Skip to main content
Top
Published in: Earth Science Informatics 1/2021

21-01-2021 | Research Article

A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods

Authors: Mahsa Sheikh Kazemi, Mohammad Ebrarim Banihabib, Jaber Soltani

Published in: Earth Science Informatics | Issue 1/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Since sediment concentration is an effective factor on increasing debris flood’s peak flow and damages from floods, developing new models to predict the sediment concentration of debris floods has crucial importance. In this study, a hybrid SVR-PSO model was proposed to predict the concentration of sediment in typical and debris floods, and it was examined in three basins located in Gilan, Mazandaran, and Tehran Provinces, Iran. Mean elevation and slope of the basin, the area of the basin, current day’s rainfall, the rainfall of previous days (1–3 days before flood) for all rain-gauge stations of the basins, as well as the discharge of the previous day, were used as the input variables of the model. Then, various combinations of variables were tested to assess the factors influencing the concentration of sediment in typical and debris floods in order to find the best variable combination with a high performance in predicting the concentration of sediment in the studied floods. The results showed that basin elevation, current day’s rainfall, previous day’s discharge, rainfall of the previous day, basin area, rainfall of the previous two days, basin slope, and rainfall of the previous three days were the key factors influencing the concentration of sediment in typical and debris floods, respectively. Coefficient of determination, root mean square error, and mean absolute percentage error were estimated 0.96, 0.003, and 14.38% for the proposed model at the testing phase, respectively. This implies model’s good performance for predicting the concentration of sediment in typical and debris floods so that the present model can provide reliable predictions of flood character in basins.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
go back to reference Adamowski J (2013) Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India. Annals of Warsaw University of Life Sciences-SGGW. Land Reclamation 45(1):71–83 Adamowski J (2013) Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India. Annals of Warsaw University of Life Sciences-SGGW. Land Reclamation 45(1):71–83
go back to reference Banihabib ME, Bahram E (2009) Experimental analyses of sedimentation in the slit dam Reservoir. World Environmental and Water Resources Congress, ASCE, Great Rivers 5845–5856 Banihabib ME, Bahram E (2009) Experimental analyses of sedimentation in the slit dam Reservoir. World Environmental and Water Resources Congress, ASCE, Great Rivers 5845–5856
go back to reference Banihabib ME, Forghani A (2017) An assessment framework for the mitigation effects of check dams on debris flow. Catena 152:277–284CrossRef Banihabib ME, Forghani A (2017) An assessment framework for the mitigation effects of check dams on debris flow. Catena 152:277–284CrossRef
go back to reference Banihabib ME et al (2020) Bayesian networks model for identification of the effective variables in the forecasting of debris flows occurrence. Environ Earth Sci 79:1–15CrossRef Banihabib ME et al (2020) Bayesian networks model for identification of the effective variables in the forecasting of debris flows occurrence. Environ Earth Sci 79:1–15CrossRef
go back to reference Byun H, Lee S-W (2002) Applications of support vector machines for pattern recognition: a survey. International Workshop on Support Vector Machines, Springer 2388:213–236 Byun H, Lee S-W (2002) Applications of support vector machines for pattern recognition: a survey. International Workshop on Support Vector Machines, Springer 2388:213–236
go back to reference Cannon S et al (2007) Storm rainfall conditions for floods and debris flows from recently burned basins in southwestern Colorado and Southern California. AGUFM 2007:H43F–H1692F Cannon S et al (2007) Storm rainfall conditions for floods and debris flows from recently burned basins in southwestern Colorado and Southern California. AGUFM 2007:H43F–H1692F
