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Water Resources Management

Erschienen in:

28.09.2016

Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting

verfasst von: Vahid Moosavi, Ali Talebi, Mohammad Reza Hadian

Erschienen in: Water Resources Management | Ausgabe 1/2017

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Abstract

Effective runoff prediction is one of the main aspects of successful water resources management. One of the most important problems in the modeling of such hydrological processes is the non-stationarities in the data. Several data mining models have deficiencies in handling non-stationary data particularly when signal variations are highly non-stationary. The main objective of this study was to develop a robust model to estimate daily runoff quantities. Firstly, Group Method of Data Handling (GMDH) was used in its single form to model the rainfall-runoff process. Then, the discrete wavelet and wavelet packet transforms were used to decompose the original data to their corresponding components. Thereafter, hybrid models were developed using the wavelet-based analyzed data. Three different rivers were selected to perform these modeling approaches. Results showed that GMDH model had a moderate performance (R² ≈ 0.84, RMSE ≈ 2.17 m³/s and Max. Error ≈ 24 m³/s for Ghale Chay River). Wavelet transform enhanced the ability of the GMDH model to some extent (R² ≈ 0.90, RMSE ≈ 1.7 m³/s, and Max. Error ≈ 16 m³/s for Ghale-Chay River). However, it was shown that wavelet packet transform significantly enhanced the ability of the single GMDH model with R² of 0.94, RMSE of 1.37m³/s, and Maximum Error of about 9.8m³/s for Ghale-Chay River. The results were similar in the other two rivers. It was confirmed that the wavelet packet transform can be effectively used to deal with the non- stationarities in the data and can efficiently enhance the performance of GMDH model.

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Abrahart RJ, See LM, Dawson CW, Shamseldin AY, Wilby RL (2010) Nearly two decades of neural network hydrologic modeling. In: Sivakumar B, Berndtsson R (eds) Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific Publishing, Hackensack, pp. 267–346CrossRef

Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36:480–513CrossRef

Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef

Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390(1–2):85–91CrossRef

Amanifard N, Nariman-Zadeh N, Farahani MH, Khalkhali A (2008) Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks. J. Energy Convers Manag 49(10):2588–2594CrossRef

Behzad M, Asghari K, Eazi M, Palhang M (2009) Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst Appl 36:7624–7629CrossRef

Camporese M, Paniconi C, Putti M, Orlandini S (2010) Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resour Res 46:W02512. doi:10.1029/2008wr007536 CrossRef

Cannas B, Fanni A, Sias G, Tronei S, Zedda MK (2006) River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European geosciences union, pp. 234–243

Chau KW, Wu CL (2010) A Hybrid Model Coupled with Singular Spectrum Analysis for Daily Rainfall Prediction. J Hydroinf 12(4):458–473CrossRef

Chen XY, Chau KW, Busari AO (2015) A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Eng Appl Artif Intell 46(A):258–268CrossRef

Christopoulou EB, Skodras AN, Georgakilas AA (2002) The “Trous” wavelet transform versus classical methods for the improvement of solar images. In: proc. 14th Int. conf. On. Digital Signal Processing 2:885–888

Cohen A, Kovacevic J (1996) Wavelets: the mathematical background. Proc IEEE 84:514–522CrossRef

Dorna M, Braga ALS, Llanos CH, Coelho LS (2012) A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides. Expert Syst Appl 39:12268–12279CrossRef

Farlow SJ (1984) Self -organizing Method in Modeling. GMDH Type Algorithm; Marcel Dekker Inc

Gholami V, Chau KW, Fadaee F, Torkaman J, Ghaffari A (2015) Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. J Hydrol 529(3):1060–1069CrossRef

Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern Syst 1(4):364–378CrossRef

Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104:144–155CrossRef

Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manag 29:1283–1293CrossRef

Kisi O (2009) Neural networks and wavelet conjunction model for intermittent stream flow forecasting. J Hydrol Eng 14:773–782CrossRef

Kisi O, Ozkan C, Akay B (2012) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol:428–429

Liu Y, Ngan KN (2006) Embedded wavelet packet object-based image coding based on context classification and quadtree ordering. Sihnal Process-Image 21:143–155CrossRef

