Advertisement

Water Resources Management

, Volume 30, Issue 7, pp 2391–2404 | Cite as

Estimation of Wind-Driven Coastal Waves Near a Mangrove Forest Using Adaptive Neuro-Fuzzy Inference System

  • Roslan Hashim
  • Chandrabhushan Roy
  • Shahaboddin Shamshirband
  • Shervin Motamedi
  • Arnitza Fitri
  • Dalibor Petković
  • KI-IL Song
Article

Abstract

At the coastline of the Carey Island, mangroves provide natural protection against the wind-driven coastal waves. The area is located at the west Malaysia within the waters of the Straits of Malacca. Recently, its coastline has been exposed to increasing rates of coastal erosion due to mangrove deforestation. In order to provide mitigating measures, it is necessary to study wave characteristics in this region. For this purpose, we collected 5 years (2009 to 2013) of hourly measurements for wind direction, wave height, wind speed and wave period. Moreover, we used the adaptive neuro-fuzzy inference system (ANFIS) to estimate the wave period and height. The model was trained using the measured data. The validation of the model gave satisfactory R2 values of 0.8484 and 0.9496 for wave height and wave period, respectively. The findings from this study suggest that fuzzy logic based technique satisfactorily predicts the differences between multiple inputs and single output in terms of non-linear relationship. The developed model can be used to further study the effect of non-linear wind-driven waves on the depleting coastal mangrove forests in similar tropical and sub-tropical areas. We suggest further research to test the model in different geographical locations, such as in deep-ocean, narrow straits and other coastal sites, which were not covered in this study.

Keywords

Ocean Wave estimation ANFIS Wave height Coastal engineering 

Notes

Acknowledgments

We are thankful of the comments and suggestions from the editor and the reviewers. The authors express their sincere thanks for the funding support they received from HIR-MOHE University of Malaya under Grant No. UM.C/HIR/MOHE/ENG/34 and UM.C/HIR/MOHE/ENG/47.

