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<p>The Himalayan rivers are prone to frequent floods and pose serious risks to human lives and infrastructure. Accurate discharge prediction is crucial for effective flood mitigation and sustainable water resource management. This study focuses on the Sindh River, a vital water source in the Kashmir valley, supporting hydropower and irrigation. Advanced artificial intelligence techniques were applied to analyze 40 years of historical discharge data to address its complex hydrological dynamics. The study evaluated various machine learning models, including K-Nearest Neighbors, Support Vector Regression, Gradient Boosting, Extreme Gradient Boosting, Random Forest (RF), Artificial Neural Network (ANN), and Seasonal Autoregressive Integrated Moving Average (SARIMA). A hybrid RF-SARIMA model was also developed to improve prediction accuracy. The dataset was split into 80% for training and 20% for testing. Model performance was assessed using statistical metrics such as coefficient of determination (<i>R</i>²), mean squared error, mean absolute error, and root mean squared error, along with visual tools like box plots, scatter plots, Taylor diagrams, and time series analyses. Results revealed that RF, SARIMA, and ANN performed well among standalone models. However, the hybrid RF-SARIMA model delivered the best results, with an <i>R</i>² of 0.88 and a correlation coefficient above 0.9 for monthly discharge predictions. This study highlights the hybrid model’s potential to enhance discharge forecasting for the Sindh River, providing valuable insights for flood management and sustainable water planning in the Himalayan regions.</p>

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AI-driven forecasting of river discharge: the case study of the Himalayan mountainous river

  • 期刊论文

  • 发表日期：

    2025年2月8日

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  • Shakeel Ahmad Rather,
  • Mahesh Patel,
  • Kanish Kapoor

摘要

The Himalayan rivers are prone to frequent floods and pose serious risks to human lives and infrastructure. Accurate discharge prediction is crucial for effective flood mitigation and sustainable water resource management. This study focuses on the Sindh River, a vital water source in the Kashmir valley, supporting hydropower and irrigation. Advanced artificial intelligence techniques were applied to analyze 40 years of historical discharge data to address its complex hydrological dynamics. The study evaluated various machine learning models, including K-Nearest Neighbors, Support Vector Regression, Gradient Boosting, Extreme Gradient Boosting, Random Forest (RF), Artificial Neural Network (ANN), and Seasonal Autoregressive Integrated Moving Average (SARIMA). A hybrid RF-SARIMA model was also developed to improve prediction accuracy. The dataset was split into 80% for training and 20% for testing. Model performance was assessed using statistical metrics such as coefficient of determination (R²), mean squared error, mean absolute error, and root mean squared error, along with visual tools like box plots, scatter plots, Taylor diagrams, and time series analyses. Results revealed that RF, SARIMA, and ANN performed well among standalone models. However, the hybrid RF-SARIMA model delivered the best results, with an R² of 0.88 and a correlation coefficient above 0.9 for monthly discharge predictions. This study highlights the hybrid model’s potential to enhance discharge forecasting for the Sindh River, providing valuable insights for flood management and sustainable water planning in the Himalayan regions.