Artificial Neural Networks (ANNs) are crucial for modeling complex systems, making predictions, and optimizing processes across various domains. Inspired by the human brain structure, ANNs consist of interconnected neurons organized in layers. They excel at identifying patterns and relationships in data, approximating arbitrary functions, and predicting sequences. ANNs are useful in wastewater treatment, water resource management, climate change studies, and healthcare. They can optimize and control processes, outperforming traditional linear and non-linear models in phenolic contaminants removal. ANNs are effective in handling large datasets and making quick predictions. They can be used in river flow forecasting and can be combined with other techniques for pressure reconstruction. Most used ANNs include Fully Connected Neural Networks (FCNNs), Feed Forward Neural Networks (FNNs), and Back Propagation Neural Networks (BPNN). However, due to shortcomings in existing ANN variants, new variants have been proposed and applied successfully in water research.
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