Читать книгу Selenium Contamination in Water - Группа авторов - Страница 18

AI and Deep learning methods have spread their capabilities in depicting contests for water‐sanitation amenities and research forums. Deep learning presents an outstanding substitute to countless studies in optimization (Dentel 1995). Compared to out‐of‐date machine learning algorithms, deep learning has a robust learning capability to efficiently utilize data sets for data mining and knowledge mining. The objective of this investigation is to assess the prevailing unconventional methods. This paper further explores the boundaries and predictions of deep learning.

Furthermore, a novel placement of a machine learning ensemble in the field of water distribution networks was studied by Camarinha‐Matos and Martinelli (1998). This was an innovative application to govern and implement water distribution networks by a supervision classification, a distributed information management framework for water quality monitoring and recreation.

An alternate implementation of ANN along with SVM (Haghiabi et al. 2018) investigates water quality prediction. These authors reach a valuable outcome from their research, that “tansig” and “RBF”, which are transfer and kernel functions, demonstrate significant performance compared to other functions. SVM proves to be the most accurate model compared to other machine learning algorithms.

Finally, it is time to end up the discussion by looking at the most important issue of all, i.e. cost. It is extremely important to give a keen thought to the cost issue for wastewater treatment. Machine learning has been uniquely deployed (Torregrossa et al. 2018) for efficient energy cost modeling for wastewater treatment plants. The researchers have innovatively proposed cost as a parameter to evaluate the performance of the system.

Thus, these technologies ensure that performance is accurately predicted and assists in ensuring that efforts are made to deal with issues in advance. Machine learning generates innovative visions that can be used as evidence for future research on scheduling the distribution of the water resources