Including Expert Knowledge in Finite Element Models by Means of Fuzzy Based Parameter Estimation

Abstract: In this paper we present a novel approach for modelling spatial distributed biochemical and environmental processes like the growth of plants and the related biochemical reactions. One of the main challenges for modelling of spatial distributed phenomena is the estimation of the model parameters. The physical phenomena like flow and mass transport can be described by PDEs of fluid dynamics, but for effects like growth rates often no analytic models are available. However, in many cases experts have knowledge about the system behaviour that can be formulated by a set of if-then-rules. As this kind of knowledge can easily be handled by so-called Fuzzy models we propose the coupling of FEM models with such Fuzzy models. By this means one or more parameters of the classical PDEs are estimated by Fuzzy Models. Besides the natural inclusion of expert knowledge a second benefit of this approach consists in the fact that Fuzzy Models can describe even very nonlinear phenomena. The proposed approach is applied for modelling the growth of algae of Orbetello lake in Italy

A review on integration of artificial intelligence into water quality modelling

2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A review on the integration of artificial intelligence into coastal modeling

Author name used in this publication: Kwokwing Chau2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Prediction of Spatial-Temporal Distribution of Algal Metabolites in Eagle Creek Reservoir, Indianapolis, IN

Indiana University-Purdue University Indianapolis (IUPUI)In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased Fuzzy Inference System Models (ANFIS) were developed and implemented to determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for understanding algae dynamics and developing prediction methods for algal taste and odor release events. In this research the methodology for prediction of 2-MIB and geosmin production was explored. The approach incorporated a combination of numerical and heuristic modeling to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s variable data measured at monitoring stations and consisting of chemical/physical and biological parameters with the addition of calculated mixing conditions within the reservoir were used to train and validate the models. The Adaptive – Network based Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of multiple model constraints. The predictions followed the generally observed trends of algal metabolites during the three seasons over three years (2008-2010). The randomly selected data pairs for geosmin for validation achieved coefficient of determination of 0.78, while 2-MIB validation was not accepted due to large differences between two observations and their model prediction. Although, these ANFIS results were accepted, the further application of the ANFIS model coupled with the numerical models to predict spatio-temporal distribution of metabolites showed serious limitations, due to numerical model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp. biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave negative biovolumes, which were assumed to represent zero values observed at the station. The taste and odor metabolite, geosmin, was under-predicted as the predicted v concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB model did not validate during EFDC to ANFIS model evaluation. The proposed approach and developed methodology could be used for future applications if the limitations are appropriately addressed

Development of a Conceptual, Mathematical and Model of System Dynamics for Landfill Water Treatment

Leachate is a major problem in landfills due to the type and amount of pollutants. In Croatia, the usual way of handling leachate is recirculation back to the landfill body. However, this method poses a danger of their leakage into the environment, especially during periods of increased precipitation. Leachate is heavily polluted with organic matter, and its spillage into the environment can cause environmental incident. This paper presents a model for efficient treatment of landfill water contaminated with organic matter, based on the operating parameters of the actual water treatment system. The aim of this scientific research is to develop a model for landfill water treatment and to design a methodology suitable for significant patterns of organic matter pollution behaviour. The developed conceptual model is a computer-based model that uses randomly selected values from the theoretical probability distribution of the applied variables. The mathematical model is based on a system of differential equations solved by the Runge-Kutta method. To validate the model, a nonparametric test was applied, given that the distributions are asymmetric non-Gaussian distributions. The methodology proposed in this paper is based on simulation modelling as a useful method in environmental protection. The developed and validated model has proven that landfill water can be effectively and economically purified. Simulation modelling and environmental informatics can effectively contribute to solving environmental problems on the computer without unnecessary risk to the environment