Application of Oil Spill Model for Singapore Coastal Waters , 2003 .

[11]  John Staudenmayer,et al.  An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. , 2009, Journal of applied physiology.

[12]  RochaLuis Mateus,et al.  Material Representations: From the Genetic Code to the Evolution of Cellular Automata , 2005 .

[13]  Øistein Johansen,et al.  DeepSpill––Field Study of a Simulated Oil and Gas Blowout in Deep Water , 2003 .

[14]  Qiao Jigang.  The CA model based on data assimilation , 2011 .

[15]  Kh. M. Gamzaev.  Modeling the spread of an oil slick on the sea surface , 2009 .

[16]  Haruhisa Shimoda,et al.  NASDA's Earth Observation Data and Information System(EOIS) , 1995 .

[17]  Roger White,et al.  Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns , 1993 .

[18]  Cellular automata based model for the prediction of oil slicks behavior , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[19]  A. Yeh,et al.  Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta , 1998 .