INVESTIGATING THE IMPACT OF WIND ON SEA LEVELL RISE USING MULTILAYER AT COASTAL AREA, SABAH

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 12, December 2018, pp , Article ID: IJCIET_09_12_070 Available online at aeme.com/ijciet/issues.asp?jtype=ijciet&vtype= =9&IType=12 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed INVESTIGATING THE IMPACT OF WIND ON SEA LEVELL RISE USING MULTILAYER PERCEPTRON NEURAL NETWORK (MLP-NN) AT COASTAL AREA, SABAH T. Olivia Muslim Department of Civil Engineering, College of Engineering, UniversitiTenagaNasional (UNITEN), Kajang 43000, Selangor, Malaysia A. Najah Ahmed Institute of Energy Infrastructure (IEI), UniversitiTenagaNasional (UNITEN), Kajang 43000, Selangor, Malaysia M. A. Malek Institute of Power Energy (IPE), UniversitiTenagaNasional (UNITEN), Kajang 43000, Selangor, Malaysia A. El- Shafie Department of Civil Engineering, Faculty of Engineering, Universitiof Malaya (UM), Kuala Lumpur, Malaysia Amr EL-Shafie Civil Engineering Department, Giza High Institute for Engineering and Technology, Giza, Egypt ABSTRACT This study investigating the impact of wind on sea level rise (SLR) using Multilayer Perceptron Neural Network (MLP-NN) at Coastal Area, Sabah. The mean sea level (MSL) and four meteorology parameters namely; wind direction (WD), wind speed (WS), rainfall and mean cloud cover. These meteorological parameter and MSL were monitored regularly each month over a period from January 2007 to December 2016 at three different locations which is Kudat, Kota Kinabalu and Sandakan. Due to small amount of data set, both method the input data were dividedd into 80 % for training and 20% for testing data respectively.in this study, two scenarios were introduced; the scenario 1 (with wind) WD and WS as input parameterr while scenario 2 (without wind)rainfall and mean cloud cover to predict sea level at each stations. Then by using previous monthly sea water level records the model was performed by predicting SLR for1 year, 5 years, 10 years, 30 years, and 50 years ahead in the editor@iaeme.com

2 Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah future. The performance of the models was evaluated according to three statistical indices in terms of the correlation coefficient (R), root mean square error (RMSE) and scatter index (SI). Investigation results indicate that, when compared to measurements, for 50 years prediction, all three models in scenario 2 perform well (with average values of R = 0.6, RMSE = 0.2 cm and SI = 0.4). Keywords: Artificial Neural Network, Artificial Intelligence (AI), Multilayer perceptron Neural Network (MLP NN), Sea Level. Cite this Article: J T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie, Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah, International Journal of Civil Engineering and Technology (IJCIET) 9(12), 2018, pp INTRODUCTION Millions people are projected to be flooded every year due to the SLR by the 2080s. Densely populated and low lying areas are already facing other challenges such as local coastal subsidence are especially at risk. [1]. Based on rate of global SLR during the period 1993 to 2003, at about 3.1 mm per year as compared to the average rate of 1.8 mm per year during the period 1961 to 2003 (IPCC, 2007) [2]. Variations of sea levels are produced by combination of complex processes involving Meteorological parameters like the atmospheric pressure, air temperature, water temperature, ocean currents, wind, etc. [2]. Meteorological conditions which vary from the average will cause corresponding differences between the predicted and MSL. Differences between predicted and actual times of high and low water are caused mainly by the wind. In general, sea levels are raised in the direction of the wind, often called wind setup [3]. Recently, the artificial neural network (ANN) approach has been applied to many branches of science. There are a number of studies in which neural network are used to address sea level problems. S.P. Nitsure et al. [4] applied three layers in the Feed Forward Error Back-Propagation type of Artificial Neural Network (FFBPNN) that was used for this work. This method is most commonly used in ocean-related studies to predict sea water levels by using hourly wind shear velocities at stations near the USA coastline. Similarly, O. Makarynskyy et al. [5] applied the ANN to predict sea level variations at Hillary s Boat Harbour, Western Australia. J. Piri [6] applied the ANN with three most important variables that affect water levels at reservoirs, namely evaporation, wind speed and daily temperature, to predict water level fluctuations at the Chahnimeh Reservoir in Zabol. The objective of this paper is to investigate the impact of wind on SLR at Kudat, Kota Kinabalu and Sandakan, Sabah. The statistical analysis is applied to analyze the performance of the developed model in training, testing and prediction to 1, 5, 10, 30 and 50 years ahead. 2. METHODS AND MATERIALS 2.1. Study Area Sabah is the second largest state in Malaysia and shares the island of Borneo with Sarawak, Brunei, and Indonesian Kalimantan. Sabah are known as the Land below the Wind. The three stations location map is provided in Figure editor@iaeme.com

