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<p>Accurate rainfall forecasting is crucial in disaster management, agriculture, and hydrological resource planning. Traditional methods for rainfall prediction often face challenges in capturing the complex and nonlinear relationships inherent in meteorological data. In this paper, we proposed a novel multiscale ensemble approach for rainfall forecasting using ensemble empirical mode decomposition (EEMD) coupled with a long short-term memory (LSTM) model. EEMD is a pre-processing technique to decompose the rainfall data series into intrinsic mode functions (IMFs), each representing a specific oscillatory mode. These IMFs, which capture the different temporal scales of rainfall fluctuations, are fed into an LSTM neural network to effectively capture the long-term dependencies and nonlinear patterns. The proposed EEMD- LSTM model is trained and validated using historical rainfall data of Telangana state. The proposed model’s performance is rigorously assessed using various statistical metrics and compared against traditional forecasting methods. The results demonstrate the proposed model’s superiority in accuracy and robustness in capturing rainfall patterns. The EEMD-LSTM model exhibits significant potential in improving short-term rainfall forecasts, providing valuable insights for decision-making and proactive response strategies in the face of potential rainfall-related disasters.</p>

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Multiscale rainfall forecasting using a hybrid ensemble empirical mode decomposition and LSTM model

  • 期刊论文

  • 发表日期：

    2025年2月5日

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  • Bellamkonda Jyostna,
  • Admala Meena,
  • Santosha Rathod,
  • Mangal Deep Tuti,
  • Kapil Choudhary,
  • Achal Lama,
  • Ammaladinne Tharun Kumar,
  • Bojjireddygari Nanda Kumar Reddy,
  • D. Bhanusree,
  • J. Rakesh,
  • Arun Kumar Swarnaraj,
  • Anil Kumar

摘要

Accurate rainfall forecasting is crucial in disaster management, agriculture, and hydrological resource planning. Traditional methods for rainfall prediction often face challenges in capturing the complex and nonlinear relationships inherent in meteorological data. In this paper, we proposed a novel multiscale ensemble approach for rainfall forecasting using ensemble empirical mode decomposition (EEMD) coupled with a long short-term memory (LSTM) model. EEMD is a pre-processing technique to decompose the rainfall data series into intrinsic mode functions (IMFs), each representing a specific oscillatory mode. These IMFs, which capture the different temporal scales of rainfall fluctuations, are fed into an LSTM neural network to effectively capture the long-term dependencies and nonlinear patterns. The proposed EEMD- LSTM model is trained and validated using historical rainfall data of Telangana state. The proposed model’s performance is rigorously assessed using various statistical metrics and compared against traditional forecasting methods. The results demonstrate the proposed model’s superiority in accuracy and robustness in capturing rainfall patterns. The EEMD-LSTM model exhibits significant potential in improving short-term rainfall forecasts, providing valuable insights for decision-making and proactive response strategies in the face of potential rainfall-related disasters.