With the rapid increase in the volume of data on the aquatic environment, machine learning
has become an important tool for data analysis, classification, and prediction. Unlike …

Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

Because of its robust autonomous learning and ability to address complex problems,
artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges …

Pipeline and pipe networks are the most environmentally sustainable and energy-efficient
means for transportation and distribution of fluids of high social and commercial value like …

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce
energy consumption and greenhouse gas emissions significantly. Machine learning (ML) …

Leakages are undesirable events in water distribution networks (WDNs) with mostly
unknown incident time making it extremely difficult to accurately label leaks in distribution …

Leak detection and location in water distribution systems (WDSs) is of utmost importance for
reducing water loss, which is, however, a major challenge for water utility companies. To this …

River flow modeling plays a crucial role in water resource management and ensuring its
sustainability. Therefore, in recent years, in addition to the prediction of hydrological …