The effective management of water resources is essential to environmental stewardship and
sustainable development. Traditional approaches to water resource management (WRM) …

Over the past decade, the wide-scale adoption of artificial intelligence (AI) and machine
learning (ML) has transformed the landscape of scientific research and development, which …

With the significant increase in WQI applications worldwide and lack of specific application
guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that …

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 …

In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …

Applying artificial intelligence (AI), machine learning (ML), and big data to natural resource
management (NRM) is revolutionizing how natural resources are managed. To gain more …

Regular monitoring and assessment of water quality is essential to maintain the quality of
lake water. A commonly used method for assessing water quality is the Water Quality Index …

The growth in the volume of data generated, consumed, and stored, which is estimated to
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …

Abstract Machine learning (ML) and deep learning (DL) possess excellent advantages in
data analysis (eg, feature extraction, clustering, classification, regression, image recognition …

The aim of the work was to assess the usefulness of machine learning in predicting the
migration of pollutants from microplastics. The search for methods to reduce unnecessary …