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 …

Current research studies offer an investigation of machine learning methods used for
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …

Forecasting accurately suspended sediment load (SSL) in the basin is one of the most
critical issues for river engineering, environment, and water resources management which …

In the Zeroud basin, a diverse array of methodologies were employed to assess, simulate,
and predict the quality of groundwater intended for irrigation. These methodologies included …

Prediction of suspended sediment load (SSL) in streams is significant in hydrological
modeling and water resources engineering. Development of a consistent and accurate …

Abstract Predicting the Earth Skin Temperature (TS) using artificial intelligence (AI) has the
potential to offer valuable insights into environmental changes and their impacts. TS has …

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM)
and long short-term memory (LSTM), were used along with adaptive boosting and general …

Drought is a hazardous natural disaster that can negatively affect the environment, water
resources, agriculture, and the economy. Precise drought forecasting and trend assessment …

The current study integrates remote sensing, machine learning, and physicochemical
parameters to detect hydrodynamic conditions and groundwater quality deterioration in non …

A machine learning-based approach is applied to simulate and forecast forest fires in the
Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and …