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

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dc.authoridAkturk, Gaye/0000-0002-9477-7827
dc.authorid/0000-0003-1738-3565
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorErtugay, Nese
dc.contributor.authorElshaboury, Nehal
dc.contributor.authorAkturk, Gaye
dc.contributor.authorKartal, Veysi
dc.contributor.authorPande, Chaitanya Baliram
dc.date.accessioned2024-12-24T19:27:32Z
dc.date.available2024-12-24T19:27:32Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractDrought 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 shortterm 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.
dc.identifier.doi10.1016/j.pce.2024.103646
dc.identifier.issn1474-7065
dc.identifier.issn1873-5193
dc.identifier.scopus2-s2.0-85194529348
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.pce.2024.103646
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6688
dc.identifier.volume135
dc.identifier.wosWOS:001248732300002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofPhysics and Chemistry of The Earth
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectHydrologic drought
dc.subjectFirefly algorithm
dc.subjectGenetic algorithm
dc.subjectNature-inspired optimization
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.titleA novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting
dc.typeArticle

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