Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …

The evaluation and management of climate change impacts on natural and human systems
required the adoption of a multi-risk perspective in which the effect of multiple stressors …

This study presents the development of new empirical prediction models to evaluate swell
pressure and unconfined compression strength of expansive soils (P s UCS-ES) using three …

Bio-composites have become the prime material selection for green concrete because of the
increasing awareness of environmental issues. Due to their highly heterogenous nature …

Steel-fiber-reinforced concrete (SFRC) has emerged as a viable and efficient substitute for
traditional concrete in the construction industry. By incorporating steel fibers into the …

The machine learning algorithm 'random forest'has been applied in many areas of water
resources research including discharge simulation. Due to low setup and operation cost …

Artificial neural network (ANN) model involves computations and mathematics, which
simulate the human–brain processes. Many of the recently achieved advancements are …

This research study utilizes four machine learning techniques, ie, Multi Expression
programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference …

Accurately predicting the Modulus of Resilience (MR) of subgrade soils, which exhibit non-
linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory …

Modeling of hydrological time series is essential for sustainable development and
management of lake water resources. This study aims to develop an efficient model for …