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Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study

Mohammed Majeed Hameed1,2, Siti Fatin Mohd Razali3,4, Wan Hanna Melini Wan Mohtar3,4

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Summary

Climate change causes Colorado River drought. A new method using Beluga Whale Optimization (BWO) with Regularized Extreme Learning Machine (RELM) improves drought forecasting accuracy up to four months ahead.

Keywords:
Global Multi-Criteria Decision AnalysisHydrological droughtMultivariate standardized streamflow indexRegularized extreme learning machineWarning systems

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Area of Science:

  • Hydrology and Climate Science
  • Environmental Modeling
  • Water Resource Management

Background:

  • Climate change is significantly reducing Colorado River streamflow, leading to severe hydrological droughts.
  • Existing drought forecasting models lack accuracy, especially for longer lead times, challenging water resource management.
  • Reliable, long-term drought prediction is crucial for environmental and human activities dependent on the Colorado River.

Purpose of the Study:

  • To develop and validate a robust drought forecasting approach for the Colorado River basin.
  • To enhance the accuracy and reliability of hydrological drought predictions using advanced optimization algorithms.
  • To assess the effectiveness of the proposed models against benchmark methods for various lead times.

Main Methods:

  • Utilized the Beluga Whale Optimization (BWO) algorithm to train and optimize Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models.
  • Validated the RELM-BWO and RF-BWO models against a K-Nearest Neighbors (KNN) benchmark model.
  • Employed Global Multi-Criteria Decision Analysis (GMCDA) to evaluate forecasting reliability across four hydrological stations.

Main Results:

  • The RELM-BWO model demonstrated superior performance, achieving the lowest root-mean square error (0.2795) and mean absolute error (0.2104).
  • RELM-BWO also exhibited the highest correlation coefficient (0.9135) and lowest uncertainty (U95 = 0.1077).
  • GMCDA confirmed the reliability of RELM-BWO forecasts up to four months in advance.

Conclusions:

  • The RELM-BWO model offers a significant advancement in hydrological drought forecasting for the Colorado River.
  • This optimized approach provides reliable early warnings, supporting effective drought management and mitigation strategies.
  • The methodology is valuable for developing advanced drought assessment and early warning systems in water-scarce regions.