El-Diasty, M.

El-Rabbany, A.

English

University of New Brunswick: Centre for Digital Scholarship Journals

NotesSequential Sea-ice Concentration Prediction for Marine Operations in Ice-Infested WatersBy M. El-Diasty and A. El-Rabbany, Department of Civil Engineering, Ryerson University,CanadaMarine operations in ice-infested waters require reliable and timely infor­mation about the sea ice conditions. The Canadian Ice Service produces and distributes the ice information to mariners operating in Canadian waters, mainly in the form of daily ice charts. Unfortunately, due to the time differ­ence between the production and the use of the ice charts, the ice informa­tion is always out of date, which endan­gers the safety of marine operations. To efficiently overcome this problem, a reliable model for predicting the sea ice concentration over time is devel­oped.Examining the ice charts of the Gulf of St. Lawrence during the period 1987 to 1998 showed that the sea ice condi­tions change according to a regular pat­tern to some extent. Therefore, a neu­ral network function approximation sys­tem could model, and hence predict, these changes efficiently when trained, using multiple-year ice concentration readings. Initially, the training was done in the batch mode. However, this was found inefficient when abrupt changes in the values of the ice con­centration were encountered. Therefore, a sequential model, which uses the modular neural network structure, was developed. In addition to overcoming the drawbacks of the batch method, the sequential model is more suitable for real-time applica­tions.IntroductionMarine operations in ice-infested waters require reliable and timely infor­mation about the sea ice conditions. Validation experiments have shown that radar remote sensing, particularly RADARSAT, has the capability of provid­ing such comprehensive information. The ScanSAR modes of RADARSAT are recommended for comprehensive mon­itoring of the sea ice conditions. The Canadian Ice Service (CIS) is primarily using these modes, along with other sources of information such as air­borne and other satellite remote sens­ing, for extracting the sea ice informa­tion in the form of daily ice charts (Ramsay et a l., 1998 ; Canadian Ice Service, 1994). The ice charts contain information such as the ice concentra­tion and type, ice edge location, ice­bergs and open leads. The total con­centration of the sea ice is the most important element of the ice informa­tion required to support vesse ls with no ice capability (Haykin et a l., 1994). Unfortunately, due to the time differ­ence between the production and the use of the ice charts, the ice informa­tion is always out of date, which jeop­ardises the safety of marine operations (Canadian Ice Service, 2001). To effi­ciently overcome this problem, a neural network-based model is developed for sequentially predicting the sea ice con­centration overtim e. A supervised neu-h 1 fil 7 4 1ICE AN ALYSISANALYSE PE GLACE_______Gulf of St. LawrenceGolfe du Saint-Laurent_________V1800Z26 MAR/MAR 2002BASED ON/BASEE SUR:RECON: 26 MAR/MAR 2002 RAQARSAT 26 MAR/MAR 2002 11Z WRN REGIONS OUEST NOAA: 26 MAR/MAR 16ZjAworrlCGDX #177 COTF #60 CGY3 #22Figure 1 : An example o f the CIS ice chart for the Gulf o f St. Lawrenceral network is trained to predict the ice concentra­tion at a given location and time using the C IS ’s ice charts. The input to the network is a vector which represents the current ice concentrations over a test area containing 40 points (see Figure 1). The input vector is mapped to an output vector that gives the predicted ice concentrations.Production and Format of the Ice ChartsThe Canadian Ice Service is responsible for provid­ing the ice information in Canadian waters, mainly through its daily ice charts (Figure 1). To do this, the CIS uses various space-borne and airborne remote sensing sensors, shore station observa­tions and shipboard ice observations (Canadian Ice Service, 1994). The charts use the North American Datum 1927 (NAD 27) and the Lambert conical projection. The World Meteorological Organization (WMO) symbolisation for ice informa­tion, frequently referred to as the ‘Egg Code’ , is used to describe the ice conditions (Figure 1).Boundaries are drawn around the ice areas with different concentrations; each is represented by an egg code (Canadian Ice Service, 1994).An egg code is an oval shaped symbol, which con­tains three parts that describe the concentration of the ice, the stage of development (age) of the ice concentration and the predominant form of ice (floe size). These are expressed by up to 12 numer­ical values. The concentration of the ice represents the ratio between the area of the water surface covered by ice and the total area, and is expressed in tenths. The value of the ice concentration varies from 10/10  for consolidated ice to 1 /10  for open water. The single uppermost parameter in the egg code represents the total concentration, which includes all stages of development. The second row in the egg code matrix contains the partial concentration for the thickest (left), the second thickest (middle) and the third thickest (right) ice types. The partial concentration field may contain two numbers, if only two ice types are present in the area (see Figure 1). If there is one ice type only, the partial concentration field