메뉴 건너뛰기




Volumn 534, Issue , 2016, Pages 104-112

Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

Author keywords

Chemical oxygen demand (COD); Estimation; Least square support vector machine; M5 model tree; Mathematical modeling; Multivariate adaptive regression splines

Indexed keywords

CHEMICAL OXYGEN DEMAND; ESTIMATION; FORESTRY; MATHEMATICAL MODELS; MEAN SQUARE ERROR; OIL SPILLS; POLLUTION; REGRESSION ANALYSIS; RIVERS; SPLINES; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS); WATER POLLUTION; WATER QUALITY; WATER RESOURCES;

EID: 84953791253     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.12.014     Document Type: Article
Times cited : (307)

References (49)
  • 2
    • 78049531014 scopus 로고    scopus 로고
    • Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)
    • Andres J.D., Lorca P., Juez F.J., Sánchez-Lasheras F. Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Syst. Appl. 2010, 38:1866-1875.
    • (2010) Expert Syst. Appl. , vol.38 , pp. 1866-1875
    • Andres, J.D.1    Lorca, P.2    Juez, F.J.3    Sánchez-Lasheras, F.4
  • 3
    • 84939437333 scopus 로고    scopus 로고
    • Applicability of a fuzzy genetic system for crack diagnosis in Timoshenko beams
    • Aydin K., Kisi O. Applicability of a fuzzy genetic system for crack diagnosis in Timoshenko beams. J. Comput. Civ. Eng. 2014, 04014073. 10.1061/(ASCE)CP.1943-5487.0000385.
    • (2014) J. Comput. Civ. Eng.
    • Aydin, K.1    Kisi, O.2
  • 5
    • 84871950182 scopus 로고    scopus 로고
    • Water quality index and fractal dimension analysis of water parameters
    • Bhardwaj R., Parmar K.S. Water quality index and fractal dimension analysis of water parameters. Int. J. Environ. Sci. Technol. 2013, 10:151-164.
    • (2013) Int. J. Environ. Sci. Technol. , vol.10 , pp. 151-164
    • Bhardwaj, R.1    Parmar, K.S.2
  • 6
    • 84930795153 scopus 로고    scopus 로고
    • Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management
    • Bhardwaj R., Parmar K.S. Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management. Environ. Sci. Pollut. Res. 2015, 22:397-414. 10.1007/s11356-014-3346-1.
    • (2015) Environ. Sci. Pollut. Res. , vol.22 , pp. 397-414
    • Bhardwaj, R.1    Parmar, K.S.2
  • 7
    • 12144264770 scopus 로고    scopus 로고
    • Neural networks and M5 model trees in modeling water level-discharge relationship
    • Bhattacharya B., Solomatine D.P. Neural networks and M5 model trees in modeling water level-discharge relationship. Neurocomputing 2005, 63:381-396.
    • (2005) Neurocomputing , vol.63 , pp. 381-396
    • Bhattacharya, B.1    Solomatine, D.P.2
  • 8
    • 33645967437 scopus 로고    scopus 로고
    • Machine learning in sedimentation modelling
    • Bhattacharya B., Solomatine D.P. Machine learning in sedimentation modelling. Neural Netw. 2006, 19:208-214.
    • (2006) Neural Netw. , vol.19 , pp. 208-214
    • Bhattacharya, B.1    Solomatine, D.P.2
  • 9
    • 84892864342 scopus 로고    scopus 로고
    • Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams
    • Cheng M.Y., Cao M.T. Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Eng. Appl. Artif. Intell. 2014, 28:86-96.
    • (2014) Eng. Appl. Artif. Intell. , vol.28 , pp. 86-96
    • Cheng, M.Y.1    Cao, M.T.2
  • 10
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes C., Vapnik V. Support vector networks. Mach. Learn. 1995, 20:273-297.
    • (1995) Mach. Learn. , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 11
  • 12
    • 84886565039 scopus 로고    scopus 로고
    • Evaluation of the parameters of water quality with wavelet techniques
    • Dokmen F., Aslan J. Evaluation of the parameters of water quality with wavelet techniques. Water Resour. Manage. 2013, 27:4977-4988.
    • (2013) Water Resour. Manage. , vol.27 , pp. 4977-4988
    • Dokmen, F.1    Aslan, J.2
