Applications of hybrid wavelet–artificial intelligence models in hydrology: a review
Accurate and reliable water resources planning and management to ensure sustainable use
of watershed resources cannot be achieved without precise and reliable models …
of watershed resources cannot be achieved without precise and reliable models …
A comprehensive review of conventional, machine leaning, and deep learning models for groundwater level (GWL) forecasting
Groundwater level (GWL) refers to the depth of the water table or the level of water below the
Earth's surface in underground formations. It is an important factor in managing and …
Earth's surface in underground formations. It is an important factor in managing and …
Process‐guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control
Urban flood control is a crucial task, which commonly faces fast rising peak flows resulting
from urbanization. To mitigate future flood damages, it is imperative to construct an on-line …
from urbanization. To mitigate future flood damages, it is imperative to construct an on-line …
Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model
The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS
and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is …
and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is …
Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)
Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate
forecasting is essential in all water resources applications. In this study, artificial neural …
forecasting is essential in all water resources applications. In this study, artificial neural …
Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review
Since the middle of the twentieth century, artificial intelligence (AI) models have been used
widely in engineering and science problems. Water resource variable modeling and …
widely in engineering and science problems. Water resource variable modeling and …
Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques
The development of the stage-discharge relationship is a fundamental issue in hydrological
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …
Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?
Rainfall-runoff modeling is of great importance in hydrological sciences. Several different
models have been developed for runoff modeling in three main categories ie physically …
models have been developed for runoff modeling in three main categories ie physically …