scholarly journals Decision support systems (DSS) for wastewater treatment plants – A review of the state of the art

2019 ◽  
Vol 290 ◽  
pp. 121814 ◽  
Author(s):  
Giorgio Mannina ◽  
Taise Ferreira Rebouças ◽  
Alida Cosenza ◽  
Miquel Sànchez-Marrè ◽  
Karina Gibert
Keyword(s):  
Wastewater Treatment ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Wastewater Treatment Plants ◽  
State Of The Art ◽  
The State

scholarly journals Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

2021 ◽  
Vol 4 (3(112)) ◽  
pp. 43-55
Author(s):  
Areej Adnan Abed ◽  
Iurii Repilo ◽  
Ruslan Zhyvotovskyi ◽  
Andrii Shyshatskyi ◽  
Spartak Hohoniants ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
The State ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Artificial Neural ◽  
Intelligent Decision Support

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

scholarly journals Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

2017 ◽  
Vol 28 (6) ◽  
pp. 1107-1117 ◽  
Author(s):  
Stavros Sakellariou ◽  
Stergios Tampekis ◽  
Fani Samara ◽  
Athanassios Sfougaris ◽  
Olga Christopoulou
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Forest Fires ◽  
Support Systems ◽  
State Of The Art

Mathematical Models Generators of Decision Support Systems for Help in Case of Catastrophes

2011 ◽  
pp. 201-220 ◽  
Author(s):  
José G. R. Hernández ◽  
María J. G. García
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Private University ◽  
The State ◽  
The Moment ◽  
The Impact

Immediately after the catastrophes that affected Venezuela at the end of 1999, especially the flood of the State of Vargas, a group of investigators of a consultancy company and of a private university of Caracas Venezuela, started working in decisions support systems (DSS) that could be useful in the moment of a catastrophe, helping to minimize the impact of its three principal stages: Pre-catastrophe, Impact and Post-catastrophe. Clearly, for the development of these DSS, it was indispensable to construct mathematical models to support them. The objective of this chapter is to disclose this experience by presenting some of these mathematical models and its conversion in DSS that supports decision making in the case of catastrophes.

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