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Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks

  • Research Article - Hydrology and Hydraulics
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A Correction to this article was published on 18 November 2023

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Abstract

Groundwater-level and rainfall measurements from 37 borewells in the Visakhapatnam district, Andhra Pradesh, India, from 2002 to 2021 were analyzed using Bayesian Neural Networks (BNNs) to comprehend the predictability of groundwater levels. We found chaotic dynamics in the groundwater and rainfall data, but a dominant trend component was seen in the groundwater-level data from phase plots. Dynamics suggest the presence of self-organized criticality/chaos in the groundwater dynamics over decadal time scales. We used BNN prediction models, (i) nonlinear autoregressive (NAR), (ii) nonlinear input–output (NIO) and (iii) nonlinear autoregressive exogenic Input (NARX), to predict the groundwater levels with rainfall and temperature as exogenic inputs. We noticed ~ 94 to 95% prediction accuracy with the NAR model with optimal inputs and ~ 1% improvement with added exogenic input. Interestingly, the study indicates that (i) the dynamics of the groundwater differ significantly from rainfall and temperature in the region, (ii) the nonlinear autoregressive model based on the self-organized dynamics of groundwater-level changes is robust in providing prediction accuracy up to ~ 95%, and (iii) the dynamics of remaining ~ 5% groundwater-level changes may be due to the presence of randomly varying extreme weather events and man-made/anthropogenic changes.

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Data used in the study are available from the corresponding author upon request.

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Acknowledgements

We thank the Director of CSIR-NGRI for permitting us to publish this work (Ref. No. NGRI/Lib/2023/Pub-074). We are also thankful to the anonymous reviewers and editor for the encouragement and constructive support during the review process.

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Authors and Affiliations

  1. CSIR-NGRI, Hyderabad, Telangana, India

    Rekapalli Rajesh & N. Purnachandra Rao

  2. Andhra University, Visakhapatnam, Andhra Pradesh, India

    Gathala Vinod Mathews & Shaik Kareemunisa Begum

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  1. Rekapalli Rajesh
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  3. N. Purnachandra Rao
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  4. Shaik Kareemunisa Begum
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Contributions

The first author planned the experiment and wrote the first draft of the manuscript and finalized the final manuscript. The second author downloaded the online data and processed the data under the guidance of first author and was involved in the preparation of the manuscript draft and finalization. Third and fourth authors were involved in the manuscript draft preparation and finalization.

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Correspondence to Gathala Vinod Mathews.

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Edited by Dr. Hossein Hamidifar (ASSOCIATE EDITOR).

Author Statement: This work has been done as a part of the PhD of the corresponding author G. Vinod Mathews. Groundwater level and Rainfall data (2002–2021) of the study area (Visakhapatnam) is collected from A.P. Groundwater Department. Temperature data is obtained from Terra Climate’s high-resolution online repository (Abatzoglou et al. 2018) for the period April 2002 to December 2021 https://doi.org/10.1038/sdata.2017.191. The Land Use Land Cover (LULC) time series data has been downloaded and extracted from Google Earth Engine Global Land Cover and Land Use Change, 2000–2020|GLAD (umd.edu).

The original online version of this article was revised: Figure 1 was not correct.

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Rajesh, R., Mathews, G.V., Rao, N.P. et al. Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks. Acta Geophys. 72, 2759–2772 (2024). https://doi.org/10.1007/s11600-023-01189-z

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