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Updated: Jul 19, 2025

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Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

Padala Raja Shekar1, Aneesh Mathew2, Arun P S3

  • 1Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India.

Environmental Monitoring and Assessment
|August 17, 2023
PubMed
Summary

Accurate streamflow estimation is vital for water resource management. The Long Short-Term Memory (LSTM) model significantly outperformed other artificial intelligence (AI) and Soil and Water Assessment Tool (SWAT) models in simulating rainfall-runoff processes in the Murredu River basin.

Keywords:
HGBoostLSTMRainfall-runoff modelsSWATXGBoost

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Area of Science:

  • Hydrology and Water Resources Engineering
  • Environmental Science
  • Artificial Intelligence in Environmental Modeling

Background:

  • Increasing global water demand necessitates precise streamflow estimation for effective watershed management.
  • Rainfall-runoff models are crucial tools for understanding and predicting hydrological processes.
  • The Murredu River basin faces challenges in water resource management due to growing demands.

Purpose of the Study:

  • To accurately estimate monthly streamflow in the Murredu River basin using various hydrological models.
  • To compare the performance of the Soil and Water Assessment Tool (SWAT) model against multiple artificial intelligence (AI) models.
  • To identify the most effective model for rainfall-runoff simulation to support sustainable water resource planning.

Main Methods:

  • Employed the Soil and Water Assessment Tool (SWAT) model for hydrological simulation.
  • Utilized eight artificial intelligence (AI) models: k-nearest neighbour, support vector regression, linear regression, artificial neural networks, random forest, XGBoost, Histogram-based Gradient Boost, and Long Short-Term Memory (LSTM).
  • Calibrated and validated models using monthly streamflow data from the Murredu River basin (1999-2005).

Main Results:

  • All nine models demonstrated suitability for simulating the rainfall-runoff process.
  • The Long Short-Term Memory (LSTM) model exhibited superior performance, achieving high coefficients of determination (R² = 0.97) and Nash-Sutcliffe efficiency (NSE = 0.96 in calibration; R² = 0.97, NSE = 0.92 in validation).
  • Other AI and SWAT models provided satisfactory results but were less accurate than LSTM.

Conclusions:

  • The Long Short-Term Memory (LSTM) model is highly effective for accurate monthly streamflow modeling in the Murredu River basin.
  • Selecting advanced AI models like LSTM can significantly enhance the precision of rainfall-runoff simulations.
  • Findings support the use of optimal models for improved water resource management and sustainable planning in river basins.