Liu Z, Todini E (2002) Towards a comprehensive physically-based rainfall-runoff model. Hydrol Earth Syst Sc 6(5):859–881CrossRef

Mehra RK (1977) Group method of data handling (GMDH): Review and experience. IEEE Conf Dec Cont 16:29–34

Moosavi V, Niazi Y (2015) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides. doi:10.1007/s10346-014-0547-0

Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27:1301–1321CrossRef

Moosavi V, Vafakhah M, Shirmohammadi B, Ranjbar B (2014) Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arab J Sci Eng 39:1785–1796CrossRef

Moosavi V, Talebi A, Mokhtari MH, Fallah Shamsi SR, Niazi Y (2015) A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sens Environ 169:243–254CrossRef

Najafzadeh M (2015) Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean Eng 99:85–94CrossRef

Najafzadeh M, Lim SY (2015) Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Sci Inf 8(1):187–196CrossRef

Najafzadeh M, Barani GA, Hessami Kermani MR (2013) GMDH based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106CrossRef

Nourani V, Kisi Z, Mehdi K (2011) Two hybrid artificial intelligence approaches for modeling rainfall-runoff process. J Hydrol 402:41–59CrossRef

Ogden RT (1997) Essential wavelets for statistical applications and data analysis Birkhauser, Boston

Padilha CEA, Padilha CAA, Souza DFS, Oliveira JA, Macedo GR, Santos ES (2015) Prediction of rhamnolipid breakthrough curves on activated carbon and Amberlite XAD-2 using artificial neural network and group method data handling models. J Mol Liq 206:293–299CrossRef

Partal T (2009) River flow forecasting using different artificial neural network algorithms and wavelet transform. Can J Civ Eng 36:26–38CrossRef

Rajaee T (2011) Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Sci Total Environ 409:2917–2928CrossRef

Sang Y, Wang Z, Liu C (2015) Wavelet neural modeling for hydrologic time series forecasting with uncertainty evaluation. Water Resour Manag 29:1789–1801CrossRef

Sehgal V, Sahay RR, Chatterjee C (2014) Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resour Manag 28:1733–1749CrossRef

Shafaei M, Kisi O (2016) Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour Manag 30:79–97CrossRef

Shahabi S, Khanjani MJ, Kermani MH (2015) Hybrid wavelet-GMDH model to forecast significant wave height. doi:10.2166/ws.2015.151

Shirmohammadi B, Moradi HR, Moosavi V, Taie Semiromi M, Zeinali A (2013) Forecasting of meteorological drought using wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of East Azerbaijan province, Iran. Nat Hazards 69:389–402CrossRef

Sreekanth P, Geethanjali DN, Sreedevi PD, Ahmed S, Kumar NR, Jayanthi PDK (2009) Forecasting groundwater level using artificial neural networks. Curr Sci 96(7):933–939

Taormina R, Chau KW (2015a) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines. J Hydrol 529(3):1617–1632CrossRef

Taormina R, Chau KW (2015b) Neural Network River forecasting with multi-objective fully informed particle swarm optimization. J Hydroinf 17:99–112CrossRef

Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25:1670–1676CrossRef

Vissikirsky VA, Stepashko VS (2005) Growth dynamics of trees irrigated with wastewater: GMDH modeling, assessment, and control issues. Instrum Sci Technol 2:229–249CrossRef

Volterra V (1959) Theory of Functionals and of integrals and Integro-differential equations. Madrid (Spanish), translated version reprinted. Dover Publications, New York

Wang CM, Huang YF (2009) Evolutionary-based feature selection approaches with new criteria for data mining: a case study of credit approval data. Expert Syst Appl 36(3):5900–5908CrossRef

Wang WC, Chau KW, Xu D, Chen XY (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675CrossRef

Wu JD, Liu CH (2009) An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl 36:4278–4286CrossRef

Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93CrossRef

Titel: Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting
verfasst von: Vahid Moosavi
Ali Talebi
Mohammad Reza Hadian
Publikationsdatum: 28.09.2016
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 1/2017
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-016-1507-3

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