Abstract
Artificial beach nourishment is one of the most important environmentally friendly coastal protection methods since it protects the aesthetic and recreational values of the beach and increases its protective properties. Therefore, the main aim of the current study is to assess the accuracy of multivariate adaptive regression splines (MARS) in predicting the parameters, namely sediment transport coefficients (K) and the diffusion rate (Ω), which affect beach nourishment performance. The performance of the MARS was determined by comparison of the models using exponential, linear, and power regression equations trained by conventional regression analyses (CRA) and the teaching-learning based optimization (TLBO) algorithm. In all models, two different input data obtained from the experimental study were used, one dimensional and one non-dimensional. The results presented that the MARS models gave lower error values than the CRA and TLBO models according to the root mean square error, mean absolute error, and scattering index criteria. When the models were evaluated, it was revealed that dimensional and non-dimensional models gave approximate results. We proved that the dimensional and non-dimensional MARS models can be used to estimate the (K) and (Ω) values.
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References
Adamowski J, Chan HF, Prasher SO, Sharda VN (2012) Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J Hydroinf 14(3):731–744. https://doi.org/10.2166/hydro.2011.044
Anthony EJ, Cohen O, Sabatier F (2011) Chronic offshore loss of nourishment on Nice beach, French Riviera: a case of over-nourishment of a steep beach? Coast Eng 58(4):374–383. https://doi.org/10.1016/j.coastaleng.2010.11.001
Balshi MS, Mc Guire AD, Duffy P, Flannigan M, Walsh J, Melillo J (2009) Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Glob Chang Biol 15(3):578–600. https://doi.org/10.1111/j.1365-2486.2008.01679.x
Bayram A, Uzlu E, Kankal M, Dede T (2015) Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm. Environ Earth Sci 73(10):6565–6576. https://doi.org/10.1007/s12665-014-3876-3
Bergeron Y, Cyr D, Girardin MP, Carcaillet C (2011) Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: collating global climate model experiments with sedimentary charcoal data. Int J Wildland Fire 19(8):1127–1139. https://doi.org/10.1071/WF09092
Bergillos RJ, Rodríguez-Delgado C, Ortega-Sánchez M (2017) Advances in management tools for modeling artificial nourishments in mixed beaches. J Mar Syst 172:1–13. https://doi.org/10.1016/j.jmarsys.2017.02.009
Brambilla W, Conforti A, Simeone S, Carrara P, Lanucara S, De Falco G (2019) Data set of submerged sand deposits organised in an interoperable spatial data infrastructure (Western Sardinia, Mediterranean Sea). Earth Syst Sci Data 11:515–527. https://doi.org/10.5194/essd-11-515-2019
Brown JM, Phelps JJ, Barkwith A, Hurst MD, Ellis MA, Plater AJ (2016) The effectiveness of beach mega-nourishment, assessed over three management epochs. J Environ Manag 184:400–408. https://doi.org/10.1016/j.jenvman.2016.09.090
Cooke BC, Jones AR, Goodwin ID, Bishop MJ (2012) Nourishment practices on Australian sandy beaches: a review. J Environ Manag 113:319–327. https://doi.org/10.1016/j.jenvman.2012.09.025
Day P, Das AK (2016) Application of multivariate adaptive regression spline-assisted objective function on optimization of heat transfer rate around a cylinder. Nucl Eng Technol 48(6):1315–1320. https://doi.org/10.1016/j.net.2016.06.011
Dean RG (2002) Beach nourishment: theory and practice, advanced series on ocean engineering. World Scientific, Singapore
Dean RG, Yoo CH (1992) Beach nourishment performance predictions. J Waterw Port Coast Ocean Eng 118:567–586. https://doi.org/10.1061/(ASCE)0733-950X(1992)118:6(567
