Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

Summary Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

[1]  V. Jothiprakash,et al.  Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data , 2012 .

[2]  Dulakshi S. K. Karunasinghe,et al.  Chaotic time series prediction with a global model: Artificial neural network , 2006 .

[3]  Indrajeet Chaubey,et al.  A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models , 2007 .

[4]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[5]  Evangelos Tziritis,et al.  Geochemical status and interactions between soil and groundwater systems in the area of Akrefnio, Central Greece. Risk assessment, under the scope of mankind and natural environment , 2011 .

[6]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

[7]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .

[8]  O. Kisi.  Wavelet regression model for short-term streamflow forecasting. , 2010 .

[9]  Chuntian Cheng,et al.  Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models , 2005, ICNC.

[10]  Umut Okkan,et al.  Using wavelet transform to improve generalization capability of feed forward neural networks in monthly runoff prediction , 2012 .

[11]  Sevket Durucan,et al.  River flow prediction using artificial neural networks: generalisation beyond the calibration range. , 2000 .

[12]  Abdullah Almasri.  Some aspects of wavelet analysis in time series , 2000 .

[13]  F. Anctil,et al.  An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition , 2004 .

[14]  Simon Li,et al.  Uncertainties in real‐time flood forecasting with neural networks , 2007 .

[15]  Sungwon Kim,et al.  Assessment of Pan Evaporation Modeling Using Bootstrap Resampling and Soft Computing Methods , 2015, J. Comput. Civ. Eng..

[16]  O. Kisi,et al.  Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones , 2012, Water Resources Management.

[17]  Guy P. Nason,et al.  Wavelet Methods in Statistics with R , 2008 .

[18]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[19]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[20]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.

[21]  Wang Wen-sheng,et al.  Cuntan Station of the Yangtze River Annual Runoff Forecasting with Set Pair Analysis Method , 2006 .

[22]  Ozgur Kisi,et al.  A Wavelet-Genetic Programming Model for Predicting Short-Term and Long-Term Air Temperatures , 2011 .

[23]  Stefan Fritsch,et al.  neuralnet: Training of Neural Networks , 2010, R J..

[24]  Beom-Soo Kim,et al.  Fuzzy Logic Model of Langmuir Probe Discharge Data , 2002, Comput. Chem..

[25]  Rahib Hidayat Abiyev,et al.  Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction , 2011, Neural Computing and Applications.

[26]  E. Clothiaux,et al.  Neural Networks and Their Applications , 1994 .

[27]  V. Singh,et al.  Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns , 2013, Water Resources Management.

[28]  Ozgur Kisi,et al.  River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model , 2011 .

[29]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  Faridah Othman,et al.  Reservoir inflow forecasting using artificial neural network , 2011 .

[31]  Taher Rajaee,et al.  Wavelet and neuro-fuzzy conjunction approach for suspended sediment prediction. , 2010 .

[32]  Wensheng Wang,et al.  Wavelet Network Model and Its Application to the Prediction of Hydrology , 2003 .

[33]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[34]  Ana G. Elias,et al.  Discrete wavelet analysis to assess long-term trends in geomagnetic activity , 2006 .

[35]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[36]  Özgür Kişi,et al.  Evapotranspiration modeling using a wavelet regression model , 2010, Irrigation Science.

[37]  T. S. Lee,et al.  Applicability of Adaptive Neuro-Fuzzy Inference Systems in Daily Reservoir Inflow Forecasting , 2011 .

[38]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[39]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[40]  Young-Oh Kim,et al.  Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction , 2005 .

[41]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[42]  Chandranath Chatterjee,et al.  Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) , 2010 .

[43]  Ju-liang Jin,et al.  Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model , 2009 .

[44]  Chuntian Cheng,et al.  Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models , 2005, ISNN.

[45]  Xavier Otazu,et al.  Comparison between Mallat's and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images , 2005 .

[46]  Teresa B. Culver,et al.  Bootstrapped artificial neural networks for synthetic flow generation with a small data sample , 2006 .

[47]  José Manuel Benítez,et al.  Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS , 2012 .

[48]  Jan Adamowski,et al.  Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data / Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych , 2012 .

[49]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[50]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[51]  Alice E. Smith,et al.  Bias and variance of validation methods for function approximation neural networks under conditions of sparse data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[52]  E. Zio,et al.  A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes , 2006, IEEE Transactions on Nuclear Science.

[53]  Chandranath Chatterjee,et al.  Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach , 2010 .

[54]  Chien-Ming Chou,et al.  Application of wavelet‐based multi‐model Kalman filters to real‐time flood forecasting , 2004 .

[55]  Renée J. Miller,et al.  Similarity search over time-series data using wavelets , 2002, Proceedings 18th International Conference on Data Engineering.

[56]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[57]  Ozgur Kisi,et al.  A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .

[58]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[59]  Y. Travi,et al.  Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia) , 2001 .

[60]  Thierry Blu,et al.  Generalized Daubechies Wavelet Families , 2007, IEEE Transactions on Signal Processing.

[61]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[62]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[63]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[65]  K. Minu,et al.  Wavelet Neural Networks for Nonlinear Time Series Analysis , 2010 .

[66]  Anteneh Meshesha Belayneh,et al.  Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression , 2012, Appl. Comput. Intell. Soft Comput..

[67]  K. N. Tiwari,et al.  Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment , 2009 .

[68]  Deg-Hyo Bae,et al.  Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique , 2007 .

[69]  Celso Augusto Guimarães Santos,et al.  Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir , 2014 .

[70]  Robert Tibshirani,et al.  A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.

[71]  Mohammad H. Aminfar,et al.  A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation , 2009, Eng. Appl. Artif. Intell..

[72]  Marley M. B. R. Vellasco,et al.  Neural Networks for Inflow Forecasting Using Precipitation Information , 2007, IEA/AIE.

[73]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[74]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[75]  Saman Razavi,et al.  Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach , 2009 .

[76]  Yan Li,et al.  Comparison of Several Flood Forecasting Models in Yangtze River , 2005 .

[77]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[78]  Nasreen Islam Khan,et al.  Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach , 2012 .

[79]  Alessandra Fanni,et al.  River flow forecasting using neural networks and wavelet analysis , 2005 .

[80]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[81]  Ozgur Kisi,et al.  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process , 2011 .

[82]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[83]  Umut Okkan,et al.  The combined use of wavelet transform and black box models in reservoir inflow modeling , 2013 .

[84]  O. Kisi.  River flow forecasting and estimation using different artificial neural network techniques , 2008 .

[85]  Linda See,et al.  Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning , 2006 .

[86]  Fatih Evrendilek.  Assessing neural networks with wavelet denoising and regression models in predicting diel dynamics of eddy covariance-measured latent and sensible heat fluxes and evapotranspiration , 2012, Neural Computing and Applications.

[87]  K. Chau,et al.  Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques , 2009 .