go back to reference Chen K-Y, Wang C-H (2007) Support vector regression with genetic algorithms in forecasting tourism demand. Tour Manag 28(1):215–226CrossRef Chen K-Y, Wang C-H (2007) Support vector regression with genetic algorithms in forecasting tourism demand. Tour Manag 28(1):215–226CrossRef
go back to reference Chen S-T, Yu PS, Tang YH (2010) Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. J Hydrol 385(1–4):13–22CrossRef Chen S-T, Yu PS, Tang YH (2010) Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. J Hydrol 385(1–4):13–22CrossRef
go back to reference Crosta GB, Frattini P (2008) Rainfall-induced landslides and debris flows. Hydrol Processes: An Int J 22(4):473–477CrossRef Crosta GB, Frattini P (2008) Rainfall-induced landslides and debris flows. Hydrol Processes: An Int J 22(4):473–477CrossRef
go back to reference Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genet 4(1):31 Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genet 4(1):31
go back to reference Fattahi H, Karimpouli S (2016) Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20(5):1075–1094CrossRef Fattahi H, Karimpouli S (2016) Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20(5):1075–1094CrossRef
go back to reference Fattahi H, Gholami A, Amiribakhtiar MS, Moradi S (2015) Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search. Neural Comput Applic 26(4):789–798CrossRef Fattahi H, Gholami A, Amiribakhtiar MS, Moradi S (2015) Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search. Neural Comput Applic 26(4):789–798CrossRef
go back to reference Gholami R, Moradzadeh A, Maleki S, Amiri S, Hanachi J (2014) Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J Pet Sci Eng 122:643–656CrossRef Gholami R, Moradzadeh A, Maleki S, Amiri S, Hanachi J (2014) Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J Pet Sci Eng 122:643–656CrossRef
go back to reference Hamel LH (2009) Knowledge discovery with support vector machines. John Wiley & Sons, Inc., Hoboken, NJ Hamel LH (2009) Knowledge discovery with support vector machines. John Wiley & Sons, Inc., Hoboken, NJ
go back to reference Hassan-Esfahani L, Banihabib ME (2016) The impact of slit and detention dams on debris flow control using GSTARS 3.0. Environ Earth Sci 75(4):328CrossRef Hassan-Esfahani L, Banihabib ME (2016) The impact of slit and detention dams on debris flow control using GSTARS 3.0. Environ Earth Sci 75(4):328CrossRef
go back to reference Hirano M (1997) Prediction of debris flow for warning and evacuation, In: Lecture notes in earth sciences, recent developments on debris flows, edited by: Armanini, A. and Michiue, M. Springer, Berlin Heidelberg, New York 64:7–26 Hirano M (1997) Prediction of debris flow for warning and evacuation, In: Lecture notes in earth sciences, recent developments on debris flows, edited by: Armanini, A. and Michiue, M. Springer, Berlin Heidelberg, New York 64:7–26
go back to reference Hirano M, et al. (1997) Estimation of hazard area due to debris flow. In: Debris-Flow Hazards Mitigation: Mechanics, Prediction, and Assessment; edited by Chen, C-L., Proc. 1st international conference, San Francisco, California. American Society of Civil Engineers 697–706 Hirano M, et al. (1997) Estimation of hazard area due to debris flow. In: Debris-Flow Hazards Mitigation: Mechanics, Prediction, and Assessment; edited by Chen, C-L., Proc. 1st international conference, San Francisco, California. American Society of Civil Engineers 697–706
go back to reference Hu W, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43(1):155–172CrossRef Hu W, Yan L, Liu K, Wang H (2016) A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43(1):155–172CrossRef
go back to reference Huang C-L, Dun J-F (2008) A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391CrossRef Huang C-L, Dun J-F (2008) A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391CrossRef
go back to reference Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240CrossRef Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240CrossRef
go back to reference Jan C-D, Chen C-L (2005) Debris flows caused by typhoon herb in Taiwan. In: Jakob, M., Hungr, O. (Eds), Debris-Flow Hazards and Related Phenomena. Springer, Berlin, Heidelberg 539–563 Jan C-D, Chen C-L (2005) Debris flows caused by typhoon herb in Taiwan. In: Jakob, M., Hungr, O. (Eds), Debris-Flow Hazards and Related Phenomena. Springer, Berlin, Heidelberg 539–563
