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Exploring a long short-term memory for mountain flood forecasting based on watershed-internal knowledge graph and

Songsong Wang1,2, Ouguan Xu1

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This study enhances mountain flood forecasting using Long Short-Term Memory (LSTM) networks integrated with a watershed-internal Knowledge Graph (KG) and Large Language Model (LLM). This LLM-KG-LSTM approach improves forecasting accuracy by incorporating critical hydrological data relationships.

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Area of Science:

  • Hydrology and Water Resources Engineering
  • Artificial Intelligence in Environmental Science
  • Disaster Management and Forecasting

Background:

  • Mountain floods present significant challenges due to rapid water level fluctuations in small watersheds.
  • Accurate real-time forecasting requires comprehensive data on factors influencing these hydrological events.
  • Existing forecasting models may not fully capture complex watershed dynamics and data interdependencies.

Purpose of the Study:

  • To investigate the efficacy of Long Short-Term Memory (LSTM) networks for mountain flood forecasting.
  • To develop and apply a watershed-internal Knowledge Graph (KG) and Large Language Model (LLM) for enhanced hydrological data organization and analysis.
  • To optimize input data selection and improve the accuracy of water level predictions.

Main Methods:

  • Development of a hydrological KG for specific forecasting points (Qixi Reservoir, Qiaodongcun) in Zhejiang Province, China.
  • Integration of the KG with a Large Language Model (LLM) to structure and interpret watershed information.
  • Application and comparative analysis of LSTM, Recurrent Neural Networks (RNN), and Gated Recurrent Units (GRU) for water level forecasting.

Main Results:

  • The integrated LLM-KG-LSTM model demonstrated a 3% increase in accuracy compared to the standard LSTM model.
  • The LSTM-based models outperformed both RNN and GRU in mountain flood forecasting accuracy.
  • The study successfully identified critical factors influencing water level changes and optimized input data combinations.

Conclusions:

  • Integrating watershed-internal KGs and LLMs with LSTM networks significantly enhances mountain flood forecasting accuracy.
  • The proposed methodology provides a robust framework for organizing multi-dimensional disaster data and improving forecasting algorithms.
  • Future research should focus on the interplay between disaster data relationships and algorithmic parallelism for advanced forecasting.