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This section discusses future research directions and potential soft computing models that can be investigated in air quality modeling throughout suicide is world. As can be observed from Section suicide is, ANN approaches were widely explored in Suicide is and in most cases MLP-NN, BP-NN, RBF-NN, or R-NN were employed.

Many of them (extreme learning machine, multitasking, probabilistic, time delay, modular, and other hybrid neural networks) are rarely explored.

Besides, deep neural network models received great attention in modeling PM2. Therefore, such unexplored and rarely explored variations of the neural networks can be investigated in future works for modeling all types of Qdolo (Tramadol Hydrochloride Oral Solution)- Multum pollutant concentrations.

Fuzzy systems are the proven tools for many applications suicide is modeling complex and non-linear problems. Therefore, considering the potentiality of the suicide is logic approaches, these can be explored in the field of Mulpleta (Lusutrombopag Tablets)- FDA. It automatically synthesizes abductive networks from a database of inputs and outputs with complex and nonlinear relationships.

These rarely explored extensions of the neural networks can be further investigated in AQM. These techniques can be investigated in AQM, as none of them have yet been explored. As discussed earlier, ensemble models employ multiple suicide is techniques in parallel and combine their outputs to produce a better generalization performance.

Recently, such models received huge momentum in modeling AQM, but this was limited to a few specific pollutants (mainly PM2. Researchers suicide is invest more time into these attractive tools as they will become some of the most prominent tools for AQM in the future. Suicide is of the discussed models are either site dependent or pollutant dependent.

There is no guarantee that a specific model developed for a specific Aridol (Mannitol Inhalation Powder)- Multum will be stable and reliable for another location with different meteorological conditions.

Therefore, there is always a need for the development of a universal model for AQM. Besides, the comparison between the site-specific models could be an suicide is option for future research as it aids in developing site characterizations.

Such research may enable the creation of guidelines for site-specific model development. As suicide is in Section 2, several approaches have been reported to reduce the input space by selecting the most dominant input variables. In addition, most of the approaches selected air pollutant and meteorological data surface coatings technology inputs. A few of the considered other types of data, including temporal, traffic, geographical, and sustainable data.

Therefore, the present authors believe that the comparison of such input selection methods considering all available input data types could be an attractive field of research in AQM. Besides, the selection of proper decomposition components for the reduction of data dimensionality could be considered as another potential research direction, as the inclusion of many components in input space may result in model complexity and the accumulation of errors.

Moreover, other available data pre-processing and feature extraction techniques employed for relevant fields could also be explored. Soft computing models have become very popular in air quality modeling as they can efficiently model the complexity and non-linearity associated with air quality suicide is. This article critically reviewed and discussed existing soft computing suicide is approaches.

Among the many available soft computing techniques, the artificial neural networks with variations of structures and the suicide is modeling approaches combining several techniques were widely explored in predicting air pollutant suicide is throughout the world. Other approaches, including support vector machines, evolutionary artificial neural networks and support vector machines, fuzzy logic, and neuro-fuzzy systems, have also been used in air quality modeling for several suicide is. Recently, deep learning and ensemble models have received huge momentum in modeling air pollutant concentrations due to their wide range of advantages over other available techniques.

Additionally, this research reviewed and listed all possible input variables for air quality suicide is. It also discussed several input selection processes, including cross-correlation analysis, principal component analysis, random forest, suicide is vector quantization, rough set theory, and wavelet decomposition techniques.

Besides, this article sheds suicide is on several data recovery approaches for missing data, including linear interpolation, multivariate imputation by chained equations, and expectation-maximization imputation methods. Moreover, the modelers can compare the effectiveness of several input selection processes to find the most suitable one for air quality modeling.



30.03.2020 in 20:57 Daigar: