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Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

Author

Listed:
  • Md Abul Ehsan Bhuiyan

    (Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269-3088, USA)

  • Feifei Yang

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269-3088, USA)

  • Nishan Kumar Biswas

    (Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA)

  • Saiful Haque Rahat

    (Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH 45220, USA)

  • Tahneen Jahan Neelam

    (Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA)

Abstract

The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications.

Suggested Citation

  • Md Abul Ehsan Bhuiyan & Feifei Yang & Nishan Kumar Biswas & Saiful Haque Rahat & Tahneen Jahan Neelam, 2020. "Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin," Forecasting, MDPI, vol. 2(3), pages 1-19, July.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:3:p:14-266:d:389754
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    References listed on IDEAS

    as
    1. Dougherty, Mark S. & Cobbett, Mark R., 1997. "Short-term inter-urban traffic forecasts using neural networks," International Journal of Forecasting, Elsevier, vol. 13(1), pages 21-31, March.
    2. Faisal Hossain & Nitin Katiyar & Yang Hong & Aaron Wolf, 2007. "The emerging role of satellite rainfall data in improving the hydro-political situation of flood monitoring in the under-developed regions of the world," 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. 43(2), pages 199-210, November.
    3. Feifei Yang & David W. Wanik & Diego Cerrai & Md Abul Ehsan Bhuiyan & Emmanouil N. Anagnostou, 2020. "Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
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    Cited by:

    1. Khurshid Jahan & Anwar Zahid & Md Abul Ehsan Bhuiyan & Iqbal Ali, 2022. "A Resilient and Nature-Based Drinking Water Supply Source for Saline and Arsenic Prone Coastal Aquifers of the Bengal Delta," Sustainability, MDPI, vol. 14(11), pages 1-22, May.
    2. Minxue He & Haksu Lee, 2021. "Advances in Hydrological Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-3, July.
    3. Khurshid Jahan & Soni M. Pradhanang & Md Abul Ehsan Bhuiyan, 2021. "Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number," Land, MDPI, vol. 10(5), pages 1-18, April.

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