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Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index

Author

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  • Anurag Malik

    (G.B. Pant University of Agriculture & Technology)

  • Anil Kumar

    (G.B. Pant University of Agriculture & Technology)

  • Rajesh P. Singh

    (G.B. Pant University of Agriculture & Technology)

Abstract

Quantification and prediction of drought events are important for planning and management of water resources in coping with climate change scenarios at global and local scales. In this study, heuristic approaches including Co-Active Neuro Fuzzy Inference System (CANFIS), Multi-Layer Perceptron Neural Network (MLPNN) and Multiple Linear Regression (MLR) were utilized to predict the hydrological drought based on multi-scalar Streamflow Drought Index (SDI) at Naula and Kedar stations located in upper Ramganga River basin, Uttarakhand State, India. The SDI was calculated on 1-, 3-, 6-, 9-, 12- and 24-month time scales (SDI-1, SDI-3, SDI-6, SDI-9, SDI-12, and SDI-24) using monthly streamflow data of 33 years (1975-2007). The significant input variables (lags) for CANFIS, MLPNN, and MLR models were derived using autocorrelation and partial autocorrelation functions (ACF &PACF) at 5% significance level on SDI-1, SDI-3, SDI-6, SDI-9, SDI-12 and SDI-24 data series. The predicted values of multi-scalar SDI using CANFIS, MLPNN and MLR models were compared with the calculated values, based on root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of correlation (COC) and Willmott index (WI). The visual interpretation was also made using line diagram, scatter diagram and Taylor diagram (TD). The results of analysis revealed that the performance of CANFIS models was the best for hydrological drought prediction at 3-, 6- and 12-month time scales for Naula station, and at 1-, 3-, 12- and 24-month time scales for Kedar station; while MLPNN was the best at 1- and 9-month time scales for Naula station, and at 6- and 9-month time scales for Kedar station. The MLR model was found to be the best at 24-month time scale for Naula station only. The results of this study could be helpful in prediction of hydrological drought on multiple time scales and decision making for remedial schemes to cope with hydrological drought at Naula and Kedar stations.

Suggested Citation

  • Anurag Malik & Anil Kumar & Rajesh P. Singh, 2019. "Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3985-4006, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:11:d:10.1007_s11269-019-02350-4
    DOI: 10.1007/s11269-019-02350-4
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    References listed on IDEAS

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    6. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 537-579, October.
    7. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    8. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    9. Elnaz Ghabelnezam & Raoof Mostafazadeh & Zeinab Hazbavi & Guangwei Huang, 2023. "Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    10. Fatemeh Barzegari Banadkooki & Vijay P. Singh & Mohammad Ehteram, 2021. "Multi-timescale drought prediction using new hybrid artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2461-2478, April.
    11. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    12. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
    13. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).

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