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Monthly streamflow forecasting using artificial intelligence approach: a case study in a semi-arid region of India

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Abstract

Accurate and reliable streamflow forecasting is paramount in the field of water resource planning and management, especially in semi-arid regions. However, streamflow time series are highly complex and non-linear in nature; traditional or physical-based models may fail to capture the complexity and maintain the robustness of the datasets. Therefore, the present study aims to improve the forecasting accuracy and reduce the uncertainty in the datasets by using the data-driven approach such as artificial neural network (ANN) that can efficiently handle the non-linearity in the large and complex hydrological data. This study method includes two steps: i.e., first to develop the ANN models using different combinations of inputs such as rainfall, temperature, and streamflow lag by one or two and then to validate the developed models to forecast the streamflow by using a total of four performance evaluation indices such as correlation coefficient (R), root mean square error (RMSE), modified Nash-Sutcliff efficiency (MNSE), and modified index of agreement (MIA). The proposed method is demonstrated in Jakham reservoir located in Pratapgarh district, Rajasthan, India, for improving the accuracy of monthly streamflow forecasting over a 40-year period (1975–2015). We found that increasing the number of input parameters improves the accuracy of the model and enhance its performance. According to the results, the ANN models 5 and 6 (M5 and M6) showed significant variation in the performance evaluation criteria. This clearly indicates that ANN model with an input combination of lag one or two streamflow (i.e., model M5 and M6) is performed better when compared to a model that incorporates only monthly rainfall and monthly lag one or two rainfall as inputs. Overall, the application of ANN models M5 and M6 (with lag one and two streamflow as an input) can forecast monthly streamflow forecasting with better accuracy.

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Authors and Affiliations

  1. Groundwater Hydrology Division, National Institute of Hydrology, Roorkee, 247667, Uttarakhand, India

    Priyanka Sharma & Survey D. Sharma

  2. Department of Soil and Water Engineering, Punjab Agricultural University, Punjab, 141027, Ludhiana, India

    Dnyaneshwar Madane

  3. Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur, 313001, India

    S. R. Bhakar

Authors
  1. Priyanka Sharma
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  2. Dnyaneshwar Madane
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  3. S. R. Bhakar
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  4. Survey D. Sharma
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Correspondence to Priyanka Sharma.

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Responsible Editor: Broder J. Merkel

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Sharma, P., Madane, D., Bhakar, S.R. et al. Monthly streamflow forecasting using artificial intelligence approach: a case study in a semi-arid region of India. Arab J Geosci 14, 2440 (2021). https://doi.org/10.1007/s12517-021-08778-6

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  • DOI: https://doi.org/10.1007/s12517-021-08778-6

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