Beyond traditional metrics: exploring the potential of hybrid algorithms for Drought characterization and prediction in the Tromso Region, Norway
| dc.authorid | 0000-0003-2906-0771 | |
| dc.authorid | 0000-0001-7547-847X | |
| dc.authorid | 0000-0002-1870-3535 | |
| dc.contributor.author | Oruç, Sertaç | |
| dc.contributor.author | Tuğrul, Türker | |
| dc.contributor.author | Hınıs, Mehmet Ali | |
| dc.date.accessioned | 2024-11-14T07:55:10Z | |
| dc.date.available | 2024-11-14T07:55:10Z | |
| dc.date.issued | 2024 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. | |
| dc.identifier.doi | 10.3390/app14177813 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.issue | 17 | en_US |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https:/dx.doi.org/10.3390/app14177813 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12637 | |
| dc.identifier.volume | 14 | en_US |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.relation.ispartof | Applied Sciences (Switzerland) | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | ANFIS | |
| dc.subject | Dam Management | |
| dc.subject | Deep Learning | |
| dc.subject | Drought Modeling | |
| dc.subject | SPI | |
| dc.subject | Risk Assessment | |
| dc.title | Beyond traditional metrics: exploring the potential of hybrid algorithms for Drought characterization and prediction in the Tromso Region, Norway | |
| dc.type | Article |