will be leftblank, as the concentration of the will be present­ed by the total concentration (Figure 1).The third field in the ice code contains the stages of development (age) for the ice types reported in the partial concentration field. Thicker ice refers to older ice, and vice versa. Various codes are used, depending of the stage of development. For exam­ple, a code of ‘1 ’ is a assigned to the new ice (less than 10 cm in thickness), while a code of ‘9 ’ is assigned to the second stage thin first-year ice (50- 70 cm in thickness). Medium/thick first-year ice as well as old ice are assigned a dot (•) as part of their code. The last field in the code represents the predominant forms of the sea ice (floe sizes) cor­responding to the stages of development identified in the previous field. Various codes are given to var­ious floe sizes, which vary from ‘0 ’ for the pancake ice to ‘7 ’ for the giant floe (width greater than 10 km). Fast ice and icebergs are given the codes of ‘8 ’ and ‘9 ’ , respectively. Undetermined ice form,, unknown or no form is assigned the code of ‘X ’ .Figure 2: Three-layer feed-forward neural network with the structure [160-300-40]Artificial Neural Network SystemArtificial neural networks were used to build our computational model for the ice conditions because of their ability to learn from examples and ‘generalise’ on these examples. We chose the three-layer feed-forward neural network trained using the back-propagation algorithm for these experiments (Figure 2). The back-propagation algo­rithm uses gradient descent in weight space to minimize the output error. It converges to a locally optimal solution when an adequate number of input-output pairs of sam ples are subjected to the network for an adequate number of epochs. The back-propagation algorithm can be summarised in three steps as follows (see Abdelazim et a l., 2001 and El-Rabbany et a l., 2002 for more details). The first step is to propagate the input forward through the network. The neurons in the first layer receive external inputs:a °  = pa m+1 =  f m+1( W m+la m + b m+i)(i)to the layer m+1. The outputs of the neurons in the last layer are considered the network outputs:a = a M (3)The next step is to calculate the output error and back-propagate it to calculate the sensitivities (s values).s M  =  - 2 F m  { n M  ) ( t - a )s m = F m ( n m ) ( W m+l ) s m+l(4)(5)for m = M-l, . . . ,  2 , 1Finally, the weights and biases are updated, based on the calculated sensitivities, using the approxi­mate steepest descent rule:m -l\Tw m ( k  + 1 ) = W  m (jfc) -  CMm (a  m~l )(6 )b m ( k  + \) =  b m ( k ) - Q L S m (7)Where a  is the step size. The three steps are repeated until the error reaches a minimum; hence, the calculated weights of the connections would represent the solution network.for m = 0, 1, M-l (2) Results for the Sea Ice PredictionWhere M  is the number of layers in the network. Equation (1) provides the starting point for equa­tion (2), and the output of the layer m is the inputAs mentioned above, a supervised neural network was trained to predict the ice concentrations at a randomly selected location within of the Gulf of St.Output LayerHidden LayerInput LayerEntire training data set”, J>2”, r, *5 *7p8 •••®®®® o o o—o ®® ®teutlo o—o o ®<§)®®Ü1o—o• o•■ o• ®•®•a ®•■ (a)•■ ID***•A•w• • • w• ••„ .w•Kfcg.......w•Lawrence, which were provided by the Canadian Ice Service (CIS). The dataset consisted of the C IS ’s weekly ice charts over 11 years (1987-1998). The input ice data was mapped to an output vector that gave the predicted ice concentrations. The tradi­tional non-modular feed-forward neural network structure failed to map the required function, and hence, was modularised to give better prediction performance. The structure of the neural network was built using the NeuralWare software (NeuralWare, 2001). Several tests were conducted to optimize the structure of the neural network. It was concluded that the modular neural network with the structure [160-300-5-40] gives the best results, i.e ., has the lowest Root-Mean-Square (RMS) error. This structure represents 160 source neurons, 300 hidden neurons for both the local experts and the gating network, 5 ‘gating’ outputFigure 3 : Training patterns used in the neural network inputneurons, and 40 network output neurons.Initially, we used the time (i.e ., week number) as the only input to the network, which is usually followed in the literature (see, for example, El-Rabbany et al., 2002). However, although the trained network repre­sented the training dataset reasonably well, the net­work prediction was rather poor due to the abrupt changes in the values of the ice concentration over short period of time (see Figure 5 below). Therefore, we followed another approach, which was proposed by Schuh et al. (2002). In this approach, the imme­diate past values of the ice concentration are used as input to the network, while future values of the ice concentration are used as the desired (i.e ., actu­al) output. In the subsequent epochs, the training patterns are time-shifted as shown in Figure 3. Based on the size of the available dataset, a total 599 different patterns were created.Figure 4 : Selection of testing, training and validation data subsetsO  CM ^  ( D C O O C M ' t œ œ O M ' t ® ®  O  CM ’ st" CD CO O  CM CO 2 2 2  i n i f i U ) « ) « ® ® t