Multiple Resolution River flow Time Series Modelling using Machine Learning Methods

We develop multiple resolution river flow time series model using machine learning methods at three locations in South Australia. In multiple resolution river flow models, we identify the best method from a set of widely used machine learning methods. We also identify optimized lag values of river flow and rainfall as input time series in predicting river flow multiple days ahead. The best models are ranked based on mean absolute error. Experimental results demonstrate that M5P method provides the lowest error over artificial neural network, linear regression and support vector regression in modelling river flow for multiple days ahead prediction for all three locations. Although M5P gives better accuracy over other methods for these locations as also found in the recent research in hydrological time series modelling, it may not be the best method for other geographical locations. Detailed evaluation of statistical and machine learning methods may be needed in predicting river flow for any location of interest.

[1]  Holger R. Maier,et al.  Neural network based modelling of environmental variables: A systematic approach , 2001 .

[2]  Andrew Metcalfe,et al.  Wavelet-Based Rainfall-Stream Flow Models for the Southeast Murray Darling Basin , 2014 .

[3]  Aman Mohammad Kalteh,et al.  Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform , 2013, Comput. Geosci..

[4]  Kurt Hornik,et al.  Open-source machine learning: R meets Weka , 2009, Comput. Stat..

[5]  Durdu Ömer Faruk.  A hybrid neural network and ARIMA model for water quality time series prediction , 2010, Eng. Appl. Artif. Intell..

[6]  Y. Wang,et al.  Analysis and modeling of multivariate chaotic time series based on neural network , 2009, Expert Syst. Appl..

[7]  P Sirisha,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR WATER QUALITY PREDICTION , 2012 .

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

[9]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[10]  Xiaochao Wang,et al.  A Four-Stage Hybrid Model for Hydrological Time Series Forecasting , 2014, PloS one.

[11]  T. Sathish,et al.  River Flow Forecasting using Recurrent Neural Networks , 2004 .

[12]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[13]  Cyril Voyant,et al.  Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation , 2012, ArXiv.

[14]  M. Pal,et al.  M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey , 2013, Water Resources.

[15]  R. K. Price,et al.  Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin , 2009 .

[16]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[17]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[18]  Dimitri P. Solomatine,et al.  River flow forecasting using artificial neural networks , 2001 .

[19]  Babak Omidvar,et al.  Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic , 2010, Expert Syst. Appl..

[20]  Ian H. Witten,et al.  Induction of model trees for predicting continuous classes , 1996 .