  • 13
    • 82355192118 scopus 로고    scopus 로고
    • Attribution of the river flow growth in the Plata Basin
    • Doyle M.E., Barros V.R. Attribution of the river flow growth in the Plata Basin. Int. J. Climatol. 2011, 31:2234-2248.
    • (2011) Int. J. Climatol. , vol.31 , pp. 2234-2248
    • Doyle, M.E.1    Barros, V.R.2
  • 14
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 1991, 19:1-67.
    • (1991) Ann. Stat. , vol.19 , pp. 1-67
    • Friedman, J.H.1
  • 16
    • 58349097333 scopus 로고    scopus 로고
    • Prediction of aeration efficiency on stepped cascades by using least square support vector machines
    • Hanbay D., Baylar A., Batan M. Prediction of aeration efficiency on stepped cascades by using least square support vector machines. Expert Syst. Appl. 2009, 36:4248-4252.
    • (2009) Expert Syst. Appl. , vol.36 , pp. 4248-4252
    • Hanbay, D.1    Baylar, A.2    Batan, M.3
  • 17
    • 33644676683 scopus 로고    scopus 로고
    • Predicting engine reliability by support vector machines
    • Hong W.C., Pai P.F. Predicting engine reliability by support vector machines. Int. J. Adv. Manuf. Technol. 2006, 28:154-161.
    • (2006) Int. J. Adv. Manuf. Technol. , vol.28 , pp. 154-161
    • Hong, W.C.1    Pai, P.F.2
  • 18
    • 61749102355 scopus 로고    scopus 로고
    • Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks
    • Kisi O. Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol. Process. 2009, 23:213-223.
    • (2009) Hydrol. Process. , vol.23 , pp. 213-223
    • Kisi, O.1
  • 19
    • 84862679428 scopus 로고    scopus 로고
    • Suspended sediment modeling using genetic programming and soft computing techniques
    • Kisi O., Dailr A.H., Cimen M., Shiri J. Suspended sediment modeling using genetic programming and soft computing techniques. J. Hydrol. 2012, 450-451:48-58.
    • (2012) J. Hydrol. , pp. 48-58
    • Kisi, O.1    Dailr, A.H.2    Cimen, M.3    Shiri, J.4
  • 20
    • 84871475008 scopus 로고    scopus 로고
    • Modeling monthly pan evaporations using fuzzy genetic approach
    • Kisi O., Tombul M. Modeling monthly pan evaporations using fuzzy genetic approach. J. Hydrol. 2013, 477:203-212.
    • (2013) J. Hydrol. , vol.477 , pp. 203-212
    • Kisi, O.1    Tombul, M.2
  • 21
    • 84879221930 scopus 로고    scopus 로고
    • Least squares support vector machine for modeling daily reference evapotranspiration
    • Kisi O. Least squares support vector machine for modeling daily reference evapotranspiration. Irrig. Sci. 2013, 31:611-619.
    • (2013) Irrig. Sci. , vol.31 , pp. 611-619
    • Kisi, O.1
  • 22
    • 33750002678 scopus 로고    scopus 로고
    • Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions
    • Leathwick J.R., Elith J., Hastie T. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol. Modell. 2006, 199:188-196.
    • (2006) Ecol. Modell. , vol.199 , pp. 188-196
    • Leathwick, J.R.1    Elith, J.2    Hastie, T.3
  • 23
    • 26444554549 scopus 로고    scopus 로고
    • Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
    • Lee T.S., Chiu C.C., Chou Y.C., Lu C.J. Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Comput. Stat. Data Anal. 2006, 50:1113-1130.
    • (2006) Comput. Stat. Data Anal. , vol.50 , pp. 1113-1130
    • Lee, T.S.1    Chiu, C.C.2    Chou, Y.C.3    Lu, C.J.4
  • 24
    • 34548864403 scopus 로고    scopus 로고
    • Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research
    • Liu R., Chen J.M., Liu J., Deng F., Sun R. Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research. J. Environ. Manage. 2007, 85:649-658.
    • (2007) J. Environ. Manage. , vol.85 , pp. 649-658
    • Liu, R.1    Chen, J.M.2    Liu, J.3    Deng, F.4    Sun, R.5
  • 25
    • 84890090280 scopus 로고    scopus 로고
    • Improving real time flood forecasting using fuzzy inference system
