Abstract
Long-range (1 to 6 months in advance) prediction of droughts is challenging due to its inherent complexity. In this study, we developed a Long-Range Hydrological Drought Prediction Framework (HDPF), empowered by a Deep Learning (DL) approach. Starting with two state-of-the-art approaches, namely Long Short-Term Memory (LSTM), and one-dimensional Convolutional neural networks (Conv1D), we picked out Conv1D to develop the HDPF, being its relatively better performance. The devised HDPF leverages a comprehensive set of eight meteorological precursors, harnessing their collective potential to offer predictions of reasonable accuracy (> 70%). The developed HDPF is able to extract the hidden information from the pool of meteorological precursors along with its evolution over time and influence on the upcoming drought status. Additionally, while comparing the performance of the Conv1D against LSTM, it is noticed that the performance of LSTM is at par with that of Conv1D. However, considering the model parsimony and computational time we advocate the usage of Conv1D. Moreover, comparison against other popular machine learning models, such as Support Vector Regression (SVR) and Feedforward Neural Network (FNN) further affirms the superiority as well as benefits of Conv1D. The developed HDPF can also be useful to other basins in a different climate regime, subject to its recalibration with the location-specific datasets. Overall, this study advances drought prediction methodologies by demonstrating the potential of DL techniques while underscoring the utility and adaptability of the proposed Conv1D-based HDPF.
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Data Availability
The data that support the findings of this study are available from: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5, and https://indiawris.gov.in/wris/#/RiverMonitoring. The datasets are freely available and was accessed by the authors in November 2022.
Code Availability
The codes required for the analysis are written in python using scientific python development environment (spyder) IDE. The codes may be available on request from the authors.
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Acknowledgements
This study is supported by a sponsored project supported by Ministry of Earth Science (MoES), Government of India (Grant no. MoES/PAMC/H&C/124/2019-PC-II). Authors further acknowledge the National Supercomputing Mission (NSM) for providing computing resources of ‘PARAM Shakti’ at IIT Kharagpur, which is implemented by C-DAC and supported by the Ministry of Electronics and Information Technology (MeitY).
Funding
Ministry of Earth Science (MoES), Government of India, Grant no. MoES/PAMC/H & C/124/2019-PC-II, Rajib Maity.
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Conceptualization: Rajib Maity; Methodology: Mohd Imran Khan, Rajib Maity; Formal analysis: Mohd Imran Khan, Investigation: Mohd Imran Khan, Rajib Maity; Writing—original draft preparation: Mohd Imran Khan; Writing—review and editing: Rajib Maity; Funding acquisition: Rajib Maity; Resources: Rajib Maity; Supervision: Rajib Maity.
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Khan, M.I., Maity, R. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning. Water Resour Manage 38, 1497–1509 (2024). https://doi.org/10.1007/s11269-024-03735-w
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DOI: https://doi.org/10.1007/s11269-024-03735-w