Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models

This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.

[1]  C. M. DeChant,et al.  Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation , 2011 .

[2]  Meysam Motahari,et al.  Development of a PSO-ANN Model for Rainfall-Runoff Response in Basins, Case Study: Karaj Basin , 2017 .

[3]  Jan Adamowski,et al.  Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model , 2018, Stochastic Environmental Research and Risk Assessment.

[4]  R. Deo,et al.  Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm , 2017 .

[5]  Hikmet Kerem Cigizoglu,et al.  Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data , 2007, Environ. Model. Softw..

[6]  Fanping Zhang,et al.  A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting , 2014, J. Appl. Math..

[7]  Rahim Barzegar,et al.  Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. , 2017, The Science of the total environment.

[8]  R. Barzegar,et al.  Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran , 2016, Modeling Earth Systems and Environment.

[9]  Fionn Murtagh,et al.  Wavelet-based combined signal filtering and prediction , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Sajjad Ahmad,et al.  Suspended sediment load prediction of river systems: An artificial neural network approach , 2011 .

[11]  J. Poesen,et al.  Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate , 2001 .

[12]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[13]  Fionn Murtagh,et al.  Wavelet-based feature extraction and decomposition strategies for financial forecasting , 1998 .

[14]  D. Labat,et al.  Rainfall-runoff relations for karstic springs. Part II: Continuous wavelet and discrete orthogonal multiresolution analyses. , 2000 .

[15]  A. Grossmann,et al.  DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .

[16]  Mohammad Reza Kavianpour,et al.  Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms , 2017, International Journal of Environmental Science and Technology.

[17]  Chih Ted Yang,et al.  Sediment transport : theory and practice / Chih Ted Yang , 1995 .

[18]  Ji Chen,et al.  A service-oriented architecture for ensemble flood forecast from numerical weather prediction , 2015 .

[19]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

[20]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[21]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[22]  Christopher Y Choi,et al.  Assessment of physically-based and data-driven models to predict microbial water quality in open channels. , 2010, Journal of environmental sciences.

[23]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

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

[25]  X. Y. Chen,et al.  A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model , 2015, Eng. Appl. Artif. Intell..

[26]  M. Alizadeh,et al.  A new approach for simulating and forecasting the rainfall-runoff process within the next two months , 2017 .

[27]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[28]  Wei Shan,et al.  Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China. , 2017, Journal of environmental sciences.

[29]  R. Deo,et al.  Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model , 2017, Stochastic Environmental Research and Risk Assessment.

[30]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .

[31]  Mohamad Javad Alizadeh,et al.  Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. , 2015, Marine pollution bulletin.

[32]  Ozgur Kisi,et al.  Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors , 2017, Environmental Science and Pollution Research.

[33]  D. Seo,et al.  Ensemble prediction and data assimilation for operational hydrology , 2014 .

[34]  Hamed Kiani,et al.  A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir , 2015 .

[35]  Nathalie E. M. Asselman,et al.  Suspended sediment dynamics in a large drainage basin: the River Rhine , 1999 .

[36]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[37]  Taher Rajaee,et al.  Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. , 2011, The Science of the total environment.

[38]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[39]  Turgay PartalT. Partal.  River flow forecasting using different artificial neural network algorithms and wavelet transform , 2009 .

[40]  R. Khosa,et al.  Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach , 2012 .

[41]  Jan Adamowski,et al.  Comparison of machine learning models for predicting fluoride contamination in groundwater , 2017, Stochastic Environmental Research and Risk Assessment.

[42]  L. Rijn.  Principles of sediment transport in rivers, estuaries and coastal seas , 1993 .

[43]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[44]  Xixi Lu,et al.  Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China , 2007 .