References

  1. Altunkaynak A, Özger M (2004) Temporal significant wave height estimation from wind speed by perceptron Kalman filtering. Ocean Eng 31:1245–1255CrossRefGoogle Scholar
  2. Alyazichi YM, Jones BG, McLean EJ (2015) Spatial distribution of heavy metal contaminations in Yowie Bay sediments and their environmental impacts. Water Resour Manag VIII 196:363Google Scholar
  3. Ashton A, Murray AB, Arnoult O (2001) Formation of coastline features by large-scale instabilities induced by high-angle waves. Nature 414:296–300CrossRefGoogle Scholar
  4. Ata R, Koçyigit Y (2010) An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Syst Applic 37:5454–5460CrossRefGoogle Scholar
  5. Bobba AG (2012) Ground Water-Surface Water Interface (GWSWI) modeling: recent advances and future challenges. Water Resour Manag 26:4105–4131CrossRefGoogle Scholar
  6. Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28:4747–4763CrossRefGoogle Scholar
  7. Ching YC, Lee YH, Toriman ME, Abdullah M, Yatim BB (2015) Effect of the big flood events on the water quality of the Muar River, Malaysia. Sustain Water Resour Manag 1:97–110CrossRefGoogle Scholar
  8. Deo MC, Jha A, Chaphekar A, Ravikant K (2001) Neural networks for wave forecasting. Ocean Eng 28:889–898CrossRefGoogle Scholar
  9. Enayatifar R, Sadaei HJ, Abdullah AH, Gani A (2013) Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting. Energy Convers Manag 76:1104–1116CrossRefGoogle Scholar
  10. Fernandez R, Bonansea M, Marques M (2014) Monitoring turbid plume behavior from landsat imagery. Water Resour Manag 28:3255–3269CrossRefGoogle Scholar
  11. Hashim R, Kamali B, Tamin NM, Zakaria R (2010) An integrated approach to coastal rehabilitation: mangrove restoration in Sungai Haji Dorani, Malaysia. Estuar Coast Shelf Sci 86:118–124CrossRefGoogle Scholar
  12. Healy TR (2010) Hydrodynamic modelling for mangrove afforestation at Haji Dorani, west coast peninsular Malaysia. The University of WaikatoGoogle Scholar
  13. Helfer F, Sahin O, Lemckert C, Anissimov Y (2013) Salinity gradient energy: a new source of renewable energy for Australia. proceedings of the 8th International Conference of the European Water Resources Association. EWRA, PortoGoogle Scholar
  14. Jain P, Deo M (2006) Neural networks in ocean engineering. Ships Offshore Struct 1:25–35CrossRefGoogle Scholar
  15. Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. Syst, Man Cybernet, IEEE Trans 23:665–685CrossRefGoogle Scholar
  16. Kaliraj S, Chandrasekar N, Magesh N (2014) Impacts of wave energy and littoral currents on shoreline erosion/accretion along the south-west coast of Kanyakumari, Tamil Nadu using DSAS and geospatial technology. Environ Earth Sci 71:4523–4542CrossRefGoogle Scholar
  17. Kamali B, Hashim R (2011) Mangrove restoration without planting. Ecol Eng 37:387–391CrossRefGoogle Scholar
  18. Kamranzad B, Etemad-Shahidi A, Kazeminezhad MH (2011) Wave height forecasting in Dayyer, the Persian Gulf. Ocean Eng 38:248–255CrossRefGoogle Scholar
  19. Karamouz M, Zahmatkesh Z, Goharian E et al. (2014) Combined impact of inland and coastal floods: mapping knowledge base for development of planning strategies. J Water Resour Plan Manag 04014098Google Scholar
  20. Kazeminezhad M, Etemad-Shahidi A, Mousavi S (2005) Application of fuzzy inference system in the prediction of wave parameters. Ocean Eng 32:1709–1725CrossRefGoogle Scholar
  21. Khan MR, Voss CI, Yu W, Michael HA (2014) Water resources management in the Ganges basin: a comparison of three strategies for conjunctive use of groundwater and surface water. Water Resour Manag 28:1235–1250CrossRefGoogle Scholar
  22. Kim D, Grant WE, Cairns DM, Bartholdy J (2013) Effects of the North Atlantic Oscillation and wind waves on salt marsh dynamics in the Danish Wadden Sea: a quantitative model as proof of concept. Geo-Mar Lett 33:253–261CrossRefGoogle Scholar
  23. Kim S, Singh VP, Seo Y, Kim HS (2014) Modeling nonlinear monthly evapotranspiration using soft computing and data reconstruction techniques. Water Resour Manag 28:185–206CrossRefGoogle Scholar
  24. Kumar N, Feddersen F, Uchiyama Y et al. (2015) Midshelf to surfzone coupled ROMS-SWAN model data comparison of waves, currents, and temperature: diagnosis of subtidal forcings and response. J Phys OceanogGoogle Scholar
  25. Lee SC, Hashim R, Motamedi S et al. (2014) Utilization of geotextile tube for sandy and muddy coastal management: a review. Sci World J 2014Google Scholar
