The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential.

Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven by the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, particularly those associated with the Internet of Things (IoT).

Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights.

Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the inter-dependence among the parameters.

Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques.

Soil salinization is a widespread phenomenon leading to land degradation, particularly in regions with brackish inland aquaculture ponds. However, because of the high geographical and temporal fluctuation, monitoring vast areas provides substantial challenges. This study uses remote sensing data and machine learning techniques to predict soil salinity.