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

The increasing occurrence of harmful algae blooms globally poses significant challenges to
water management. In water treatment utilities, coagulation is the first treatment process of …

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk
for water authorities globally due to their toxicity and economic impacts. Anticipating bloom …

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing
aquatic environments, but it is still a challenging topic to accurately understand and predict …

With the steady growth in the data set on the COVID‐19 pandemic, empirical works that
employ novel and yet appropriate statistical techniques to corroborate previous findings of …

Many countries have attempted to monitor and predict harmful algal blooms to mitigate
related problems and establish management practices. The current alert system-based …

Water quality forecasting can provide useful information for public health protection and
support water resources management. In order to forecast water quality more accurately, this …

Early warning systems for harmful cyanobacterial blooms (HCBs) that enable precautional
control measures within water bodies and in water works are largely based on inferential …

Harmful algal blooms (HABs) are among the most severe ecological marine problems
worldwide. Under favorable climate and oceanographic conditions, toxin-producing …

The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents
a promising solution to algal bloom forecasting. However, the discontinuous and non …