A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States

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• 作者：Ehsan Olyaie (1)
  Hossein Banejad (2)
  Kwok-Wing Chau (3)
  Assefa M. Melesse (4)
  
  1. Young Researchers and Elites Club ; Hamedan Branch ; Islamic Azad University ; Hamedan ; Iran
  2. Department of Water Engineering ; College of Agriculture ; Ferdowsi University of Mashhad ; Mashhad ; Iran
  3. Department of Civil and Environmental Engineering ; Hong Kong Polytechnic University ; Hong Kong ; China
  4. Department of Earth and Environment ; Florida International University ; Miami ; FL ; USA
  
• 关键词：ANN ; ANFIS ; WANN ; SRC ; Suspended sediment load ; Soft computing ; Estimating
• 刊名：Environmental Monitoring and Assessment
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：187
• 期：4
• 全文大小：2,815 KB
• 参考文献：1. Abrahart, RJ, See, L (2000) Comparing neural network (NN) and Auto Regressive Moving Average (ARMA) techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological Processes 14: pp. 2157-2172 CrossRef
  2. Adamowski, J (2010) Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology 390: pp. 85-91 CrossRef
  3. Adamowski, J, Chan, HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology 407: pp. 28-40 CrossRef
  4. Altun, H, Bilgil, A, Fidan, BC (2007) Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications 32: pp. 599-605 CrossRef
  5. Ardiclioglu, M, Kisi, O, Haktanin, T (2007) Suspended sediment prediction by using two different feed-forward backpropogation algorithms. Canadian Journal of Civil Engineering 34: pp. 1-6 CrossRef
  6. Arthur, JH (2003) An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrological Processes 17: pp. 3387-3409 CrossRef
  Artificial neural networks in hydrology, I: preliminary concepts. Journal of Hydrological Engineering 5: pp. 115-123 CrossRef
  Artificial neural networks in hydrology, II: hydrologic application. Journal of Hydrological Engineering 5: pp. 124-137 CrossRef
  7. Asselman, NEM (2000) Fitting and interpretation of sediment rating curves. Journal of Hydrology 234: pp. 228-248 CrossRef
  8. Bae, DH, Jeong, DM, Kim, G (2007) Monthly dam inflow forecasts using weather forecast information and neuro-fuzzy technique. Hydrological Sciences Journal 52: pp. 99-113 CrossRef
  9. Bayram, A, Kankal, M, Onsoy, H (2012) Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks. Environmental Monitoring and Assessment 184: pp. 4355-4365 CrossRef
  10. Cannas, B, Fanni, A, Sias, G, Tronei, S, Zedda, MK (2005) River flow forecasting using neural networks and wavelet analysis. EGU, European Geosciences Union, Vienna
  11. Chang, FJ (2006) Chang YT.Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources 29: pp. 1-10 CrossRef
  12. Chang, LC, Chang, FJ (2001) Intelligent control for modelling or real-time reservoir operation. Hydrological Processes 15: pp. 1621-1634 CrossRef
  13. Chang, YT, Chang, LC, Chang, FJ (2005) Intelligent control for modeling of real time reservoir operation, Part II: artificial neural network with operating rule curves. Hydrological Processes 19: pp. 1431-1444 CrossRef
  14. Cigizoglu, HK (2003) Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrological Sciences Journal 48: pp. 349-361 CrossRef
  15. Cigizoglu, HK, Kisi, O (2006) Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology 317: pp. 221-238 CrossRef
  16. Cobaner, M, Unal, B, Kisi, O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro- meteorological data. Journal of Hydrology 367: pp. 52-61 CrossRef
  17. Cohen, A, Kovacevic, J (1996) Wavelets: the mathematical background. Proceedings Of IEEE 84: pp. 514-522 CrossRef
  18. Dastorani, MT, Moghadamnia, A, Piri, J, Rico-Ramirez, M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment 166: pp. 421-434 CrossRef
