IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i7d10.1007_s11269-019-02265-0.html
   My bibliography  Save this article

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

Author

Listed:
  • Sarita Gajbhiye Meshram

    (Rani Durgawati University)

  • M. A. Ghorbani

    (University of Tabriz
    Near East University)

  • Ravinesh C. Deo

    (University of Southern Queensland)

  • Mahsa Hasanpour Kashani

    (University of Mohaghegh Ardabili)

  • Chandrashekhar Meshram

    (Rani Durgawati University)

  • Vahid Karimi

    (University of Tabriz)

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.

Suggested Citation

  • Sarita Gajbhiye Meshram & M. A. Ghorbani & Ravinesh C. Deo & Mahsa Hasanpour Kashani & Chandrashekhar Meshram & Vahid Karimi, 2019. "New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2335-2356, May.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:7:d:10.1007_s11269-019-02265-0
    DOI: 10.1007/s11269-019-02265-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02265-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-019-02265-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
    3. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    4. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    2. Alireza Moghaddam Nia & Debasmita Misra & Mahsa Hasanpour Kashani & Mohsen Ghafari & Madhumita Sahoo & Marzieh Ghodsi & Mohammad Tahmoures & Somayeh Taheri & Maryam Sadat Jaafarzadeh, 2023. "Runoff and Sediment Yield Processes in a Tropical Eastern Indian River Basin: A Multiple Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-15, August.
    3. Lubna Jamal Chachan, 2022. "Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey," Technium, Technium Science, vol. 4(1), pages 239-249.
    4. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    5. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    6. Arvind Yadav & Premkumar Chithaluru & Aman Singh & Marwan Ali Albahar & Anca Jurcut & Roberto Marcelo Álvarez & Ramesh Kumar Mojjada & Devendra Joshi, 2022. "Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    2. Kazi Ali Tamaddun & Ajay Kalra & Sajjad Ahmad, 2019. "Spatiotemporal Variation in the Continental US Streamflow in Association with Large-Scale Climate Signals Across Multiple Spectral Bands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1947-1968, April.
    3. Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    5. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "A general model for energy hub economic dispatch," Applied Energy, Elsevier, vol. 190(C), pages 1090-1111.
    6. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    7. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    8. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    9. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    10. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
    11. Dong-Hyuk Kim & Ha-Yeong Kim & Ki-Hoon Moon & Jin-Hoon Jeong, 2022. "Prediction of Fracture Toughness of Intermediate Layer of Asphalt Pavements Using Artificial Neural Network," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
    12. Gonzalez de Durana, Jose & Barambones, Oscar, 2018. "Technology-free microgrid modeling with application to demand side management," Applied Energy, Elsevier, vol. 219(C), pages 165-178.
    13. Guangyang Wu & Lanhai Li & Sajjad Ahmad & Xi Chen & Xiangliang Pan, 2013. "A Dynamic Model for Vulnerability Assessment of Regional Water Resources in Arid Areas: A Case Study of Bayingolin, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3085-3101, June.
    14. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    15. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    16. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    17. Sahoo, Subham & Pullaguram, Deepak & Mishra, Sukumar & Wu, Jianzhong & Senroy, Nilanjan, 2018. "A containment based distributed finite-time controller for bounded voltage regulation & proportionate current sharing in DC microgrids," Applied Energy, Elsevier, vol. 228(C), pages 2526-2538.
    18. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
    19. Polamarasetty P Kumar & Ramakrishna S. S. Nuvvula & Md. Alamgir Hossain & SK. A. Shezan & Vishnu Suresh & Michal Jasinski & Radomir Gono & Zbigniew Leonowicz, 2022. "Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context," Energies, MDPI, vol. 15(14), pages 1-50, July.
    20. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:33:y:2019:i:7:d:10.1007_s11269-019-02265-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.