River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …

Agriculture plays an important role in sustaining all human activities. Major challenges such
as overpopulation, competition for resources poses a threat to the food security of the planet …

Given the severe global warming situation, it is very important to explore the factors
influencing carbon emission intensity and accurately analyze the trends in the development …

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 …

Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

In this study, monthly solar radiation (SR) estimation was performed using five different
machine learning-based approaches. The models used are support vector machine …

In recent years, applications of convolutional neural networks (CNNs) to runoff prediction
have received some attention due to their excellent feature extraction capabilities. However …

This article explores the suitability of a long short-term memory recurrent neural network
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …

Monthly streamflow prediction is very important for many hydrological applications in
providing information for optimal use of water resources. In this study, the prediction …

In this research, a machine learning model namely extreme learning machine (ELM) is
proposed to predict the compressive strength of foamed concrete. The potential of the ELM …