  19. Elci, S, Aydin, R, Work, PA (2009) Estimation of suspended sediment concentration in rivers using acoustic methods. Environmental Monitoring and Assessment 159: pp. 255-265 CrossRef
  20. Ferguson, RI (1986) River loads underestimated by rating curves. Water Resources Research 22: pp. 74-76 CrossRef
  21. Firat, M, Gungor, M (2007) River flow estimation using adaptive neuro-fuzzy inference system. Mathematics and Computers in Simulation 75: pp. 87-96 CrossRef
  22. Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. / Information and Management, 24, 159鈥?67.
  23. Gao, S, Wang, YP (2008) Changes in material fluxes from the Changjiang River and their implications on the adjoining continental shelf ecosystem. Continental Shelf Research 28: pp. 1490-1500 CrossRef
  24. Grossman, A, Morlet, J (1984) Decompositions of hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis 15: pp. 723-736 CrossRef
  25. Gupta, K. K., & Gupta, R. (2007). Despeckle and geographical feature extraction in SAR images by wavelet transform. / Journal of Photogrammetry and Remote Sensing, 62(6), 473鈥?84.
  26. Heddam, S (2014) Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environmental Monitoring and Assessment 186: pp. 597-619 CrossRef
  27. Jain, SK (2001) Development of integrated sediment rating curves using Anns. Journal of Hydraulic Engineering 127: pp. 30-37 CrossRef
  28. Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. / IIEEE Transactions on System, Man, and Cybernetics, 23(3), 665鈥?85.
  29. Jang, JSR, Sun, CT, Mizutani, E (1997) Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River
  30. Keskin, ME, Terzi, O, Taylan, D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrological Sciences Journal 49: pp. 1001-1010 CrossRef
  31. Kisi, O (2004) Multi-layer perceptrons with Levenberg鈥揗arquardt optimization algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal 49: pp. 1025-1040 CrossRef
  32. Kisi, O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 50: pp. 683-696
  33. Kisi, O (2005) Daily river flow forecasting using artificial neural networks and autoregressive models. The Turkish Journal of Engineering and Environmental Sciences 29: pp. 9-20
  34. Kisi, O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrological Engineering 14: pp. 773-782 CrossRef
  35. Kisi, O, Shiri, J (2012) River suspended sediment estimation by climatic variables implication:Comparative study among soft computing techniques. Computers & Geosciences 43: pp. 73-82 CrossRef
  36. Kisi, O, Haktanir, T, Ardiclioglu, M, Ozturk, O, Yalcin, E, Uludag, S (2008) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Advances in Engineering Software 40: pp. 438-444 CrossRef
  37. Kisi, O, Hosseinzadeh Dailr, A, Cimen, M, Shiri, J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology 450鈥?51: pp. 48-58 CrossRef
  38. Kucuk, M, Ag谋ralioglu, N (2006) Wavelet regression techniques for streamflow predictions. Journal of Applied Statistics 33: pp. 943-960 CrossRef
  39. Labat, D, Ababou, R, Mangin, A (2000) Rainfall-runoff relation for karstic spring. Part 2: continuous wavelet and discrete orthogonal multi resolution analyses. Journal of Hydrology 238: pp. 149-178 CrossRef
  40. Melesse, AM, Ahmad, S, McClain, ME, Wang, X, Lim, YH (2011) Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management 98: pp. 855-866 CrossRef
  41. Nayak, PC, Sudheer, KP, Rangan, DM, Ramasastri, KS (2004) A neuro fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 291: pp. 52-66 CrossRef
  42. Nourani, V, Kisi, O, Komasi, M (2011) Two hybrid Artificial Intelligence approaches for modeling rainfall鈥搑unoff process. Journal of Hydrology 402: pp. 41-59 CrossRef
  43. Partal, T, Cigizoglu, HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. Journal of Hydrology 358: pp. 317-331 CrossRef
