Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies

SARIGÖL M., KATİPOĞLU O. M., DALKILIÇ H. Y.

Pure and Applied Geophysics, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00024-024-03607-9
  • Dergi Adı: Pure and Applied Geophysics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Geobase, INSPEC
  • Anahtar Kelimeler: Deep learning, konya closed basin, machine learning, neural networks, streamflow prediction
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

Modeling monthly stream flows most accurately is of vital importance for water resource management, agricultural irrigation and efficient hydroelectric energy production, especially in semi-arid areas. Soft computing approaches have recently taken an important place in estimating streamflow time series. The potential of various data-driven approaches to predict streamflow in challenging climate conditions was evaluated. The study used machine and deep learning algorithms to model average monthly stream flows in two stream gauging stations in semi-arid region of the Konya closed basin where agriculture is at the forefront, accurate and reliable estimation of the stream flows is the basis of the study. For this, the performances of emotional neural network algorithm (EmNN), long-short term memory (LSTM), Elman neural network (ENN), nonlinear autoregressive exogenous model (NARX), recurrent neural network (RNN), group method of data handling (GMDH) were compared. The study’s unique contribution lies in its comprehensive comparison of these diverse algorithms, including newer approaches like EmNN, in the specific context of semi-arid hydrology. Partial autocorrelation analysis was applied to select input combinations, and lagged values exceeding 95% confidence limits were presented to the models as the most essential features. Artificial intelligence (AI) models use lagged stream flows to predict the streamflow time series. Statistical parameters, scatter diagrams and a time series approach are used to compare model performance. The GMDH model produced the following test results for 1604 no station: KGE: 0.656, R2: 0.608, NSE: 0.343, RMSE: 27.021, MAE: 3.834, MAPE: 0.662, MBE: −0.217, BF: 0.972. Similarly, for 1623 no station, the GMDH model yielded the following test results: KGE: 0.770, R2: 0.615, NSE: 0.531, RMSE: 0.006, MAE: 0.047, MAPE: 0.217, MBE: −0.012, BF: 0.956. In addition, the EmNN algorithm was the approach with second prediction accuracy. The findings of the study are important resources for optimizing the selection of AI models for streamflow prediction in semi-regional areas. The study also provides critical information for policymakers and decision-makers in similar climate zones worldwide for water resource management.