O f f i ® N N N N N  O O O D O O O D C O r o O C S ®  < 5 f 2  i n i n t o m i n m u D i o i f l i f l i f l i n t n ^ f w i n w u î i o L O W i f i i nTime (weeks)Figure 5: Actual versus predicted ice concentration values for the year 1998The dataset was divided into three subsets: training, testing and validation subsets (Figure 4). The first 50 patterns were assigned to the testing subset, while the last 50 patterns were assigned to the val­idation subset. The training subset was selected to represent the middle portion of the dataset, i.e ., 499 patterns. The training was stopped based on testing the generalization performance of the neural network using the testing subset. After training and testing the network, we generalized the model to predict ahead the last 50 patterns of the dataset and compared the results with the actual values of the ice concentration. This was done in a sequential manner to emulate the real-time condition. Figure 5 shows the predicted ice concentration values versus the desired values.The performance of the network was measured based on the percentage of correct prediction, i.e ., the ratio of the correct network output values and the actual values at the various points in the test area. The network solutions show that the predic­tion performance of the neural network varied between 86 per cent (43 out of 50 were correct) and 98 per cent (49 out of 50 were correct). The average overall performance of the network over the entire test area (i.e ., over the 40 points) was 93 .2  per cent. This shows that, despite the fact that the environmental conditions were not includ­ed, the neural network was capable of predicting the ice concentration at high prediction accuracy level. Figure 5 shows the actual versus predictedice concentrations for the year 1998 , at a random­ly selected point within the test area.Conclusions and Future WorkA sequential neural network-based model for ice con­centration prediction was developed in this paper. The modular neural network structure gave the best performance results (i.e ., the minimum RMS), and therefore was used in predicting the ice concentra­tion values at a selected test area within the Gulf of St. Lawrence. It is shown that, despite the absence of the environmental data, the prediction perform­ance of the neural network varied between 86 per cent and 98 per cent. The average overall perform­ance of the network over the entire test area (i.e., over the 40 points) was 93 .2  per cent. To further enhance the prediction capability, a future version of the model will include environmental data and other ice parameters, e.g., the partial ice concentrations and the stages of development of the ice types.AcknowledgmentsThis research is supported by the Centre for Research in Earth and Space Technology (CRESTech), an Ontario Centre of Excellence. The ice charts were provided by the Canadian Ice Service (CIS) in Ottawa.ReferencesAbdelazim, S ., A. El-Rabbany and G. Auda (2001). ‘Artificial Neural Networks for Ice Concentration Prediction.' Accepted for Publication at the Journal of Marine GeodesyCanadian Ice Service (1994). ‘MANICE: Manual of Standard Procedures for Observing and Reporting Ice Conditions.’ Environment Canada, Ottawa, CanadaCanadian Ice Service (2001). ‘ECDIS Ice Objects’, Version 3 .0 , Environment Canada, Ottawa, CanadaEl-Rabbany, A. (2000). ‘Integrated Navigation Chart System.’ Invited presentation at the ‘Ice in ECDIS’ Workshop, St. John’s , Newfoundland, 5-6 JuneEl-Rabbany, A., G. Auda and S. Abdelazim (2002). ‘Predicting Sea Ice Conditions Using Neural Networks.’ Journal of Navigation, Vol. 55, Number 1, pp. 137-143Haykin, S ., E.O. Lewis, R.K. Raney and J.R . Rossiter(1994). Remote Sensing of Sea Ice and Icebergs, John Wiley & Sons, IncNeuralWare Inc. (2000). ‘NeuralWorks li/Plus: Software Manual’, PA, USARamsay, B., M. Manore, L. Weir, K. Wilson and D. Bradley (1998). ‘Utilization of RADARSAT Data in the Canadian Ice Service.’ Application Development and Research Opportunity (ADRO) Symposium, Montreal, OctoberRandell, C., M. Rokonuzzaman, J. Youden and R. Khan (1998). ‘Evaluation of RADARSAT for Detection, Classification and Discrimination of Icebergs.’ Application Development and Research Opportunity (ADRO) Symposium, Montreal, OctoberSchuh et al, (2002). ‘Prediction of Earth Orientation Parameters by Artificial Neural Networks.’ Journal of Geodesy, Vol. 76, pp. 247-258E-mail: rabbany@ryerson.caSPECIALIST SERVICE PROVIDER FOR:NAUTICAL CHARTING SURVEY TO IHO S-44 STANDARDUNCLOS 76 EEZ SEISMIC AND BATHYMETRY SURVEYROUTEING MEASURES TO IMO STANDARDOCEANOGRAPHY AND ENVIRONMENTAL STUDIESVESSEL CHARTERSEUROPE:Endeavour House,Adm iralty Road, Great Yarmouth, Norfolk NR30 3NG, U .K .Te l: + 44 (0) 1493 84S600 F a x : + 44 <0) 1493 852106 m ail: mktégardline.comAMERICAS:8556 Katy Freeway, Suite 104,Houston TX  77024 Te l: ♦ 1 713 973 2855 Fax : ♦ 1 713 464 8275 m ail: usa@gardline.comSOUTH EAST ASIA:371 Beach Road, Keypoint#02-25,Singapore 199597 Te l: ♦ 65 62922533 Fax : ♦ 65 62937694 m ail: gardsing@gardline.com

Sequential Sea-ice Concentration Prediction for Marine Operations in Ice-Infested Waters

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Sequential Sea-ice Concentration Prediction for Marine Operations in Ice-Infested Waters

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