User profiles for Gebre Gelete
Gebre GeleteNear East University Verified email at neu.edu.tr Cited by 317 |
Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling
G Gelete - Earth Science Informatics, 2023 - Springer
The main aim of this study was to develop hybrid machine learning (ML)-based ensemble
modeling of the rainfall-runoff process in the Katar catchment, Ethiopia. This study used four …
modeling of the rainfall-runoff process in the Katar catchment, Ethiopia. This study used four …
Estimation of suspended sediment load using artificial intelligence‐based ensemble model
Suspended sediment modeling is an important subject for decision‐makers at the catchment
level. Accurate and reliable modeling of suspended sediment load (SSL) is important for …
level. Accurate and reliable modeling of suspended sediment load (SSL) is important for …
Impact of climate change on the hydrology of Blue Nile basin, Ethiopia: a review
Climate change alters the spacial and temporal availability of water resources by affecting the
hydrologic cycle. The main objective of this paper is to review the climate change effect on …
hydrologic cycle. The main objective of this paper is to review the climate change effect on …
Hybrid extreme gradient boosting and nonlinear ensemble models for suspended sediment load prediction in an agricultural catchment
G Gelete - Water Resources Management, 2023 - Springer
In this study, four individual models namely Hammerstein-Weiner (HW), Extreme Learning
Machine (ELM), Long Short-Term Memory (LSTM) and Least Square Support Vector Machine …
Machine (ELM), Long Short-Term Memory (LSTM) and Least Square Support Vector Machine …
Ensemble physically based semi-distributed models for the rainfall-runoff process modeling in the data-scarce Katar catchment, Ethiopia
This study evaluates the performance of the soil and water assessment tool (SWAT), the
hydrologiska byråns vattenbalansavdelning (HBV) and the hydrologic engineering center-…
hydrologiska byråns vattenbalansavdelning (HBV) and the hydrologic engineering center-…
Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation
A reliable pan evaporation (E pan ) estimation over a daily scale is vital for sustainable
water and agriculture management, especially for designing water use allocations, irrigation …
water and agriculture management, especially for designing water use allocations, irrigation …
Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling
This study evaluates the performance of the Hydrologic Engineering Center-Hydrologic
Modelling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil and …
Modelling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil and …
Hybrid emotional neural networks and novel multi-model stacking algorithms for multi-lake water level fluctuation modeling
Lakes play a crucial role in the water cycle, and accurately modeling their water level fluctuation
is vital for managing water resources, ecosystems, and flood control. The current study …
is vital for managing water resources, ecosystems, and flood control. The current study …
Relative humidity quantification using interpretable machine learning based-stacking approach: representative case study in Ethiopia
Relative humidity (RH) is among the water cycle’s important parameters and stochastic
processes. Accurate estimation of RH is essential for numerous water resources management …
processes. Accurate estimation of RH is essential for numerous water resources management …
Ensemble of artificial intelligence and physically based models for rainfall–runoff modeling in the upper Blue Nile Basin
This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS),
feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic …
feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic …