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New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

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Abstract

Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear, artificial intelligence (AI) models for optimal prediction can be far reaching in real-life decision support systems. AI-based models are considered to be favorable predictive tools since the nonlinear nature of suspended sediment data series warrants the utilization of nonlinear predictive methods for feature extraction, and for accurate simulation of suspended sediment load. In this study, Artificial Neural Network (ANN) approaches are employed to estimate the monthly sediment load where the two-phase Feed-forward Neuron Network Particle Swarm Optimization Gravitational Search Algorithm (FNN-PSOGSA) is developed, and then evaluated in respect to 3 distinct algorithms: the Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed-forward Neuron Network (FNN) and the single-phase Feed-forward Neuron Network Particle Swarm Optimization (FNN-PSO). The study is carried out using the monthly rainfall, runoff and sediment data spanning a 10 year period (2000–2009) where about 75% of data are used in model training phase, 25% in testing phase. Three statistical performance criteria namely: the mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE) and the Willmott’s Index (WI) and diagnostic plots visualizing the tested results are used to evaluate the performance of four AI-based models. The results reveal that the objective model (the two-phase FNN-PSOGSA model) and the single-phase FNN-PSO model yielded more precise results compared to the other forecast models. This result accorded to an NSE value of 0.612 (for the FNN-PSOGSA model) vs. an NS value of 0.500, 0.331 and 0.244 for the FNN-PSO, FNN and ANFIS models, and WI = 0.832 vs. 0.771, 0.692 and 0.726, respectively The study also demonstrated that the FNN model generated slightly better results than the ANFIS model for the estimation of sediment load data but overall, the two-phase FNN-PSOGSA model outperformed all comparison models. In light of the superior performance, this research advocates that the fully-optimized two-phase FNN-PSOGSA model can be explored as a decision-support tool for monthly sediment load forecasting using the rainfall and runoff values as the predictor datasets.

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Acknowledgements

The authors are grateful to Central Water Commission (CWC) Bhopal, India for supplying the runoff and sediment data and Indian Metrological Department (IMD) Pune, India for supplying the rainfall data. Dr. R C Deo thanks the support of USQ s-ADOSP (2017) research program.

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Authors and Affiliations

  1. Department of Mathematics & Computer Science, Rani Durgawati University, Jabalpur, India

    Sarita Gajbhiye Meshram & Chandrashekhar Meshram

  2. Department of Water Engineering, University of Tabriz, Tabriz, Iran

    M. A. Ghorbani & Vahid Karimi

  3. Engineering Faculty, Near East University, 99138 Nicosia, 10, Mersin, North Cyprus, Turkey

    M. A. Ghorbani

  4. School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD, 4300, Australia

    Ravinesh C. Deo

  5. Department of Water Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Mahsa Hasanpour Kashani

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  1. Sarita Gajbhiye Meshram
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  2. M. A. Ghorbani
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  3. Ravinesh C. Deo
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  4. Mahsa Hasanpour Kashani
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  5. Chandrashekhar Meshram
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  6. Vahid Karimi
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Correspondence to Sarita Gajbhiye Meshram.

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Meshram, S.G., Ghorbani, M.A., Deo, R.C. et al. New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm. Water Resour Manage 33, 2335–2356 (2019). https://doi.org/10.1007/s11269-019-02265-0

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