go back to reference Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theory Eng 3(1):1793–8201 Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theory Eng 3(1):1793–8201
go back to reference Kalanaki M, Soltani J (2013) Simulation and performance assessment between hybrid algorithms SVR-CACO and SVR-CGA to more accurate predicting of the pipe failure rates. J Novel Appl Sci 2(S3):1054–1063 Kalanaki M, Soltani J (2013) Simulation and performance assessment between hybrid algorithms SVR-CACO and SVR-CGA to more accurate predicting of the pipe failure rates. J Novel Appl Sci 2(S3):1054–1063
go back to reference Kalanaki M et al (2013) The use of hybrid SVR-PSO model to predict pipes failure rates. Int J Sci Eng Res 4(11):1022–1025 Kalanaki M et al (2013) The use of hybrid SVR-PSO model to predict pipes failure rates. Int J Sci Eng Res 4(11):1022–1025
go back to reference Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks. IEEE Service Center, Piscataway, NJ 4:1942–1948 Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks. IEEE Service Center, Piscataway, NJ 4:1942–1948
go back to reference Kern AN, Addison P, Oommen T, Salazar SE, Coffman RA (2017) Machine learning based predictive modeling of debris flow probability following wildfire in the intermountain Western United States. Math Geosci 49(6):717–735CrossRef Kern AN, Addison P, Oommen T, Salazar SE, Coffman RA (2017) Machine learning based predictive modeling of debris flow probability following wildfire in the intermountain Western United States. Math Geosci 49(6):717–735CrossRef
go back to reference Lin J-W, Chen CW, Peng CY (2012) Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat Hazards 64(1):273–282CrossRef Lin J-W, Chen CW, Peng CY (2012) Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat Hazards 64(1):273–282CrossRef
go back to reference Nikolopoulos EI, Destro E, Bhuiyan MAE, Borga M, Anagnostou EN (2018) Evaluation of predictive models for post-fire debris flow occurrence in the western United States. Nat Hazards Earth Syst Sci 18(9):2331–2343CrossRef Nikolopoulos EI, Destro E, Bhuiyan MAE, Borga M, Anagnostou EN (2018) Evaluation of predictive models for post-fire debris flow occurrence in the western United States. Nat Hazards Earth Syst Sci 18(9):2331–2343CrossRef
go back to reference Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRef Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRef
go back to reference Ranaee V, Ebrahimzadeh A, Ghaderi R (2010) Application of the PSO–SVM model for recognition of control chart patterns. ISA Trans 49(4):577–586CrossRef Ranaee V, Ebrahimzadeh A, Ghaderi R (2010) Application of the PSO–SVM model for recognition of control chart patterns. ISA Trans 49(4):577–586CrossRef
go back to reference Riazi A, Türker U (2018) A genetic algorithm-based search space splitting pattern and its application in hydraulic and coastal engineering problems. Neural Comput & Applic 30(12):3603–3612 Riazi A, Türker U (2018) A genetic algorithm-based search space splitting pattern and its application in hydraulic and coastal engineering problems. Neural Comput & Applic 30(12):3603–3612
go back to reference Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a GSA-SVM hybrid system. Math Comput Model 57(1–2):270–278CrossRef Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a GSA-SVM hybrid system. Math Comput Model 57(1–2):270–278CrossRef
go back to reference Senoo K et al (1985) Rainfall indexes for debris flow warning evacuating program. Shin-Sabo 38(2):16–21 Senoo K et al (1985) Rainfall indexes for debris flow warning evacuating program. Shin-Sabo 38(2):16–21
go back to reference Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In Proceeding of IEEE International Conference on Evolutionary Computation. IEEE world congress on computational intelligence, pp 69–73 Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In Proceeding of IEEE International Conference on Evolutionary Computation. IEEE world congress on computational intelligence, pp 69–73
go back to reference Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222CrossRef Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222CrossRef