    • Lohani A.K., Goel N.K., Bhatia K.K.S. Improving real time flood forecasting using fuzzy inference system. J. Hydrol. 2014, 509:25-41.
    • (2014) J. Hydrol. , vol.509 , pp. 25-41
    • Lohani, A.K.1    Goel, N.K.2    Bhatia, K.K.S.3
  • 26
    • 84862642100 scopus 로고    scopus 로고
    • Comparative study of different wavelets for hydrologic forecasting
    • Maheshwaran R., Khosa R. Comparative study of different wavelets for hydrologic forecasting. Comput. Geosci. 2012, 46:284-295.
    • (2012) Comput. Geosci. , vol.46 , pp. 284-295
    • Maheshwaran, R.1    Khosa, R.2
  • 27
    • 84871523656 scopus 로고    scopus 로고
    • Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics
    • Maheshwaran R., Khosa R. Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics. Comput. Geosci. 2013, 52:422-436.
    • (2013) Comput. Geosci. , vol.52 , pp. 422-436
    • Maheshwaran, R.1    Khosa, R.2
  • 28
    • 0004255908 scopus 로고    scopus 로고
    • The McGraw-Hill Companies Inc., New York, NY, 414
    • Mitchell T.M. Machine Learning 1997, The McGraw-Hill Companies Inc., New York, NY, 414.
    • (1997) Machine Learning
    • Mitchell, T.M.1
  • 29
    • 1942490118 scopus 로고    scopus 로고
    • A neuro fuzzy computing technique for modeling hydrological time series
    • Nayak P.C., Sudheer K.P., Ranjan D.M., Ramasastri K.S. A neuro fuzzy computing technique for modeling hydrological time series. J. Hydrol. 2004, 291:52-66.
    • (2004) J. Hydrol. , vol.291 , pp. 52-66
    • Nayak, P.C.1    Sudheer, K.P.2    Ranjan, D.M.3    Ramasastri, K.S.4
  • 30
    • 65449173509 scopus 로고    scopus 로고
    • M5 model tree based modelling of reference evapotranspiration
    • Pal M., Deswal S. M5 model tree based modelling of reference evapotranspiration. Hydrol. Process. 2009, 23:1437-1443.
    • (2009) Hydrol. Process. , vol.23 , pp. 1437-1443
    • Pal, M.1    Deswal, S.2
  • 32
    • 84893657962 scopus 로고    scopus 로고
    • Wavelet and statistical analysis of river water quality parameters
    • Parmar K.S., Bhadwaj R. Wavelet and statistical analysis of river water quality parameters. Appl. Math. Comput. 2013, 219:10172-10182.
    • (2013) Appl. Math. Comput. , vol.219 , pp. 10172-10182
    • Parmar, K.S.1    Bhadwaj, R.2
  • 33
    • 34447527322 scopus 로고    scopus 로고
    • Wavelet and neuro fuzzy conjunction model for precipitation forecasting
    • Partal T., Kisi O. Wavelet and neuro fuzzy conjunction model for precipitation forecasting. J. Hydrol. 2007, 342:199-212.
    • (2007) J. Hydrol. , vol.342 , pp. 199-212
    • Partal, T.1    Kisi, O.2
  • 35
    • 66449136989 scopus 로고    scopus 로고
    • Time series prediction using support vector machines: a survey
    • Sapankevych N.I., Sankar R. Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 2009, 4:24-38.
    • (2009) IEEE Comput. Intell. Mag. , vol.4 , pp. 24-38
    • Sapankevych, N.I.1    Sankar, R.2
  • 36
    • 0039595510 scopus 로고    scopus 로고
    • Forecasting recessions: can we do better on MARS?
    • Sephton P. Forecasting recessions: can we do better on MARS?. Fed. Reserve Bank St. Louis Rev. 2001, 83:39-49.
    • (2001) Fed. Reserve Bank St. Louis Rev. , vol.83 , pp. 39-49
    • Sephton, P.1
  • 38
    • 33747356756 scopus 로고    scopus 로고
    • Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques
    • Sharda V., Prasher S.O., Patel R.M., Ojavasi P.R., Prakash C. Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques. Agric. Water Manage. 2006, 83:233-242.
    • (2006) Agric. Water Manage. , vol.83 , pp. 233-242
    • Sharda, V.1    Prasher, S.O.2    Patel, R.M.3    Ojavasi, P.R.4    Prakash, C.5
  • 39
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Smola J.A., Bernhard Scholkopf A tutorial on support vector regression. Stat. Comput. 2004, 14:199-222.
