Hybrid expert system DELAQUA — a toolkit for water quality control of lakes and reservoirs

Ecological Modelling, 71 (1994) 17-36

17

Elsevier Science B.V., Amsterdam

Hybrid expert system D E L A Q U A - a toolkit for water quality control of lakes and reservoirs Frieder Recknagel a, Thomas Petzoldt b, Olaf Jaeke b and Falk Krusche b University of Adelaide, Dept. of Environmental Science, Roseworthy Campus, Roseworthy, S.A. 5371, Australia b Dresden University of Technology, Institute of Hydrobiology, Mommsenstr. 13, 8027 Dresden, Germany (Received 21 September 1992; accepted 15 January 1993)

ABSTRACT Recknagel, F., Petzoldt, Th., Jaeke, O. and Krusche, F., 1994. Hybrid expert system D E L A Q U A a toolkit for water quality control of lakes and reservoirs. Ecol. Modelling, 71: 17-36. -

An expert system to be used for assisting in control of water quality of lakes and reservoirs is presented. Its character is hybrid with regard to structure and function. Shell N E X P E R T OBJECT serves as platform for the implementation, while the hypertext system ToolBook has been used to develop the user interface which consists of three levels: (1) an object-oriented geographic interface with maps of the country, region and catchment area of waters under consideration, (2) an intelligent front-end to support the handling of the simulation model SALMO and the historical data base HIDA, and (3) a user interface to consult knowledge bases of three water quality problems (eutrophication, algal blooms and pathogens). The deterministic model SALMO, empirical models of the Vollenweider-type and a fuzzy model are accessible from the knowledge bases for eutrophication and algal blooms.

1. I N T R O D U C T I O N

Environmental systems have proven to be one of the most challenging fields for the application and verification of innovations in computer sciences and systems analysis. High-speed computers make it possible to apply the concept of cellular automata for qualitative approaches in modeling and simulation of ecosystems (Camara et al., 1990). By the use of Correspondence to: F. Recknagel, University of Adelaide, Dept. of Environmental Science, Roseworthy Campus, Roseworthy, S.A. 5371, Australia. Fax: + 61-8-30-37956. 0304-3800/94/$07.00 © 1994 - Elsevier Science B.V. All rights reserved SSDI 0 3 0 4 - 3 8 0 0 ( 9 3 ) E 0 0 3 5 - 2

18

F. RECKNAGEL ET AL.

multiprocessor computers, the parallel simulation of ecological food webs can be performed (Haefner, 1991) and the potentials of neural networks can be utilized for ecological modeling (Ball and Gimblett, 1992). The increasing availability of declarative languages and expert system shells stimulates the applications of expert systems in the field of environmental engineering (Lai and Berthouex, 1991; Nix and Collins, 1991). Objectoriented languages pave the way for more holistic approaches in modeling and simulation of ecosystems (Sequeira et al., 1991). Even though there is a need for different methods and approaches to make the state and behaviour of environmental systems more transparent and predictable, we must take care for tools which help to integrate well-tried traditional methods with the potentials of new methods. Such tools can be characterized by following functional requirements: rule- and object-oriented programming, different inference mechanisms, prefabricated user interface, external interfaces for most common languages and data bases, support in storing, retrieving and visualization of data bases composed of any type of information, and assistance in handling of numerical simulation models. Since 1991 we have been concerned with the construction of the hybrid expert system D E L A Q U A by using N E X P E R T OBJECT (Neuron Data, Inc., Palo Alto, CA), a developing environment for expert systems, and ToolBook (Asymetrix Corporation, Washington), an environment for object-oriented programming with OpenScript. D E L A Q U A (Deep Expert system LAke water QUAlity) consists of numerical models, a fuzzy model, knowledge and data bases for assisting in the assessment, control and management of the water quality of lakes and reservoirs. The previous version of D E L A Q U A (Recknagel et al., 1991) disposed of a user interface to access the different modules written in FORTRAN, P R O L O G and dBASE in an optional but separate manner. Now, by taking advantage of the potentials of N E X P E R T OBJECT and ToolBook, an integrated, network-like communication between the modules is performed. Results so far show that N E X P E R T OBJECT and ToolBook are convenient tools for software integration and fulfill the functional requirements as mentioned above. 2. ARCHITECTURE AND FUNCTIONING OF DELAQUA The modules of D E L A Q U A are arranged hierarchically (Fig. 1) where the different levels can be made accessible by one more of the following user-selected interfaces: (1) an object-oriented geographic user interface, which makes maps available of the country, region and catchment area of the water under consideration, (2) an intelligent front-end which supports

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Fig. l. Architecture of the hybrid expert system DELAOUA.

