Author dc.contributor.author | Jafarizadeh Farshad | |
Author dc.contributor.author | Rajabi Meysam | |
Author dc.contributor.author | Tabasi Somayeh | |
Author dc.contributor.author | Seyedkamali Reza | |
Author dc.contributor.author | Davoodi Shadfar | |
Author dc.contributor.author | Ghorbani Hamzeh | |
Author dc.contributor.author | Alvar Mehdi Ahmadi | |
Author dc.contributor.author | Radwan Ahmed E. | |
Author dc.contributor.author | Mako Csaba | |
Availability Date dc.date.accessioned | 2023-02-16T11:10:46Z | |
Availability Date dc.date.available | 2023-02-16T11:10:46Z | |
Release dc.date.issued | 2022 | |
Issn dc.identifier.issn | 2352-4847 | |
uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/19949 | |
Abstract dc.description.abstract | Hydrological drought forecasting is a key component in water resources modeling as it relates directly to water availability. It is crucial in managing and operating dams, which are constructed in rivers. In this study, multiple extreme learning machines (ELMs) are utilized to forecast hydrological drought. For this purpose, the standardized hydrological drought index (SHDI) and standardized precipitation index (SPI) are computed for 1 and 3 aggregated months. Two scenarios are considered, namely, using SHDI in previous months as the input, and using SHDI and SPI in previous months as the input. Considering these scenarios and two timescales (1 and 3 months), 12 input–output combinations are generated. Then, five different ELMs and support vector machine models are used to predict the SHDI on both timescales. For preprocessing of the data, the wavelet is hybridized with the models, leading to 144 different models. The results indicate that ELMs are capable of forecasting SHDI with high precision. The self-adaptive differential evolution ELM outperforms the other models and the wavelet has a highly positive effect on the model performance, especially in error reduction. In general, using ELMs in hydrological drought forecasting is promising and this model can feasibly be used for this purpose. | |
Language dc.language | en | |
Keywords dc.subject | K-nearest neighbor distance weighted (DWKNN) | |
Keywords dc.subject | pore pressure prediction | |
Keywords dc.subject | hybrid machine learning | |
Keywords dc.subject | feature selection | |
Keywords dc.subject | root mean squared error | |
Title dc.title | Data driven models to predict pore pressure using drilling and petrophysical data | |
Type dc.type | folyóiratcikk | |
Date Change dc.date.updated | 2023-02-14T13:32:30Z | |
Version dc.description.version | kiadói | |
dc.rights.accessRights | nyílt hozzáférésű | |
dc.description.notes | A publikáció a Nemzeti Közszolgálati Egyetem 2020. évi Tématerületi Kiválóság Program keretében, a Fenntartható biztonság és társadalmi környezet elnevezésű projekt támogatásával valósult meg, az Innovációs és Technológiai Minisztérium Nemzeti Kutatási, Fejlesztési és Innovációs Alapból nyújtott támogatásával, a Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal által kibocsátott támogatói okirat alapján. | |
Doi ID dc.identifier.doi | 10.1016/j.egyr.2022.04.073 | |
Discipline Discipline + dc.subject.discipline | Műszaki tudományok | |
dc.subject.sciencebranch | Anyagtudományok és technológiák | |
MTMT ID dc.identifier.mtmt | 32835921 | |
dc.identifier.journalTitle | Energy Reports | |
dc.identifier.journalVolume | 8 | |
Scope dc.format.page | 6551-6562 | |
Wos ID dc.identifier.wos | 000806042100002 | |
ID Scopus dc.identifier.scopus | 85130412228 | |
dc.identifier.journalAbbreviatedTitle | ENERGY REP | |
Release Date dc.description.issuedate | 2022 | |
Author institution dc.contributor.department | Információs Társadalom Kutatóintézet | |
Author institution dc.contributor.department | Menedzsment Tanszék | |
Author institution dc.contributor.department | Közszervezési és Infotechnológiai Tanszék | |
Author institution dc.contributor.department | Információs Társadalom Kutatóintézet | |
Author institution dc.contributor.department | Szociológiai Intézet | |
Author institution dc.contributor.department | Gazdaság- és Regionális Tudományi Doktori Iskola | |
Author institution dc.contributor.department | Államtudományi és Nemzetközi Tanulmányok Kar | |
Author institution dc.contributor.department | Kiberbiztonsági Kutatóintézet |