go back to reference Soebroto AA, Cholissodin I, Frestantiya MT, Arief ZE (2018) Integration method of local-global SVR and parallel time variant PSO in water level forecasting for flood early warning system. Telkomnika 16(3):1193–1200CrossRef Soebroto AA, Cholissodin I, Frestantiya MT, Arief ZE (2018) Integration method of local-global SVR and parallel time variant PSO in water level forecasting for flood early warning system. Telkomnika 16(3):1193–1200CrossRef
go back to reference Takahashi T, Das DK (2014) Debris flow: mechanics, prediction and countermeasures,2nd edition. CRC Press/Balkema, Leiden, the Netherlands Takahashi T, Das DK (2014) Debris flow: mechanics, prediction and countermeasures,2nd edition. CRC Press/Balkema, Leiden, the Netherlands
go back to reference Tan B, Duan A (1995) Study on prediction for rainstorm debris flow along Mountain District rail ways. Natural Disasters 4(2):43–52. (In Chinese) Tan B, Duan A (1995) Study on prediction for rainstorm debris flow along Mountain District rail ways. Natural Disasters 4(2):43–52. (In Chinese)
go back to reference Tian S, Wang C, Zhang Z (2017) A hybrid method of debris flow velocity estimation based on empirical equation. Int J Heat Technol 35(1):147–152CrossRef Tian S, Wang C, Zhang Z (2017) A hybrid method of debris flow velocity estimation based on empirical equation. Int J Heat Technol 35(1):147–152CrossRef
go back to reference Vapnik V (1992) Principles of risk minimization for learning theory. In Lippman, D. S., Moody, J. E., and Touretzky, D. S., editors. Advances in Neural Information Processing System (NIPS). Morgan Kaufman, San Mateo, CA 4:831–838 Vapnik V (1992) Principles of risk minimization for learning theory. In Lippman, D. S., Moody, J. E., and Touretzky, D. S., editors. Advances in Neural Information Processing System (NIPS). Morgan Kaufman, San Mateo, CA 4:831–838
go back to reference Vapnik VN (1995) The nature of statistical learning Theory. Springer-Verlag, New York Vapnik VN (1995) The nature of statistical learning Theory. Springer-Verlag, New York
go back to reference Vapnik V (1998) Statistical learning Theory. Vol. 1. John Wiley & Sons, New York Vapnik V (1998) Statistical learning Theory. Vol. 1.  John Wiley & Sons, New York
go back to reference Vapnik V (2013) The nature of statistical learning theory, Springer science & business media, Berlin Vapnik V (2013) The nature of statistical learning theory, Springer science & business media, Berlin
go back to reference Xiong K, Adhikari BR, Stamatopoulos CA, Zhan Y, Wu S, Dong Z, di B (2020) Comparison of different machine learning methods for debris flow susceptibility mapping: a case study in the Sichuan Province, China. Remote Sens 12(2):295CrossRef Xiong K, Adhikari BR, Stamatopoulos CA, Zhan Y, Wu S, Dong Z, di B (2020) Comparison of different machine learning methods for debris flow susceptibility mapping: a case study in the Sichuan Province, China. Remote Sens 12(2):295CrossRef
go back to reference Yu P-S, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716CrossRef Yu P-S, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716CrossRef
go back to reference Zhang Y, Ge T, Tian W, Liou YA (2019) Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sens 11(23):2801CrossRef Zhang Y, Ge T, Tian W, Liou YA (2019) Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sens 11(23):2801CrossRef
go back to reference Zheng L, Zhou H, Wang C, Cen K (2008) Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers. Energy Fuel 22(2):1034–1040CrossRef Zheng L, Zhou H, Wang C, Cen K (2008) Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers. Energy Fuel 22(2):1034–1040CrossRef
go back to reference Zhuang J, Cui P, Wang G, Chen X, Iqbal J, Guo X (2015) Rainfall thresholds for the occurrence of debris flows in the Jiangjia gully, Yunnan Province, China. Eng Geol 195:335–346CrossRef Zhuang J, Cui P, Wang G, Chen X, Iqbal J, Guo X (2015) Rainfall thresholds for the occurrence of debris flows in the Jiangjia gully, Yunnan Province, China. Eng Geol 195:335–346CrossRef
Metadata
Title
A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods
Authors
Mahsa Sheikh Kazemi
Mohammad Ebrarim Banihabib
Jaber Soltani
Publication date
21-01-2021
Publisher
Springer Berlin Heidelberg
Published in
Earth Science Informatics / Issue 1/2021
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI
https://doi.org/10.1007/s12145-021-00570-0

Other articles of this Issue 1/2021

Earth Science Informatics 1/2021 Go to the issue

Premium Partner