Dede T (2013) Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization. Struct Multidiscip Optim 48(5):955–964. https://doi.org/10.1007/s00158-013-0936-3
Dede T, Ayvaz Y (2015) Combined size and shape optimization of structures with a new meta-heuristic algorithm. Appl Soft Comput 28:250–258. https://doi.org/10.1016/j.asoc.2014.12.007
Dede T, Togan V (2015) A teaching learning based optimization for truss structures with frequency constraints. Struct Eng Mech 53(4):833–845. https://doi.org/10.12989/sem.2015.53.4.833
Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175. https://doi.org/10.1016/j.atmosres.2016.10.004
Di Risio M, Lisi I, Beltrami GM, De Girolamo P (2010) Physical modeling of the cross-shore short-term evolution of protected and unprotected beach nourishments. Ocean Eng 37(8–9):777–789. https://doi.org/10.1016/j.oceaneng.2010.02.008
Donohue KA (1998) The effects of sand grain size and fill placement geometry on beach nourishment performance, M.Sc. Thesis. University of Florida, USA
Dwarakish GS, Rakshith S, Natesan U (2013) Review on applications of neural network in coastal engineering. Artificial Intelligent Systems and Machine Learning 5(7):324–331
Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H (2015) Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). J Hydrol 529:1590–1600. https://doi.org/10.1016/j.jhydrol.2015.08.025
Finkl CW, Khalil S (2005) Offshore exploration for sand sources: general guidelines and procedural strategies along deltaic coasts. J Coast Res 44:203–233
Forghani A, Peralta RC (2017) Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers. J Hydrol 553:540–548. https://doi.org/10.1016/j.jhydrol.2017.08.012
Frandsen JB, Xhardé RR, Bérubé F, Tremblay OG (2015) Beach nourishment project-phase 1 large scale flume experiments evaluation of cobble-gravel-sand beaches, Technical Report. Institute National de la Recherche Scientifique, Québec, France
Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19(1):1–141. https://doi.org/10.1214/aos/1176347969
Giardino A, Broekema Y, Werf JVD, Rooijen AV, Vousdoukasi M (2015) Physical and numerical modelling of different nourishment designs. In: Proc. 36th IAHR World Congress, The Hague
Hameed M, Sharqi SS, Yaseen ZM, Afan HA, Hussain A, Elshafie A (2017) Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Comput & Applic 28(1):893–905. https://doi.org/10.1007/s00521-016-2404-7
Ishikawa T, Uda T, Miyahara S, Serizawa M, Fukuda M, Hara Y (2014) Recovery of sandy beach by gravel nourishment–example of Ninomiya Coast in Kanagawa Prefecture. In: Proc. 34th Int. Conf. of Coastal Eng. 2014, Japan, Seoul
Kankal M, Uzlu E (2017) Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput & Applic 28(Suppl 1):737–747. https://doi.org/10.1007/s00521-016-2409-2
Kankal M, Uzlu E, Nacar S, Yüksek Ö (2018) Predicting temporal rate coefficient of bar volume using hybrid artificial intelligence approaches. J Mar Sci Technol 23:596–604. https://doi.org/10.1007/s00773-017-0495-1
Karambas TV, Samaras AG (2014) Soft shore protection methods: the use of advanced numerical models in the evaluation of beach nourishment. Ocean Eng 92:129136–129136. https://doi.org/10.1016/j.oceaneng.2014.09.043
Karasu S (2004) Determination of parameters which affect beach nourishment performance, Ph.D. thesis. University of Karadeniz Technical, Trabzon in Turkish
Karasu S, Work PA, Cambazoğlu MK, Yüksek Ö (2008) Coupled longshore and cross-shore models for beach nourishment evolution at laboratory scale. J Waterw Port Coast Ocean Eng 134:30–39. https://doi.org/10.1061/(ASCE)0733-950X(2008)134:1(30
