Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models.

[1]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[6]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[7]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[8]  Thomas Jackson,et al.  Neural Computing - An Introduction , 1990 .

[9]  R. K. Kachroo.  River flow forecasting. Part 1. A discussion of the principles , 1992 .

[10]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[11]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[12]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[13]  M. J. Hall,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[14]  Raghavan Srinivasan,et al.  PREDICTION OF TWO‐YEAR PEAK STREAM‐DISCHARGES USING NEURAL NETWORKS 1 , 1997 .

[15]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[18]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

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

[20]  Demetris F. Lekkas,et al.  Improved non-linear transfer function and neural network methods of flow routing for real-time forecasting , 2001 .

[21]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[22]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[23]  K. M. O'Connor.  River flow forecasting , 2005 .

[24]  Young-Seuk Park,et al.  Self-Organizing Map , 2008 .