Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy

Ali E., Zerouali B., Tariq A., KATİPOĞLU O. M., Bailek N., Santos C. A. G., ...Daha Fazla

Water science and technology : a journal of the International Association on Water Pollution Research, cilt.90, sa.3, ss.844-877, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 90 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.2166/wst.2024.222
  • Dergi Adı: Water science and technology : a journal of the International Association on Water Pollution Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chimica, Compendex, Environment Index, Geobase, Pollution Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.844-877
  • Anahtar Kelimeler: data-driven frameworks, discrete wavelet transform, inflow prediction, parameter optimization, particle swarm optimization, reservoir management
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.