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WATER QUALITY DATA SIMULATION MODEL BASE HIDA SALMO

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20

F. R E C K N A G E L E T AL.

data base investigations and simulation runs for the water under consideration, (3) a user interface which makes it possible to consult three knowledge bases referred to the problem domains "eutrophication", "algal blooms" and " p a t h o g e n germs".

2.1. Object-oriented geographic user interface The geographical user interface, which has been programmed in OpenScript, allows access to the hierarchically arranged national, regional and water-specific maps by mouse click. Fig. 2a illustrates the outline map of Germany, which serves as m e n u area for selecting federal states. For example, after clicking by mouse at the federal state of Saxony, the skeleton map of Saxony appears on the screen, with rivers, lakes and reservoirs marked (see Fig. 2b). Now it is possible to pick out the water under consideration in the same manner. Fig. 2c shows the skeleton map of the Bautzen reservoir and its catchment area. Additionally some morphometric data of the water and the m e n u to start the modules of the expert system are included. At present a 3D-representation of the water basins is prepared by which a spatial visualization of water quality data from the data base H I D A and from the model SALMO can be performed. 2.2. Intelligent front-end for water quality investigations and simulations The intelligent front-end serves for user-friendly water quality investigations by means of the data base H I D A and the simulation model SALMO. In an interactive dialogue the selection, graphic representation, retro- or prospective interpretation of time series of water quality criteria are supported. Annual trajectories can be represented graphically in standard formats, which help in the comparison of historical and synthetic data of one water and year (see Fig. 5). The structure of the data base HIDA, which has been written in dBASE IV, is represented in Fig. 3. The head data file consists of mean characteristics of the water and year under consideration, while the water quality data file includes the measured data of the water on a sampling date. By the specification of selected criteria of the head data file (e.g. mean depth, mean residence time, external phosphorus load) H I D A supports the search for similar waters and to draw conclusions by analogy.

Fig. 2. Hierarchical geographic user interface of DELAQUA. (a) Map of Germany as example of the national level. (b) Map of Saxony as example of the regional level. (c) Bautzen reservoir as example of the water level.

EXPERT

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21

DELAQUA

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DATA

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Fig. 3. Structure of the data base H I D A .

The simulation model SALMO (Benndorf, 1979; Recknagel, 1980) has been written in F O R T R A N and allows users to predict the transient behaviour of lakes and reservoirs under changing conditions by the hand of state variables for phytoplankton, zooplankton, detritus, orthophosphate, dissolved inorganic nitrogen and oxygen. SALMO is applicable for nonshallow lakes and reservoirs (ZMIX > 5 m), provided that data of incident solar radiation, water temperature, water quantity balance and nutrient load can be made available as 10-day mean values over the year under consideration. By holding the values of 124 parameters constant, the model has been applied to 24 different lakes and reservoirs and has proven to be valid up to a certain degree (see Recknagel, 1989). In Fig. 4 the simplified structure of the ecological-geochemical lake model by Recknagel et al. (1993) is represented consisting of SALMO and a comprehensive sediment model. Fig. 5 shows a standard format of the front-end for the graphic representation of water quality data from HIDA and SALMO, taking the Bautzen

EXPERT SYSTEMDELAQUA

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25

EXPERT SYSTEM D E L A Q U A

reservoir in 1978 as an example. Fig. 5 includes trajectories of the orthophosphate concentrations (left) and of corresponding phytoplankton biomasses (right). The content of this figure can be interpreted as follows: (1) the trajectories reveal a sufficient qualitative agreement between measurements and reference simulations for 1978, and (2) the results of the scenario analysis confirm practical experience gained, e.g. at the Wahnbach reservoir (Bernhardt and Clasen, 1981), that phosphorus elimination in water intake makes oligotrophication of hypereutrophic waters possible. Vollenwelder Plot

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Fig. 6. Estimation of the trophic state of the Bautzen reservoir according to the Vollenweider-modei. (a) Numerical exact calculation. (b) Probabilistic calculation.

