Evaluating the Hydraulic Effects of the Flow through and over the Submerged Biofilter Installed in Polluted Streams.

The problem of shortage in freshwater resources in many countries around the world has led to the use of unconventional water resources such as treated wastewater and agricultural drains water to bridge the gap between the demand and supply. However, the open nature of most agricultural drains and the spread of population cumulation around them has made them vulnerable to many organic and inorganic pollutants. One of the artificial methods used to enhance the self-purification process in polluted streams is submerged biofilters (SB).

Improving the Hydraulic Effects Resulting from the Use of a Submerged Biofiter to Enhance Water Quality in Polluted Streams.

Water scarcity is one of the most serious problems facing many countries. In addition, water pollution could lose more water. A submerged biofilter (SB) is used to enhance the self-purification process in polluted streams.

Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter.

Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00-28.