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Water Resources Management

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01-05-2014

Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression

Author: Manish Kumar Goyal

Published in: Water Resources Management | Issue 7/2014

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Abstract

The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based M5 Model Tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RMSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of M5 Model Tree and wavelet regression versus ANN clearly shows that M5 Model Tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, M5 model tree offers explicit expressions for use by design engineers.

previous article Alternatives to Reduce Pumping Effects in Glacial Stratified Drift Aquifers During Periods of Low Stream Flow

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Agarwal A, RAI RK, Upadhyay A (2009), Forecasting of runoff and sediment yield using artificial neural networks. Journal Water Resource and Protection 1:368–375

Ajmera TK, Goyal MK (2012) Development of stage discharge rating curve using model tree and neural networks: an application to Peachtree Creek in Atlanta. Expert Syst Appl 39(5):5702–5710

Antonios A, Constantine EV (2003) Wavelet exploratory analysis of the FTSE ALL SHARE Index. In proceedings of the 2nd WSEAS international conference on non-linear analysis. Non-linear systems and Chaos, Athens

Ayteek A, Kisi Ö (2008) A genetic programming approach to suspended sediment modeling. J Hydrol 351:288–298CrossRef

Bhattacharya B, Solomatine DP (2005) Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing 63:381–396CrossRef

Burney SMA, Jilani TA, Ardil C (2005) Levenberg-Marquardt algorithm for karachi stock exchange share rates forecasting. World Acad Sci Eng Technol 3:171–176

Caroni E, Singh VP, Ubertini L (1984) Rainfall-runoff-sediment yield relation by stochastic modelling. Hydrol Sci J 29(2):203–218CrossRef

Chou C-M (2011) Wavelet-based multi-scale entropy analysis of complex rainfall time series. Entropy 13:241–253. doi:10.3390/e13010241 CrossRef

Cigizoglu HK (2002) Suspended sediment estimation for rivers using Artificial Neural Networks and Sediment Rating Curves. Turk J Eng Environ Sci 26:27–36

Cigizoglu HK, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317:221–228CrossRef

Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367:52–61CrossRef

Coulibaly P, Burn HD (2004) Wavelet analysis of variability in annual Canadian streamflows. Water Resour Res 40, W03105CrossRef

Fernando DAK, Shamseldin AY (2009) “Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models.” J Hydrol Eng 14(3):286–292

Goyal MK, Ojha CSP (2014) Evaluation of rule and decision tree induction algorithms for generating climate change scenarios for temperature and pan evaporation on a Lake Basin. ASCE J Hydrol Eng 10.1061/(ASCE)HE. 1943–5584.0000615

Grossman A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:732–736

Haghizadeh A, Shui LT, Goudarzi E (2010) Estimation of yield sediment using artificial neural network at basin scale. Aust J Basic Appl Sci 4(7):1668–1675

Heng S, Suetsugi T (2013) Using artificial neural network to estimate sediment load in Ungauged catchments of the Tonle Sap River Basin, Cambodia. J Water Resour Prot 5(2):111–123CrossRef

Jain A, Srinivasulu S (2004). “Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques.” Water Resour Res 40(4):W04302, 1–12

Kisi O (2004) Multi-Layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49(6):1025–1040CrossRef

Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50(4):683–696

Kisi O (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process 23:3583–3597CrossRef

Kisi O (2011) Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour Manag 25(2):579–600CrossRef

Kisi Ö, Ozkan C, Akay B (2012) Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428–429:94–103CrossRef

Goyal MK (2014) “Monthly rainfall prediction using wavelet regression and neural network: An analysis of 1901-2002 data, Assam, India”, Theoretical and Applied Climatology, Springer. doi:10.1007/s00704-013-1029-3

Goyal MK, Ojha CSP (2011) Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour Manag 25:2177–2195CrossRef

Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (2014) Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Systems With Applications. doi:10.1016/j.eswa.2014.02.047

Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs. Part II. Continuous wavelet and discrete orthogonal multiresolution analyses. J Hydrol 238(3–4):149–178CrossRef

Labat D, Ronchail J, Guyot JL (2005) Recent advances in wavelet analyses. Part 2— Amazon, Parana, Orinoco and Congo discharges time scale variability. J Hydrol 314(1–4):289–311CrossRef

Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage-discharge-sediment concentration relationships using Fuzzy logic. Hydrol Sci J 52(4):793–807CrossRef

Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124CrossRef

Mallat S, (1989) A theory of multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal, Machine Intell

Minns AW, Hall MJ (1996) Artificial neural networks as rainfall runoff models. Hydrol Sci J 41(3):399–418CrossRef

Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process. doi:10.1002/hyp.7136

Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural networks. J Hydrol Eng 128(6):588–595CrossRef

Partal T, Kucuk M (2006) Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region. Phys Chem Earth 31:1189–1200 (Turkey)CrossRef

Quinlan JR (1992) Learning with continuous classes. In: Proc. of the Fifth Australian Joint Conference on Artificial Intelligence. World Scientific Singapore 343–348

Quiroz R, Yarlequé C, Posadas A, Mares V, Immerzee Walter W (2011) Improving daily rainfall estimation from NDVI using a wavelet transform. Environ Model Softw 26:201–209CrossRef

Raghuwanshi NS, Singh R, Reddy LS (2006) Runoff and sediment yield modelling using Artificial Neural Networks: Upper Siwane River, India. J Hydrol Eng ASCE 11(1):71–79CrossRef

Reddy MS (1999) Theme paper on “Water: vision 2050. Indian Water Resources Soc. Roorkee pp. 51–53

Rosso OA, Figliola A, Creso J, Serrano E (2004) Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings. Med Biol Eng Comput 42(4):516–523

Sajikumar S, Thandaveswara BS (1999) A non-linear rainfall–runoff model using an artificial neural network. J Hydrol 216:32–55

Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modelling of hydrologic time series. Water Resources Publications, Littleton

Santos CAG, Freire FKMM (2012) Analysis of precipitation time series of urban centers of Northeastern Brazil using wavelet transform. World Acad Sci Eng Technol 67:845–850

Senthil Kumar AR, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291CrossRef

Senthil kumar AR, Ojha CSP, Goyal MK, Singh RD, Swamee PK (2012) Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic and decision tree algorithms. ASCE’s J Hydrol Eng 17(3):394–404CrossRef

Senthil kumar AR, Goyal MK, Ojha CSP, Singh RD, Swamee PK, Nema RK (2013) Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules. Water Resour Manag. Springer Netherlands 27(3):911–925

Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction interval for the model output. Neural Netw 19(2):225–235CrossRef

Singh A, Imtiyaz M, Isaac RK, Denis DM (2013) Comparison of artificial neural network models for sediment yield prediction at single Gauging Station of Watershed in Eastern India. J Hydrol Eng 18(1):115–120CrossRef

Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16:1325–1330CrossRef

Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26:1249–1256CrossRef

Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3(1):26–32CrossRef

Tokar AS, Markus M (2000) “Precipitation-runoff model-ling using artificial neural networks and conceptual mod-els,” J Hydrol Eng 5(2):156−161

Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorological Soc 79(1):61–78CrossRef

Wang Y, Witten IH (1997) Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning

Wang YM, Kerh T, Traore S (2009) Neural networks approaches for modelling river suspended sediment concentration due to tropical storms. Global Nest J 11(4):457–466

Wang D, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1(1):67–71

Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco

Yenigün K, Mahmut B, Reşit G, Mehmet M (2010) A comparative study on prediction of sediment yield in the Euphrates Basin. Int J Phys Sci 5(5):518–534

Yongli MA, Zongjue Q, Guochu S, Hu Y (2008) Study on preliminary performance of algorithms for network traffic identification. In: International conference on computer science and software engineering. pp 629–633. doi:10.1109/CSSE.2008.1277

Zhang B, Govindaraju RS (2003) Geomorphology-based artificial neural networks (GANNs) for estimation of direction runoff over watersheds. J Hydrol 273:18–34CrossRef

Zhou HC, Peng Y, Liang G-H (2008) The research of monthly discharge predictor–corrector model based on wavelet decomposition. Water Resour Manag 22(2):217–227CrossRef

Title: Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression
Author: Manish Kumar Goyal
Publication date: 01-05-2014
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 7/2014
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-014-0590-6

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