A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

KATİPOĞLU O. M., ERTUGAY N., Elshaboury N., Aktürk G., Kartal V., Pande C. B.

Physics and Chemistry of the Earth, vol.135, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 135
  • Publication Date: 2024
  • Doi Number: 10.1016/j.pce.2024.103646
  • Journal Name: Physics and Chemistry of the Earth
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, Chimica, Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Keywords: Artificial intelligence, Deep learning, Firefly algorithm, Genetic algorithm, Hydrologic drought, Nature-inspired optimization
  • Erzincan Binali Yildirim University Affiliated: Yes


Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.