Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation
| dc.authorid | 0000-0001-7547-847X | |
| dc.authorid | 0000-0002-1870-3535 | |
| dc.contributor.author | Tuğrul, Türker | |
| dc.contributor.author | Hınıs, Mehmet Ali | |
| dc.date.accessioned | 2024-07-16T11:44:53Z | |
| dc.date.available | 2024-07-16T11:44:53Z | |
| dc.date.issued | 2024 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | Drought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. | |
| dc.identifier.doi | 10.1007/s11600-024-01399-z | |
| dc.identifier.issn | 1895-6572 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https:/dx.doi.org/10.1007/s11600-024-01399-z | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12132 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | Acta Geophysica | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | ANN | |
| dc.subject | Dam Management | |
| dc.subject | Drought Modeling | |
| dc.subject | SPI | |
| dc.subject | SVM | |
| dc.subject | Wavelet Transform | |
| dc.title | Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation | |
| dc.type | Article |