    • (2004) Stat. Comput. , vol.14 , pp. 199-222
    • Smola, J.A.1    Bernhard, S.2
  • 40
    • 10244261532 scopus 로고    scopus 로고
    • M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China
    • Solomatine D.P., Xue Y. M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China. J. Hydrol. Eng. 2004, 9:491-501.
    • (2004) J. Hydrol. Eng. , vol.9 , pp. 491-501
    • Solomatine, D.P.1    Xue, Y.2
  • 41
    • 0037565156 scopus 로고    scopus 로고
    • Model trees as an alternative to neural networks in rainfall-runoff modelling
    • Solomatine D.P., Dulal K.N. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 2003, 48:399-411.
    • (2003) Hydrol. Sci. J. , vol.48 , pp. 399-411
    • Solomatine, D.P.1    Dulal, K.N.2
  • 42
    • 84903716142 scopus 로고    scopus 로고
    • Statistical analysis of aerosols over the Gangetic-Himalayan region using ARIMA model based on long-term MODIS observations
    • Soni K., Kapoor S., Parmar K.S., Kaskaoutis D.G. Statistical analysis of aerosols over the Gangetic-Himalayan region using ARIMA model based on long-term MODIS observations. Atmos. Res. 2014, 149:174-192.
    • (2014) Atmos. Res. , vol.149 , pp. 174-192
    • Soni, K.1    Kapoor, S.2    Parmar, K.S.3    Kaskaoutis, D.G.4
  • 43
    • 0005906715 scopus 로고    scopus 로고
    • Support vector machines: a nonlinear modeling and control perspective
    • Suykens J.A.K. Support vector machines: a nonlinear modeling and control perspective. Eur. J. Control 2001, 7:311-327.
    • (2001) Eur. J. Control , vol.7 , pp. 311-327
    • Suykens, J.A.K.1
  • 44
    • 0032638628 scopus 로고    scopus 로고
    • Least square support vector machine classifiers
    • Suykens J.A.K., Vandewalle J. Least square support vector machine classifiers. Neural Process. Lett. 1999, 9:293-300.
    • (1999) Neural Process. Lett. , vol.9 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 45
    • 68349105875 scopus 로고    scopus 로고
    • A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
    • Wang W., Chau K., Cheng C., Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol. 2009, 374:294-306.
    • (2009) J. Hydrol. , vol.374 , pp. 294-306
    • Wang, W.1    Chau, K.2    Cheng, C.3    Qiu, L.4
  • 46
    • 34748907750 scopus 로고    scopus 로고
    • Adaptive fuzzy modeling versus artificial neural networks
    • Wieland R., Mirschel W. Adaptive fuzzy modeling versus artificial neural networks. Environ. Modell. Softw. 2008, 23:215-224.
    • (2008) Environ. Modell. Softw. , vol.23 , pp. 215-224
    • Wieland, R.1    Mirschel, W.2
  • 47
    • 29144507028 scopus 로고    scopus 로고
    • An AGO-SVM drift modeling method for a dynamically tuned gyroscope
    • Xu G., Tian W., Jin Z. An AGO-SVM drift modeling method for a dynamically tuned gyroscope. Meas. Sci. Technol. 2006, 17:161-167.
    • (2006) Meas. Sci. Technol. , vol.17 , pp. 161-167
    • Xu, G.1    Tian, W.2    Jin, Z.3
  • 48
    • 0042262564 scopus 로고    scopus 로고
    • A multivariate adaptive regression splines model for simulation of pesticide transport in soils
    • Yang C.C., Prasher S.O., Lacroix R., Kim S.H. A multivariate adaptive regression splines model for simulation of pesticide transport in soils. Biosyst. Eng. 2003, b1:9-15.
    • (2003) Biosyst. Eng. , vol.b1 , pp. 9-15
    • Yang, C.C.1    Prasher, S.O.2    Lacroix, R.3    Kim, S.H.4
  • 49
    • 4243158375 scopus 로고    scopus 로고
    • Application of multivariate adaptive regression splines (MARS) to simulate soil temperature
    • Yang C.C., Prasher S.O., Lacroix R., Kim S.H. Application of multivariate adaptive regression splines (MARS) to simulate soil temperature. Trans. ASAE 2004, 47:881-887.
    • (2004) Trans. ASAE , vol.47 , pp. 881-887
    • Yang, C.C.1    Prasher, S.O.2    Lacroix, R.3    Kim, S.H.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.