  26. Mahjoobi J, Mosabbeb EA (2009) Prediction of significant wave height using regressive support vector machines. Ocean Eng 36:339–347CrossRefGoogle Scholar
  27. Makarynskyy O (2004) Improving wave predictions with artificial neural networks. Ocean Eng 31:709–724CrossRefGoogle Scholar
  28. Mason E, Pascual A, McWilliams JC et al. (2014) A first look at a new interannual ROMS solution for the Canary BasinGoogle Scholar
  29. Masseran N, Razali A, Ibrahim K, Zin WW (2012) Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia. Energy 37:649–656CrossRefGoogle Scholar
  30. Motamedi S, Hashim R, Zakaria R et al. (2014) Long-term assessment of an innovative mangrove rehabilitation project: case study on Carey Island, Malaysia. Sci World J 2014Google Scholar
  31. Motamedi S, Roy C, Shamshirband S, Hashim R, Petković D, Song K-I (2015a) Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology. Ultrasonics 61:103–113CrossRefGoogle Scholar
  32. Motamedi S, Shamshirband S, Hashim R, Petković D, Roy C (2015b) Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies. Eng Struct 98:49–58CrossRefGoogle Scholar
  33. Özger M (2010) Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng 37:1443–1451CrossRefGoogle Scholar
  34. Özger M, Şen Z (2007a) Prediction of wave parameters by using fuzzy logic approach. Ocean Eng 34:460–469CrossRefGoogle Scholar
  35. Özger M, Şen Z (2007b) Triple diagram method for the prediction of wave height and period. Ocean Eng 34:1060–1068CrossRefGoogle Scholar
  36. Petković D, Issa M, Pavlović ND, Pavlović NT, Zentner L (2012a) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst Applic 39:9477–9482CrossRefGoogle Scholar
  37. Petković D, Issa M, Pavlović ND, Zentner L, Ćojbašić Ž (2012b) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Applic 39:13295–13304CrossRefGoogle Scholar
  38. Putri MR, Pohlmann T (2014) Lagrangian model simulation of passive tracer dispersion in the Siak Estuary and Malacca Strait. Asian J Water, Environ Pollut 11:67–74Google Scholar
  39. Sabatier F (2007) US Army Corps of Engineers, Coastal Engineering Manual (CEM), Engineer Manual 1110-2-1100. US Army Corps of Engineers, Washington, DC (6 volumes) Méditerranée Revue géographique des pays méditerranéens/J Mediterranean Geography:146Google Scholar
  40. Shamshirband S, Petković D, Hashim R, Motamedi S (2014) Adaptive neuro-fuzzy methodology for noise assessment of wind turbine. PLoS ONE 9(7):e103414. doi: 10.1371/journal.pone.0103414 CrossRefGoogle Scholar
  41. Singh R, Kainthola A, Singh T (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45CrossRefGoogle Scholar
  42. Sopian K, Othman MH, Wirsat A (1995) The wind energy potential of Malaysia. Renew Energy 6:1005–1016CrossRefGoogle Scholar
  43. Sugeno M, Kang G (1988) Structure identification of fuzzy model Fuzzy sets and systems 28:15–33Google Scholar
  44. Taylor PK, Yelland MJ (2001) The dependence of sea surface roughness on the height and steepness of the waves. J Phys Oceanogr 31:572–590CrossRefGoogle Scholar
  45. Tian L, Collins C (2005) Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15:1305–1320CrossRefGoogle Scholar
  46. Tiang TL, Ishak D (2012) Technical review of wind energy potential as small-scale power generation sources in Penang Island Malaysia. Renew Sust Energ Rev 16:3034–3042CrossRefGoogle Scholar
  47. Toba Y, Iida N, Kawamura H, Ebuchi N, Jones IS (1990) Wave dependence of sea-surface wind stress. J Phys Oceanogr 20:705–721CrossRefGoogle Scholar
  48. Umeyama M, Nguyen DH, Minh CV, Le XR, Motani S (2014) A comprehensive approach for estimating hydraulic quantities in a multi-branched estuarine system. Water Resour Manag 28:3937–3955CrossRefGoogle Scholar
  49. Zamani A, Solomatine D, Azimian A, Heemink A (2008) Learning from data for wind–wave forecasting. Ocean Eng 35:953–962CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Roslan Hashim
    • 1
    • 2
  • Chandrabhushan Roy
    • 2
  • Shahaboddin Shamshirband
    • 3
  • Shervin Motamedi
    • 1
    • 2
  • Arnitza Fitri
    • 2
  • Dalibor Petković
    • 4
  • KI-IL Song
    • 5
  1. 1.Institute of Ocean and Earth SciencesUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  3. 3.Department of Computer System and Information Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  4. 4.Faculty of Mechanical Engineering, Deparment for Mechatronics and ControlUniversity of NišNišSerbia
  5. 5.Department of Civil EngineeringInha UniversityNam-guSouth Korea

Personalised recommendations