Khuntia S, Mujtaba H, Patra C, Farooq K, Sivakugan N, Das BM (2015) Prediction of compaction parameters of coarse grained soil using multivariate adaptive regression splines (MARS). Int J Geotech Eng 9(1):79–88. https://doi.org/10.1179/1939787914Y.0000000061
Kim KH, Widayati AY (2012) Study on alternatives of sand placement method for beach nourishment project. KSCE J Civ Eng 16(4):478–485. https://doi.org/10.1007/s12205-012-1432-9
Kömürcü Mİ, Özölçer İH, Yüksek Ö, Karasu S (2007) Determination of bar parameters caused by cross-shore sediment movement. Ocean Eng 34(5–6):685–695. https://doi.org/10.1016/j.oceaneng.2006.05.005
Kraus NC, Hanson H, Blomgren SH (1995) Modern functional design of groin systems. In: Proc. 24th Int. Conf. of Coastal Engineering, Kobe, Japan, pp 1327–1342
Kumada T, Uda T, Matsu-ura T, Sumiya M (2010) Field experiment on beach nourishment using gravel at Jinkoji coast. In: Proc. 32th Int. Conf. of Coastal Eng, Shanghai
Kuter S, Akyurek Z, Weber GW (2018) Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines. Remote Sens Environ 205:236–252. https://doi.org/10.1016/j.rse.2017.11.021
Li F, Dyt C, Griffiths C (2006) Multigrain sedimentation/erosion model based on cross-shore equilibrium sediment distribution: application to nourishment design. Estuar Coast Shelf Sci 67(4):664–672. https://doi.org/10.1016/j.ecss.2006.01.006
Lin W, Yu DY, Wang S, Zhang C, Zhang S, Tian H, Luo M, Liu S (2015) Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations. Eng Optim 47(7):994–1007. https://doi.org/10.1080/0305215X.2014.928818
López I, Aragonés L, Villacampa Y, Navarro-González FJ (2018) Gravel beaches nourishment: modelling the equilibrium beach profile. Sci Total Environ 619:772–783. https://doi.org/10.1016/j.scitotenv.2017.11.156
Marinho B, Coelho C, Larson M, Hanson H (2017) Simulating cross-shore evolution towards equilibrium of different beach nourishment schemes. In: Proc. of the Coastal Dynamics 2017, Helsingør, pp 1732–1746
Mohanty B, Tripathy S (2016) A teaching learning based optimization technique for optimal location and size of DG in distribution network. Journal of Electrical Systems and Information Technology 3(1):33–44. https://doi.org/10.1016/j.jesit.2015.11.007
Nacar, S., Kankal, M., and Hınıs, M. A. (2018). “Estimation of daily streamflow using Multivariate Adaptive Regression Splines (MARS)-A case study of Haldizen Stream.” Gümüşhane University Journal of Science and Technology Institute, Vol. 8, No. 1, pp. 38–47, https://doi.org/10.17714/gumusfenbil.311188
National Research Council (1995) Beach nourishment and protection. National Academies Press, Washington
Østergård T, Jensen RL, Maagaard SE (2018) A comparison of six metamodeling techniques applied to building performance simulations. Appl Energy 211:89–103. https://doi.org/10.1016/j.apenergy.2017.10.102
Özmen A, Yılmaz Y, Weber GW (2018) Natural gas consumption forecast with MARS and CMARS models for residential users. Energy Econ 70:357–381. https://doi.org/10.1016/j.eneco.2018.01.022
Pagán JI, López M, López I, Tenza-Abril AJ, Aragonés L (2018) Study of the evolution of gravel beaches nourished with sand. Sci Total Environ 626:87–95. https://doi.org/10.1016/j.scitotenv.2018.01.015
Pelnard-Considere, R. (1956). Essai de theorie de l'evolution des formes de rivage en plages de sable et de galets, Les Energies de la Mer: Compte Rendu Des Quatriemes Journees de L'hydraulique, Question III, rapport 1, 74–1-10
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015
Rogada JR, Barcia LA, Martinez JA, Menendez M, de Cos Juez FJ (2017) Comparative modeling of a parabolic trough collectors solar power plant with MARS models. Energies 11:37. https://doi.org/10.3390/en11010037
