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

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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.

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References

  • 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

  • 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, Forghani A (2017) An assessment framework for the mitigation effects of check dams on debris flow. Catena 152:277–284

    Article  Google Scholar 

  • Banihabib ME et al (2020) Bayesian networks model for identification of the effective variables in the forecasting of debris flows occurrence. Environ Earth Sci 79:1–15

    Article  Google Scholar 

  • 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

  • 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

  • Chen K-Y, Wang C-H (2007) Support vector regression with genetic algorithms in forecasting tourism demand. Tour Manag 28(1):215–226

    Article  Google Scholar 

  • 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):13–22

    Article  Google Scholar 

  • Crosta GB, Frattini P (2008) Rainfall-induced landslides and debris flows. Hydrol Processes: An Int J 22(4):473–477

    Article  Google Scholar 

  • Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genet 4(1):31

    Google Scholar 

  • 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):1075–1094

    Article  Google Scholar 

  • 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):789–798

    Article  Google Scholar 

  • 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 122:643–656

    Article  Google Scholar 

  • Hamel LH (2009) Knowledge discovery with support vector machines. John Wiley & Sons, Inc., Hoboken, NJ

  • 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):328

    Article  Google Scholar 

  • 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, 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

  • 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):155–172

    Article  Google Scholar 

  • Huang C-L, Dun J-F (2008) A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391

    Article  Google Scholar 

  • Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240

    Article  Google Scholar 

  • 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

  • Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theory Eng 3(1):1793–8201

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

  • 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):717–735

    Article  Google Scholar 

  • Lin J-W, Chen CW, Peng CY (2012) Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat Hazards 64(1):273–282

    Article  Google Scholar 

  • 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):2331–2343

    Article  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  • Ranaee V, Ebrahimzadeh A, Ghaderi R (2010) Application of the PSO–SVM model for recognition of control chart patterns. ISA Trans 49(4):577–586

    Article  Google Scholar 

  • 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

  • Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a GSA-SVM hybrid system. Math Comput Model 57(1–2):270–278

    Article  Google Scholar 

  • Senoo K et al (1985) Rainfall indexes for debris flow warning evacuating program. Shin-Sabo 38(2):16–21

    Google Scholar 

  • 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

  • Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  Google Scholar 

  • 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):1193–1200

    Article  Google Scholar 

  • Takahashi T, Das DK (2014) Debris flow: mechanics, prediction and countermeasures,2nd edition. CRC Press/Balkema, Leiden, the Netherlands

  • 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)

  • 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):147–152

    Article  Google Scholar 

  • 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 VN (1995) The nature of statistical learning Theory. Springer-Verlag, New York

  • Vapnik V (1998) Statistical learning Theory. Vol. 1.  John Wiley & Sons, New York

  • Vapnik V (2013) The nature of statistical learning theory, Springer science & business media, Berlin

  • 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):295

    Article  Google Scholar 

  • Yu P-S, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716

    Article  Google Scholar 

  • 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):2801

    Article  Google Scholar 

  • 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):1034–1040

    Article  Google Scholar 

  • 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 195:335–346

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to express their deep gratitude to Mr. Saeed Mozaffari for his kind contribution to developing the SVR model.

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Authors and Affiliations

  1. Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

    Mahsa Sheikh Kazemi, Mohammad Ebrarim Banihabib & Jaber Soltani

Authors
  1. Mahsa Sheikh Kazemi
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  2. Mohammad Ebrarim Banihabib
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  3. Jaber Soltani
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Correspondence to Mohammad Ebrarim Banihabib or Jaber Soltani.

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Communicated by: H. Babaie

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Kazemi, M.S., Banihabib, M.E. & Soltani, J. A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods. Earth Sci Inform 14, 365–376 (2021). https://doi.org/10.1007/s12145-021-00570-0

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