Applying and optimizing case-based reasoning for wastewater treatment systems

Jürgen Wiese, Armin Stahl, Joachim Hansen
AI Communications. Special Issue: Binding Environmental Sciences and AI volume 18 number 4, Pages 269-279, IOS Press, 2005

Abstract:

For the last years, artificial intelligence (AI) approaches have become useful tools in environmental engineering. Here, one relevant application area is the optimization of wastewater treatment plants (WWTP). In this paper, we present several examples for real-time Control (RTC) tasks and decision support systems (DSS) for wastewater treatment (WWT), specifically based on case-based reasoning (CBR). Moreover, we present an approach for optimizing the prediction accuracy of these systems. The idea of this approach is to employ knowledge-intensive similarity measures instead of simple distance metrics. In order to facilitate the modeling of these measures resulting in lower deployment costs of the CBR systems, we propose a novel machine learning technique.

Files:

  AIC2005_Wiese_Stahl_Hansen.pdf

BibTex:

@article{ WIES2005,
	Title = {Applying and optimizing case-based reasoning for wastewater treatment systems},
	Author = {Jürgen Wiese and Armin Stahl and Joachim Hansen},
	Year = {2005},
	Publisher = {IOS Press},
	Publisher = {18},
	Pages = {269-279},
	Journal = {AI Communications. Special Issue: Binding Environmental Sciences and AI},
	Number = {4}
}

     
Last modified:: 30.08.2016