Safarinejadian B, Gharibzadeh M, Rakhshan M (2014) An optimized model of electricity price forecasting in the electricity market based on fuzzy time series. System Science and Control Engineering 2(1):677–683. https://doi.org/10.1080/21642583.2014.970733
Samui P (2013) Multivariate adaptive regression spline (MARS) for prediction of elastic modulus of jointed rock mass. Geotech Geol Eng 31:249–253. https://doi.org/10.1007/s10706-012-9584-4
Smith ER, Mohr MC, Chader SA (2017) Laboratory experiments on beach change due to nearshore mound placement. Coast Eng 121:119–128. https://doi.org/10.1016/j.coastaleng.2016.12.010
Suman S (2015) Prediction of pile capacity parameters using functional networks and multivariate adaptive regression splines, M.Sc. Thesis. National Institute of Technology, Odisha
Tefek MF, Uğuz H, Güçyetmez M (2019) A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Comput Applic 31:2939–2954. https://doi.org/10.1007/s00521-017-3244-9
Tiryaki S, Tan H, Bardak S, Kankal M, Nacar S, Peker H (2019) Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood European. J Wood Wood Prod 77:645–659. https://doi.org/10.1007/s00107-019-01416-9
Tonnon PK, Huisman BJA, Stam GN, van Rijn LC (2018) Numerical modelling of erosion rates, life span and maintenance volumes of mega nourishments. Coast Eng 131:51–69. https://doi.org/10.1016/j.coastaleng.2017.10.001
Topal U, Dede T, Öztürk HT (2017) Stacking sequence optimization for maximum fundamental frequency of simply supported antisymmetric laminated composite plates using teaching–learning-based optimization. KSCE J Civ Eng 21(6):2281–2288. https://doi.org/10.1007/s12205-017-0076-1
Uda T, Kumada T, Serizawa M (2004) Predictive model of change in longitudinal profile in beach nourishment using sand of mixed grain size. In: Proc. 29th Int. Conf. of Coastal Eng, Lisbon, pp 3378–3390
USACE (2002) Army Corps of Engineers 2003, Coastal Engineering Manual, Part. V., coastal and hydraulics lab. US Army Engineer Research and Development Center, Vicksburg
Uzlu E, Kankal M, Akpınar A, Dede T (2014a) Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy 75:295303–295303. https://doi.org/10.1016/j.energy.2014.07.078
Uzlu E, Kömürcü Mİ, Kankal M, Dede T, Öztürk HT (2014b) Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Appl Ocean Res 48:103–113. https://doi.org/10.1016/j.apor.2014.08.002
Walton TL Jr, Cheng J, Wang R, Manausa M (2005) Modeling of three beach fill projects. Ocean Eng 32(5–6):557–569. https://doi.org/10.1016/j.oceaneng.2004.09.004
Work PA, Dean RG (1995) Assessment and prediction of beach-nourishment evolution. J Waterw Port Coast Ocean Eng 121:182–189. https://doi.org/10.1061/(ASCE)0733-950X(1995)121:3(182)
Work PA, Rogers EW (1997) Wave transformation for beach nourishment projects. Coast Eng 32:1–18. https://doi.org/10.1016/S0378-3839(97)00004-5
Work PA, Rogers WE (1998) Laboratory study of beach nourishment behavior. J Waterw Port Coast Ocean Eng 124(5):229–237. https://doi.org/10.1061/(ASCE)0733-950X(1998)124:5(229
Yilmaz B, Aras E, Nacar S, Kankal M (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840. https://doi.org/10.1016/j.scitotenv.2018.05.153
Zhang W, Goh ATC (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52. https://doi.org/10.1016/j.gsf.2014.10.003
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The authors dedicate this paper to the memory of the late Dr. Murat İhsan KÖMÜRCÜ.
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Karasu, S., Kankal, M., Nacar, S. et al. Prediction of Parameters which Affect Beach Nourishment Performance Using MARS, TLBO, and Conventional Regression Techniques. Thalassas 36, 245–260 (2020). https://doi.org/10.1007/s41208-019-00173-z
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DOI: https://doi.org/10.1007/s41208-019-00173-z