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Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network

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Abstract

Monsoon floods are recurring hazards in most countries of South-East Asia. In this paper, a wavelet transform-genetic algorithm-neural network model (WAGANN) is proposed for forecasting 1-day-ahead monsoon river flows which are difficult to model as they are characterized by irregularly spaced spiky large events and sustained flows of varying duration. Discrete wavelet transform (DWT) is employed for preprocessing the time series and genetic algorithm (GA) for optimizing the initial parameters of an artificial neural network (ANN) prior to the network training. Depending on different inputs, four WAGANN models are developed and evaluated for predicting flows in two Indian Rivers, the Kosi and the Gandak. These rivers are infamous for carrying large flows during monsoon (June to Sept), making the entire North Bihar of India unsafe for habitation or cultivation. When compared, WAGANN models are found to be better than autoregression models (ARs) and GA-optimized ANN models (GANNs) which use original flow time series (OFTS) for inputs, in simulating river flows during monsoon. In addition, WAGANN models predicted relatively reasonable estimates for the extreme flows, showing little bias for underprediction or overprediction.

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Acknowledgment

This research is supported by All India Council of Technical Education, New Delhi, India (F.N. 8023/BOR/RID/RPS-45/2007-8) and University Grants Commission, New Delhi, India (F.N. 33-482/2007).

Author information

Authors and Affiliations

  1. Civil Engineering Department, BIT Mesra, Patna Campus, Patna, India, 800014

    Rajeev Ranjan Sahay

  2. Civil Engineering Department, BIT Mesra, Ranchi, India

    Ayush Srivastava

Authors
  1. Rajeev Ranjan Sahay
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  2. Ayush Srivastava
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Corresponding author

Correspondence to Rajeev Ranjan Sahay.

Appendix I

Appendix I

  1. a.

    Optimal values of weights and biases for the ANN (3,6,1), used in WAGANN3 and the GANN3 models (for the Gandak River)

    1. (i)

      Interconnection weights from hidden neurons to input neurons:

      [15.4486 23.0843 -59.9149; 3.0532 11.5108 -4.9351;-5.928 4.5639 -3.3893;

      −6.7051 12.8147 7.0374; 14.5115 21.3057 -57.6155;-0.4871 -5.4408 2.8422]

    2. (ii)

      Interconnection weights from hidden neurons to output neuron:

      [14.0027 -2.3576 -3.0386 -8.3629 -14.2711 -6.3305]

    3. (iii)

      Bias to neurons in hidden layer:

      [0.5931; -4.612; 1.6813; 1.2003; 0.9016; 0.7423]

    4. (iv)

      Bias to output neuron:

      [12.474]

  2. b.

    Optimal values of weights and biases for the ANN (4,6,1), used in WAGANN4 and the GANN4 models (for the Kosi River)

    1. (i)

      Interconnection weights from hidden neurons to input neurons:

      [0.65459 -12.61 5.0574 -2.6024; -0.83345 -12.7699 8.5516 -1.1088;

      7.8724 -9.3901 -2.9088 -0.71956; 1.5982 0.87175 -1.8122 0.21225;

      2.4736 -10.7611 0.69216 -2.1315; -18.0929 8.5845 -25.7407 48.061]

    2. (ii)

      Interconnection weights from hidden neurons to output neuron:

      [15.6315 9.3779 -7.3199 38.6412 -14.6288 -9.0617]

    3. (iii)

      Bias to neurons in hidden layer:

      [1.9931; 4.6363; 4.3863; -0.25541; 1.9696; 7.4002]

    4. (iv)

      Bias to output neuron:

      [−13.6284]

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Sahay, R.R., Srivastava, A. Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network. Water Resour Manage 28, 301–317 (2014). https://doi.org/10.1007/s11269-013-0446-5

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  • DOI: https://doi.org/10.1007/s11269-013-0446-5

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