3 T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie Kudat KK Sandakan Figure 1.Location of study area Data Analysis Two sets of data were prepared. The first included record of monthly MSL data for three stations from the tide gauge ( ). See Figure 2. Figure 2.Monthly MSL data from The second set of data is meteorological parameter input. In the literature various meteorological parameters have been used to create the model for predicting the SLR. See Table 1. Based on literature, existing measured values and statistical analyses, the following four meteorological parameters were selected as the second group of data for the MLP-NN modelling in this study namely; rainfall, mean cloud cover, WD and WS These meteorological parameter wereobtained from Malaysian Meteorological Department. In this study, data were divided into two scenario which is with wind (WD and WS) as Scenario 1 and without wind (rainfall and mean cloud cover) as scenario 2. The input data were divided into 80 % for training and 20% for testing data respectively. Then, the model was performed with prediction for 1 year, 5 years, 10 years, 30 years and 50 years ahead. In order to use MLP NN structures effectively, the meteorological input parameter must be selected with great care.figure 3 represents the correlation analysis between meteorological parameters and sea water levelsfor three stations. It was found that wind components has the lowest correlation with sea water levels, while the clouds show up to +/ correlations with sea water levels. Rainfall had around correlation with the sea water levels editor@iaeme.com

4 Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah Table 1 Meteorological parameters used in previous studies Author(s) / Year Meteorological Parameter Location(s) A. Filippo et al. (2012) [7] H. Rafiean et al. (2013) [8] Atmospheric preassure, Wind Seal level pressure and Sea surface temperature Cananeia and Ilha Fiscal, Brazil New York City coastal area 0.Kisi et al. (2014) [9] S.P. Nitsure et al. (2014) [4] J. Piri et al. (2016) [6] Air temperature, Wind speed, Wind directionand Rainfall Wind speed and Wind direction Evaporation, Wind speed and Temperature Mukho Station, South Korea USA Zabol, Iran Wind Speed Wind Clouds Rainfall Correlation between meteorological parameters and sea water levels Kudat Sandakan Kota Kinabalu Figure 3.Correlation between meteorological parameters and sea water levels 2.3. Multilayer Perceptron Neural Network (MLP NN) Among the many types of ANNs, the most widely used is the feed-forward neural network such as the multi-layer perceptron (MLP) network with a back propagation training algorithm that proposed by Rumelthart, Hinton and Williams in The MLP is organized as layers of computing elements known as neurons. Each neuron is connected to other neurons by means of direct communication links, each with an associated weight [10, 11]. The network usually has three ormore layers of neurons, which comprise an input layer, a hidden layer and an output layer. The input layer admits incoming information, which is processed by the hidden layer(s), and the output layer presents the network result. Figure 4 presents a structure of feed forward that consists of input layer, hidden layers and one output layer [12].Further information on ANNs can be found in, e.g. Haykin (1999) [13]. Figure 4.Structure of a feed-forward network editor@iaeme.com

5 T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie 2.4 Data Normalization All data of the input layer are normalized to a range from 0 to 1 by the function: min() () = max() min() Where v which an input or output value min (v) is the minimum value of vmax (v) is the maximum value of v[14]. (1) 2.5. Performance Criteria For this study, the most popular statistical indices were used. The performance of the model was evaluated according to three statistical indices in terms of the correlation coefficient (R), root mean square error (RMSE) and scatter index (SI), the expressions for which are presented below: ( ) ( ) R = ( )² ( )² (2) Where xi is the value observed at the time step, RMSE = "[ ]² # (3) SI = &'() (4) y is the value simulated at the same moment of time, x is the mean value of the observation, y is the mean value of the simulation and n is the number of time steps [15]. 3. RESULTS AND DISCUSSION 3.1. Determination the Number of Hidden Neurons In this study, the optimum number of neurons was determined based on the minimum value of the Mean Square Error (MSE) of the training data set. The training was performed with a variation of 1-22 neurons. When a neuron was used, the value of the MSE was , and it decreased to when 19 neurons were used. Thus, 19 neurons were selected as the best number of neurons. Enlarging the number of neurons to more than 19 did not significantly decrease the MSE. Figure 5 shows the relationship between the numbers of neurons versus the MSE during training. While table 2 shows the connection of the number of hidden layers between the weighted value of the meteorological input parameters and the output layer at Kudat. i editor@iaeme.com

6 Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah 600 MSE Number of Neuron Figure 5. Relationship between the number of neurons and the MSE. Table 2 Connection of the hidden layer between the weight for the meteorological input parameters (W1) and the output layer (W2) No W1 W2 Meteorological Input Parameters Target Rainfall Wind Direction Wind Speed Cloud MSL Validation of the Model Figure 6 shows the proposed architecture used to validate and predict the MSL. The training and testing of the MLP-NN model were performed to minimize the MSE between the MSL and the predicted response. The performance goal was achieved randomly at 500 iterations (epochs) editor@iaeme.com

7 T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie Figure 6. Architecture of ANN with flowchart of the procedure for the algorithm. Scenario 1 (WD+WS +MSL) Scenario 2 (Rainfall +Clouds +MSL) Table 3 Statistical test at Kudat, Kota Kinabalu and Sandakan Statistical Test Location Kudat Kota Kinabalu Sandakan Train Validate Train Validate Train Validate R RMSE S R RMSE SI The best obtained results by applying the MLP-NN are summarized in Table 3 in term of statistical test. The table shows the R, RMSE and SI which are obtained from the data subsets utilized both in the training and in the testing procedures in every scenarios at all locations. During the training stage, noticed that scenario 2 at Kudat performed better with the R= , RMSE = comparing with scenario 1 where R is and RMSE is respectively. In addition, the scenario 1 at Kota Kinabalu and Sandakan results provide almost the same performance with almost the same RMSE values in training stage which is and Mean Sea Level Kudat Testing Years Actual Data Scenario 1 Scenario editor@iaeme.com

8 Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah Mean Sea Level Kota Kinabalu Testing Actual Data Years Scenario 1 Scenario 2 Mean Sea Level Sandakan Testing Actual Data Years Scenario 1 Scenario 2 Figure 7.Graphical test at Kudat, Kota Kinabalu and Sandakan The achieved results for training and testing data subsets are graphically presented on Figure 7. It can be appreciated from this figure that the graphs pertaining to case 2 are very close to the actual data MSL. Based on thefigure, the graph shown actual maximum of MSL for Kudat, Kota Kinabalu and Sandakan can be achieved in 7239mm, 7306mm, and 7278mm. At Kudat, the maximum predict SLR for scenario 1 is 7113mm and 7128mm for scenario 2. Move to Kota Kinabalu the maximum predict SLR for Scenario 1 is 7179mm compared to scenario 2 is Sandakan stated that 7185mm for scenario 1 and 7253mm for scenario 2.The validation in the present study, the scenario 2 is managed to recognize the pattern of the actual MSL. Whereas the graph of scenario 1 showed it highly volatile fluctuations for three locations Prediction of the Model Table 4 sums up the testing error statistics of the applied models by using previous monthly sea water level records. From the presented statistics it is clear that all three locations produce the most accurate results in 50 years ahead predictions in scenario 2.In the case of 50 years predictions, the developed MLP-NN in scenario 2 performing slightly better than the scenario 1. It can been seen that Kudat is more accurate prediction compared to others locations with value of R , Kota KinabaluR= while Sandakan has the lowest value of R= For the other prediction intervals, for 5 years, the MLP-NN model s R decreased from to for scenario 1and for scenario 2 value of R decreased from to at Kudat. Comparison of 10 years and 30 yearspredictionfor scenario 1, indicated that R increased from to for the latter, while RMSE also increased from to At scenario 2 the prediction for 10 years and 30 years showed the most accurate result in term value of R that increased from to At Kota Kinabalu, by increasing prediction to 5 years, indicated that R for scenarios 1 and scenario 2 decreased from to and to Comparison of 10 years and 30 years prediction, indicated that editor@iaeme.com

T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie R decreased from 0.2165 to 0.2105 for scenario 1 but increased from 0.5376 to 0.6101 for scenario 2.

9 T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie R decreased from to for scenario 1 but increased from to for scenario 2. Among the three locations, Sandakan has the lowest value. Prediction 5 years for scenario 1, the value of R decreased from to but RMSE increased from to For 10 years and 30 years prediction the value of R increased from to but decreased in RMSE from to For scenario 2 the prediction for 5 years, the R decreased from to with increased RMSE from to Then the R decreased to for 10 years prediction and the RMSE= The results were also presented in term of graphical test. Table 6 Statistical test for prediction at Sandakan, Kota Kinabalu and Kudat Location Kudat Kota Kinabalu Sandakan Performance Criteria Scenario 1 Scenario 2 Years (With wind) (Without wind) R RMSE SI R RMSE SI editor@iaeme.com

Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah Figure 8 Graphical test for prediction 50 years at Kudat, Kota Kinabalu

165 mm at Kudat, while 7249.468mm and 7329.324mm for Kota Kinabalu and Sandakan respectively. 4.

