With the development of 5G and Internet of Things (IoT), the era of big data‐driven product
design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by …

Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

Floods are one of the most devastating natural hazards, causing several deaths and
conspicuous damages all over the world. In this work, we explore the applicability of the …

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …

Plastic pollution in water bodies is an unresolved environmental issue that damages all
aquatic environments, and causes economic and health problems. Accurate detection of …

Machine learning (also called data-driven) methods have become popular in modeling flood
inundations across river basins. Among data-driven methods, traditional machine learning …

Groundwater is a vital source of freshwater, supporting the livelihood of over two billion
people worldwide. The quantitative assessment of groundwater resources is critical for …

Deep Learning (DL) methods have gained significant recognition in hydrology and water
resources applications in recent years. Beginning with a discussion on fundamental …