Flood Forecasting and Uncertainty Assessment Using Wavelet- and Bootstrap-Based Neural Networks

Accurate and reliable forecasting of flood is inevitable for flood control planning and rehabilitation. There are several models available for flood forecasting, but as far as accuracy, reliability, and data scarcity are concerned, soft computing techniques (e.g., artificial neural networks) have been found to achieve the target. A wavelet-, bootstrap-, and neural-network-based framework (BWANN) is presented here for flood forecasting. Performance comparison of the proposed BWANN model is presented with wavelet-based ANN (WANN), wavelet-based MLR (WMLR), bootstrapand wavelet-analysis-based multiple linear regression models (BWMLR), traditional ANN, and traditional multiple linear regression (MLR) models for flood forecasting. For development of WANN models, original time series data is decomposed using wavelet transformation, and wavelet sub-time series are considered to develop WANN model. A comparative analysis is carried out among different approaches of WANN model development using wavelet sub-time series.

[1]  Luis A. Bastidas,et al.  Multiobjective analysis of chaotic dynamic systems with sparse learning machines , 2006 .

[2]  N. Null Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[3]  Om Prakash Sahu,et al.  Secure Baseband Techniques for Generic Transceiver Architecture for Software-Defined Radio , 2017 .

[4]  Murat Küçük,et al.  Wavelet Regression Technique for Streamflow Prediction , 2006 .

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

[6]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[7]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[8]  Carla Maria Dal Sasso Freitas,et al.  Women in Brazilian CS Research Community: The State-of-the-Art , 2012 .

[9]  Roman Krzysztofowicz,et al.  Probabilistic and ensemble forecasting , 2001 .

[10]  Animesh Biswas,et al.  Multi-Objective Stochastic Programming in Fuzzy Environments , 2019 .

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

[12]  Ajantha Dahanayake Supporting Object Oriented Modeling Techniques , 2001 .

[13]  Yasmin A. Rios-Solis,et al.  Piece-Mold-Machine Manufacturing Planning , 2010 .

[14]  C. L. Wu,et al.  Methods to improve neural network performance in daily flows prediction , 2009 .

[15]  Adel Taweel,et al.  Prediction of Non-Functional Properties of Service-Based Systems: A Software Reliability Model , 2011 .

[16]  T. Ouarda,et al.  Non-stationary regional flood frequency analysis at ungauged sites , 2007 .

[17]  P. Jacovkis,et al.  Signal separation with almost periodic components: a wavelets based method , 2004 .

[18]  Chandranath Chatterjee,et al.  A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting , 2011 .

[19]  Sakgasit Ramingwong,et al.  ECSE: A Pseudo-SDLC Game for Software Engineering Class , 2014 .

[20]  B. Bates,et al.  Nonlinear, discrete flood event models, 3. Analysis of prediction uncertainty , 1988 .

[21]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

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

[23]  Mac McKee,et al.  Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs , 2011 .

[24]  Madan M. Gupta,et al.  River-Flow Forecasting Using Higher-Order Neural Networks , 2012 .

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

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

[27]  Heinrich Theodor Vierhaus,et al.  Design and Test Technology for Dependable Systems-on-Chip , 2010 .

[28]  Kathy Sanford,et al.  Emergent/See: Viewing Adolescents’ Video Game Creation through an Emergent Framework , 2012 .

[29]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[30]  Holger R. Maier,et al.  Determining Inputs for Neural Network Models of Multivariate Time Series , 1997 .

[31]  Madan M. Gupta,et al.  Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps , 2013 .

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

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

[34]  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 .

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

[36]  Ajantha Dahanayake Computer-Aided Method Engineering : Designing CASE Repositories for the 21st Century , 2001 .

[37]  G. Sahoo,et al.  Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models , 2009 .

[38]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .