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Climate-driven flood hazard assessment in data-scarce mountainous basins using a GIS-based machine learning and hydrodynamic modelling under CMIP6 SSP scenarios.

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Related Experiment Video

Updated: Jun 18, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6.

Hamid Anwar1, Afed Ullah Khan2, Basir Ullah1

  • 1Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan.

Scientific Reports
|July 30, 2024
PubMed
Summary

Time Lag Artificial Neural Networks (TLANN) accurately predicted streamflow in the Swat River Basin. This deep learning model offers reliable water resource management insights for future climate scenarios.

Keywords:
CMIP6Deep learningGCMPredictionStreamflow

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Area of Science:

  • Hydrology and Water Resource Management
  • Climate Change Impact Assessment
  • Artificial Intelligence in Environmental Science

Background:

  • Accurate streamflow prediction is crucial for water resource management, especially under changing climate conditions.
  • Deep learning models offer advanced capabilities for hydrological forecasting.
  • Understanding future streamflow variations under different Shared Socioeconomic Pathways (SSPs) is essential for adaptation strategies.

Purpose of the Study:

  • To predict daily streamflow in the Swat River Basin using four deep learning models.
  • To evaluate and compare the performance of Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN), and Long Short-Term Memory (LSTM).
  • To project future streamflow under SSP245 and SSP585 climate scenarios.

Main Methods:

  • Four deep learning models (FFANN, SANN, TLANN, LSTM) were employed for streamflow prediction.
  • General Circulation Models (GCMs) were selected using Taylor Diagram, Random Forest, and Gradient Boosting for Multi-Model Ensemble (MME) computation.
  • Compromise Programming determined the optimal MME for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation; models were trained and tested using observed data.

Main Results:

  • The Time Lag Artificial Neural Network (TLANN) demonstrated superior performance with the lowest RMSE, MSE, MAE, and highest R² during both training and testing phases.
  • Statistical performance indicators confirmed TLANN's effectiveness in capturing streamflow dynamics.
  • Future streamflow predictions were generated using TLANN based on MME of SSP245 and SSP585 scenarios.

Conclusions:

  • TLANN is a highly effective deep learning model for daily streamflow prediction in the Swat River Basin.
  • The study provides valuable insights into potential future streamflow changes under different climate scenarios.
  • Findings support informed planning, management, and policy-making for water resource projects in the region.