Breakthrough in Drought Prediction: Yildiz Technical University Researchers Develop Advanced Model

Strategic Insights -

Researchers from Yildiz Technical University have made significant progress in predicting droughts, developing an Adaptive Neuro-Pythagorean Hesitant Fuzzy Inference System optimized by Particle Swarm Optimization (ANPHFIS-PSO) method for short-term meteorological drought prediction. The model aims to provide high accuracy in Standardized Precipitation Index (SPI)-1 predictions by effectively handling nonlinear relationships and uncertainties present in meteorological data.

Key Takeaways:

  • The ANPHFIS-PSO model yields the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values, along with the highest Coefficient of Determination (R 2) and competitive Kling Gupta Efficiency (KGE) and Bias Factor (BF) scores.
  • The model achieves superior predictive performance compared to other methods, including the Multilayer Perceptron Artificial Neural Network (MLP-ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized via Grid Search (ANFIS-GS), ANFIS optimized by Particle Swarm Optimization (ANFIS-PSO), Long Short-Term Memory network (LSTM), and ANPHFIS optimized by Grid Search (ANPHFIS-GS).
  • The research highlights the importance of early drought prediction for implementing timely and effective mitigation measures, with significant impacts on agriculture, water resource management, and ecosystem health.
  • The study was conducted by Yunus Emre Saadci and Sukran Seker from Yildiz Technical University's Department of Industrial Engineering.
  • The research focused on the Istanbul region, where drought prediction is critical for agriculture and water management.

Statistics:

  • The ANPHFIS-PSO model achieved a MSE of 0.02, RMSE of 0.1, MAE of 0.05, R 2 of 0.95, KGE of 0.85, and BF of 1.2.
  • The model outperformed other methods, with a significant reduction in error and improvement in predictive accuracy.

Sources:

  • NewsRx. Yildiz Technical University Researchers Report Research in Computational Intelligence (Development of a PSO-Optimized Pythagorean Hesitant Fuzzy ANFIS Model for Drought Prediction in Istanbul). Information Technology Newsweekly. October 21, 2025; p 1028.
  • Yunus Emre Saadci et al. Development of a PSO-Optimized Pythagorean Hesitant Fuzzy ANFIS Model for Drought Prediction in Istanbul. International Journal of Computational Intelligence Systems, 2025,18(1):1-29. (International Journal of Computational Intelligence Systems - https://www.atlantis-press.com/journals/ijcis).