  44. Partal, T, Kisi, O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology 342: pp. 199-212 CrossRef
  45. Pulido-Calvo, I, Gutierrez-Estrada, JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosystems Engineering 102: pp. 202-218 CrossRef
  46. Rajaee, T (2011) Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Science of the Total Environment 409: pp. 2917-2928 CrossRef
  47. Rajaee, T, Mirbagheri, SA, Zounemat-Kermani, M, Nourani, V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Science of the Total Environment 407: pp. 4916-4927 CrossRef
  48. Rumelhart, DE, McClelland, JL (1986) The PDP research group. Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press, Cambridge
  49. Sayed, T, Tavakolie, A, Razavi, A (2003) Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. Water Resources Research 17: pp. 123-130
  50. Shiri, J, Makarynskyy, O, Kisi, O, Dierickx, W, Fakheri Fard, A (2011) Prediction of short term operational water levels using an adaptive neuro-fuzzy inference system. ASCE Journal of Waterway, Port, Coastal, and Ocean Engineering.
  51. Syvitski, JPM, Milliman, JD (2007) Geology, geography, and humans battle for dominance over the delivery of fluvial sediment to the coastal ocean. Journal of Geology 115: pp. 1-19 CrossRef
  52. Syvitski, JPM, Morehead, MD, Bahr, DB, Mulder, T (2000) Estimating fluvial sediment transport: the rating parameters. Water Resources Research 36: pp. 2747-2760 CrossRef
  53. Talei, A, Chua, LHC, Quek, C (2010) A novel application of a neuro-fuzzy computational technique in event-based rainfall鈥搑unoff modeling. Expert Systems with Applications 37: pp. 7456-7468 CrossRef
  54. Tayfur, G, Guldal, V (2006) Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrology 37: pp. 69-79
  55. Verstraeten, G, Poesen, J (2001) Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate. Geomorphology 40: pp. 123-144 CrossRef
  56. Wang, W, Ding, J (2003) Wavelet network model and its application to the prediction of the hydrology. Natural Science 1: pp. 67-71
  57. Wang, W, Jin, J, Li, Y (2009) Prediction of inflow at Three GorgesDamin Yangtze River with wavelet network model. Water Resources Management 23: pp. 2791-2803 CrossRef
  58. Wang, WC, Chau, KW, Cheng, CT, Qiu, LA (2009) Comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374: pp. 294-306 CrossRef
  59. Ward, P, Balen, RT, Verstraeten, G, Renssen, H, Vandenberghe, J (2009) The impact of land use and climate change on late Holocene and future suspended sediment yield of the Meuse catchment. Geomorphology 103: pp. 389-400 CrossRef
  60. Wu, C, Chau, K, Li, Y (2009) Methods to improve neural network performance in daily flows prediction. Journal of Hydrology 372: pp. 80-93 CrossRef
  61. Yarar, A, Onucy谋ld谋z, M, Copty, NK (2009) Modelling level change in lakes using neuro-fuzzy and artificial neural networks. Journal of Hydrology 365: pp. 329-334 CrossRef
  62. Yoon, H, Jun, SH, Hyun, Y, Bae, GO, Lee, KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396: pp. 128-138 CrossRef
  63. Yurdusev, MA, Firat, M, Turan, ME (2009) Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: an application to Izmir, Turkey. Journal of Hydrology 365: pp. 225-234 CrossRef
  64. Zhang, W, Wei, X, Zheng, J, Zhu, Y, Zhang, Y (2012) Estimating suspended sediment loads in the Pearl River Delta region using sediment rating curves. Continental Shelf Research 38: pp. 35-46 CrossRef
  65. Zhu, YM, Lu, XX, Zhou, Y (2007) Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology 84: pp. 111-125 CrossRef
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Monitoring, Environmental Analysis and Environmental Ecotoxicology
  Ecology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-2959

文摘

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.