Environmental hazards such as floods significantly frustrate the functionality of built assets.
In addressing flood-induced challenges, data usage has become important. Despite existing …

While some robust artificial intelligence (AI) techniques such as Gene-Expression
Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) …

Accurate determination of discharge capacity in radial gates as commonly designed check
structures is of great importance in hydraulic engineering research. The discharge …

This paper aims to obtain a relation for estimating the median size of bed sediment, d 50, at
the bends of meandering rivers based on real data. To achieve such a purpose, field data …

The present study represents the first use of kernel-based models to predict discharge
coefficient (Cd) for two distinct types of cylindrical weirs, featuring vertical support and a 30 …

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have
been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam …

Phase space reconstruction is crucial for predicting chaotic hydrological time series.
However, traditional multivariate phase space reconstruction methods, such as high …

Monthly runoff forecasting plays a critically supportive role in water resources planning and
management. Various signal decomposition techniques have been widely applied to …

Neural networks have become widely employed in streamflow forecasting due to their ability
to capture complex hydrological processes and provide accurate predictions. In this study …

For the determination of global stability after long term advanced monitoring, artificial
intelligence have been used for the data analysis of water level and displacements of …