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Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India

Author

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  • Singh, A.
  • Imtiyaz, M.
  • Isaac, R.K.
  • Denis, D.M.

Abstract

The present study was conducted in the Nagwa watershed in Jharkhand state, India. The watershed has been identified as a sensitive area for sediment and non-point source pollution. Damodar Valley Corporation (DVC), Hazaribagh, India has taken initiatives to implement several soil and water conservation measures. A calibrated and validated model to simulate hydrological processes will be a great help to the concerned watershed managers. The objectives of this study were to compare the monthly sediment yield simulation results from the soil and water assessment tool (SWAT) and the multilayer perceptron (MLP) artificial neural network model during the calibration (1993–2004) and validation periods (2005–2007), and determine the most appropriate model for the watershed. The annual average measured sediment yield was 3.7t/ha. The annual average simulated sediment yield was 3.1 and 5.0t/ha for MLP and SWAT model, respectively. Both models generally provided good correlation and model efficiency for simulating monthly sediment yield during calibration and validation. For the SWAT model the coefficient of determination (R2) and Nash-Sutcliffe simulation efficiency (NSE) values were 0.78 and 0.76 during calibration and 0.68 and 0.66 during validation, respectively. The MLP model performed better than SWAT with R2 and NSE values of 0.84 and 0.76 during training and 0.77 and 0.74 during validation periods, respectively.

Suggested Citation

  • Singh, A. & Imtiyaz, M. & Isaac, R.K. & Denis, D.M., 2012. "Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India," Agricultural Water Management, Elsevier, vol. 104(C), pages 113-120.
    • Handle: RePEc:eee:agiwat:v:104:y:2012:i:c:p:113-120
    DOI: 10.1016/j.agwat.2011.12.005
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    References listed on IDEAS

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    1. Ashok Mishra & S. Kar & V. Singh, 2007. "Prioritizing Structural Management by Quantifying the Effect of Land Use and Land Cover on Watershed Runoff and Sediment Yield," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1899-1913, November.
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    4. Swatantra Kumar Dubey & JungJin Kim & Younggu Her & Devesh Sharma & Hanseok Jeong, 2023. "Hydroclimatic Impact Assessment Using the SWAT Model in India—State of the Art Review," Sustainability, MDPI, vol. 15(22), pages 1-40, November.
    5. Vanessa Sari & Nilza Maria Reis Castro & Olavo Correa Pedrollo, 2017. "Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4909-4923, December.
    6. Jing Yu & Yongwei Yuan & Yan Nie & Enjun Ma & Hongji Li & Xiaoli Geng, 2015. "The Temporal and Spatial Evolution of Water Yield in Dali County," Sustainability, MDPI, vol. 7(5), pages 1-17, May.
    7. Evidence Chinedu Enoguanbhor & Florian Gollnow & Blake Byron Walker & Jonas Ostergaard Nielsen & Tobia Lakes, 2021. "Key Challenges for Land Use Planning and Its Environmental Assessments in the Abuja City-Region, Nigeria," Land, MDPI, vol. 10(5), pages 1-19, April.

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