10 Investigating The Impact of Wind On Sea Level Rise Using Multilayer Perceptron Neural Network (MLP-NN) At Coastal Area, Sabah Figure 8 Graphical test for prediction 50 years at Kudat, Kota Kinabalu and Sandakan Figure 8 displays the prediction sea water level values for 50 years ahead for three locations. It is clear from the figures, scenario 2 can reach the high maximum of MSL with value mm at Kudat, while mm and mm for Kota Kinabalu and Sandakan respectively. 4. CONCLUSION Monthly MSL from Kudat, Kota Kinabalu and Sandakan were used to investigate the impact of wind to SLR by MLP-NN. It is shown that during training and testing stage, the overall best performance was attained with scenario 2 for all locations where rainfall and cloud signals are further correlated with SLR. Therefore, wind is not gives higher impact to the sea level. Based on geographical, location skudat will probably to SLR because it is located near to the South China Sea. The model then used to predict the water level by using previous monthly sea water level records for predicting 1 year, 5 years, 10 years, 30 years, and 50 years ahead in the futurefor all the locations and both scenarios. An analysis of the obtained prediction results revealed that all three models performed well in 50 years ahead predictions in scenario 2. This will surely help various authorities to manage the possible damage that is expected to occur due to the impact of sea level rise in the future within the scope of the prediction of Water Resources Engineering in general and Hydrology, and for future works. ACKNOWLEDGEMENT This research is supported by the UniversitiTenagaNasional Research and Development Sdn. Bhd. (URND) under Seeding Fund U-TG-CR The authors also would like to acknowledge the UniversitiTenagaNasional for the financial support under Bold Grant /B/9/2017/14. The authors are grateful to Malaysian Meteorological Department (MetMalaysia) for providing data for this research editor@iaeme.com

11 T. Olivia Muslim, A. Najah Ahmed, M. A. Malek, A. El- Shafie and Amr EL-Shafie REFERENCES [1] E.Turban, Expert System and Applied Artificial Intelligence, 1st ed. New York: Machmillan Publishing Company, 1992, pp [2] S. Dasgupta and C. Meisner, "Climate Change and Sea Level Rise a Review of the Scientific Evidence". United State America, pp. 1-36, [3] "Introduction About Tide Predicitions - Bureau of Meteorology", Bom.gov.au, [Online].Available: [Accessed: 20- Jun- 2018]. [4] S.P. Nitsure, S.N. Londhe and K.C. Khare. (2014). Prediction of sea water levels using wind information and soft computing techniques, pp [5] O. Makarynskyy, D. Makarynska, M. Kuhn and W. Featherstone, "Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia", Estuarine, Coastal and Shelf Science, vol. 61, no. 2, pp , [6] J. Piri and M. Rezaei Kahkha, "Prediction of Water Level Fluctuations of Chahnimeh Reservoirs in Zabol Using ANN, ANFIS and Cuckoo Optimization Algorithm", Iranian Journal of Health, Safety & Environment, vol. 4, no. 2, pp. pp , [7] A. Filippo, A. Rebelo Torres, B. Kjerfve and A. Monat, "Application of Artificial Neural Network (ANN) to improve forecasting of sea level", Ocean & Coastal Management, vol. 55, pp , [8] H. Rafiean and M. Aliei, "Application of Neuro-Fuzzy Model for Predicting Sea Level Rise Utilizing Climatic Signals: A Case Study", Technical Journal of Engineering and Applied Sciences, no , pp. 1-6, [9] O. Kisi, S. Karimi, J. Shiri, O. Makarynskyy and H. Yoon, "Forecasting Sea Water Levels at Mukho Station, South Korea Using Soft Computing Techniques", The International Journal of Ocean and Climate Systems, vol. 5, no. 4, pp , [10] A. Najah, A. El-Shafie, O. Karim and O. Jaafar, "Integrated Versus Isolated Scenario For Prediction Dissolved Oxygen at Progression of Water Quality Monitoring Stations", Hydrology and Earth System Sciences Discussions, vol. 8, no. 3, pp , [11] N. Zaini, M. Abdul Malek and M. Yusoff, "Application of Computational Intelligence Methods in Modelling River Flow Prediction: A Review". Malaysia, pp. 1-5, [12] M. Ali Ghorbani, R. Khatibi, A. Aytek, O. Makarynskyy and J. Shiri, "Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks", Computers & Geosciences, vol. 36, no. 5, pp , [13] S Abdullah, M Ismail, SY Fong, AN Ahmed. Neural Network Fitting using Levenberg- Marquardt Training Algorithm for PM10 Concentration Forecasting in Kuala Terengganu. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). [14] T. Jayalakshmi and A. Santhakumaran, "Statistical Normalization and Back Propagationfor Classification", International Journal of Computer Theory and Engineering, pp , 2011 [15] O. Makarynskyy, D. Makarynska, M. Kuhn and W. Featherstone, "Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia", Estuarine, Coastal and Shelf Science, vol. 61, no. 2, pp , editor@iaeme.com

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