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A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

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Abstract

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

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Availability of Data and Materials

Data is freely available (Kothawale and Rajeevan 2017). Used models that support the research findings are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology Patna, Patna, 800005, India

    Radhikesh Kumar, Maheshwari Prasad Singh, Bishwajit Roy & Afzal Hussain Shahid

  2. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, 466114, India

    Bishwajit Roy

Authors
  1. Radhikesh Kumar
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  2. Maheshwari Prasad Singh
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  3. Bishwajit Roy
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  4. Afzal Hussain Shahid
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Contributions

BR: conceptualization; investigation; visualization. RK, BR: validation & modeling. MPS: Supervision. RK, AHS, BR: review & editing.

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Correspondence to Bishwajit Roy.

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Kumar, R., Singh, M.P., Roy, B. et al. A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions. Water Resour Manage 35, 1927–1960 (2021). https://doi.org/10.1007/s11269-021-02822-6

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  • DOI: https://doi.org/10.1007/s11269-021-02822-6

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