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

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 …

This chapter introduces a framework for combining scientific knowledge of physics-based
models with neural networks to advance scientific discovery. It explains termed physics …

Physics-based models are often used to study engineering and environmental systems. The
ability to model these systems is the key to achieving our future environmental sustainability …

This paper proposes a physics-guided recurrent neural network model (PGRNN) that
combines RNNs and physics-based models to leverage their complementary strengths and …

Harmful algal blooms (HABs) can have dire repercussions on aquatic wildlife and human
health, and may negatively affect recreational uses, aesthetics, taste, and odor in drinking …

To simultaneously address the rising need of expressing uncertainties in deep learning
models along with producing model outputs which are consistent with the known scientific …

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 …

The algal community structure is vital for aquatic management. However, the complicated
environmental and biological processes make modeling challenging. To cope with this …

Blooms of blue-green algae (BGA) threaten drinking water safety and ecosystems
worldwide. Understanding mechanisms and driving factors that promote BGA proliferation is …