Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review
![Thumbnail](/xmlui/bitstream/handle/20.500.12498/5945/neuro.pdf.jpg?sequence=4&isAllowed=y)
Göster/ Aç
Tarih
2022-09-10Yazar
DEMİR, Vahdettin
YASEEN, Zaher Mundher
Üst veri
Tüm öğe kaydını gösterÖzet
Hydrological processes forecasting is an essential step for better water management and sustainability. Among several
hydrological processes, lake water level (LWL) forecasting is one of the significant processes within a particular catchment. The complexity of the LWL fluctuation is owing to the diversity of the influential parameters including climate,
hydrology and some other morphology. In this study, several versions of neurocomputing intelligence models are
developed for LWL fluctuation forecasting at five great lakes Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and
Lake Ontario, located at the north of USA. The applied models are including M5-Tree, multivariate adaptive regression
spline (MARS) and least square support vector regression (LSSVR). The models are developed using several input
combinations that are configured based on the correlated lags in addition to the periodicity of time series. The sequential
influence of the lakes order is considered in the modeling development. Also, cross-station modeling where lag time series
of upstream lakes are used to forecast downstream LWL. Results are assessed using several statistical metrics and graphical
visualization. Overall, the results indicated that the applied forecasting models efficient and trustworthy. The component of
the periodicity time series enhances the forecasting performance. Cross-station modeling revealed an optimistic modeling
strategy for learning transfer modeling of using information of nearby site.
Koleksiyonlar
Aşağıdaki lisans dosyası bu öğe ile ilişkilidir:
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
DSpace@Karatay by Karatay University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..