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Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

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  • Junfei Chen
  • Ming Li
  • Weiguang Wang

Abstract

Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.

Suggested Citation

  • Junfei Chen & Ming Li & Weiguang Wang, 2012. "Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, September.
    • Handle: RePEc:hin:jnlmpe:915053
    DOI: 10.1155/2012/915053
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    Cited by:

    1. Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    2. Seyed Mohammad Ehsan Azimi & Seyed Javad Sadatinejad & Arash Malekian & Mohammad Hossein Jahangir, 2023. "Application of artificial intelligence hybrid models for meteorological drought prediction," 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. 116(2), pages 2565-2589, March.
    3. Neeta Nandgude & T. P. Singh & Sachin Nandgude & Mukesh Tiwari, 2023. "Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    4. 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.

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