26

F. RECKNAGEL ET AL.

KNOWLEDGE

BASE :MODULE

CLASSIFICATION

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Fig. 7. Rule network of the module for raw water classification according to TGL 27885/01,

EXPERT

SYSTEM

27

DELAQUA

2.3. User interface to consult knowledge bases The expert system D E L A Q U A has been conceived for decision support in three problem domains of water quality control: eutrophication, algal blooms and infection of waters by pathogens. For each of these initial domains, knowledge bases are available. At present they are restructured and improved in an object-oriented manner by means of N E X P E R T OBJECT to connect rules and facts with numerical and fuzzy models like a

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.

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Fig. 8. Membership functions of fuzzy variables. (a) Season of the year. (b) Trophic

preference of selected algal groups.

28

F. R E C K N A G E L E T AL.

network. For example, an ad hoc scenario analysis can be started by a corresponding rule of the knowledge base where the numerical model is handled like a procedure. Likewise rules and facts, which are handled as attributes of referred objects, can be consulted for the interpretation of a simulated scenario. At the same time numerical results of scenario analyses are used to actualize attributes of referenced objects.

2.3.1. Problem domain eutrophication Eutrophication can be defined as the process of enrichment of waters with plant nutrients which causes raw water quality loads, such as high primary production, low oxygen concentrations and increased concentrations of hydrogen sulphide, carbon dioxide, dissolved iron and manganese in the hypolimnion (Uhlmann, 1979). For the prognosis of the trophic state of a water body, the classical steady state models of Vollenweider (1976) can be applied. Using few input data such as phosphorus load, mean depth and mean residence time of the water the trophic state can be estimated and diagrammed in a retro- and prospective manner. This may be done by the module "empirical models" written in OpenScript which permits the use of numerical exact (see Fig. 6a) or probabilistic approaches (see Fig. 6b).

Testresults of Fuzzy Prediction

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Astenonella

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Blue Greens FlagelJales

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Fig. 9. Comparison of predicted (dotted) and observed (hatched) possibilities of occurrence of specific algal blooms in the Saidenbach reservoir.

29

EXPERT SYSTEM DELAQUA

A diagnosis of the trophic state of a water body is possible by means of the knowledge base "classification of raw water quality" written in PROLOG. It consists of the extensive rule-network of a German water classification standard (TGL 27885/01), which considers up to 90 water quality criteria, and the rules of the trophic state index after Walker (1979). By weighting of the criteria and criterion complexes considered by the classification standard insights can be given which influences are dominating in the calculated trophic state. On this basis recommendations can be derived for suitable control measures, not only in a therapeutic but also in a prophylactic manner. In Fig. 7 the rule network of this knowledge base is roughly represented. Therapeutic recommendations (Fig. 7) are restricted to measures in water works. Eutrophication control by in-lake measures and measures in the catchment area can also be prescribed by consultation of the catalogues T2 and T4 of the knowledge base "control of algal blooms" (Fig. 12). These catalogues are also preliminary and must be completed by additional control measures recommended by experts in this field. Convenient medium- or long-term policies for eutrophication control in the sense of prophylaxis can be found by scenario analyses by means of SALMO. With regard to scenario analyses, policies such as artificial destratification and aeration, diminution of phosphorus and nitrogen load, artificial denitrification, artificial feeding of zooplankton, manipulation of

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Fig. 10. User menu of the fuzzy model for predicting algal blooms.

30

F. R E C K N A G E L

ET AL.

food webs, and co-precipitation effects by calcite precipitation can be explored. 2.3.2. Problem domain algal blooms Algal blooms are characterized by proliferation of one or several species in huge numbers where one of the abundance criteria (number of cells, biomass, chlorophyll-a, dry weight of seston) is violating a stipulated limit value. A m o n g the harmful consequences of algal blooms which may impede the supply of drinking water are taste, odour and color in the raw water, clogging of filters and incrustation of pipes in water works. Being able to predict specific algal blooms in water bodies can be very useful for efficient preventive or operational control of such events. Such a predictive capability is n e e d e d in a short-term manner and has to take into account causal complexity, seasonal succession and natural stochasticity of algal blooms. Until now, determinstic models have failed to accomplish this in an exact numerical way. One reason may be that knowledge on algal blooms is mainly uncertain, vague and ambiguous, that is, inexact. Therefore an appropriate alternative modeling approach may be the fuzzy logic. As defined by Zadeh (1989), fuzzy logic is the logic underlying modes of reasoning which are approximate rather than exact and can be characterized as follows: - exact reasoning is viewed as a limiting case of approximate reasoning; - everything is a matter of degree; - any logic system can be fuzzified;

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Fig. 11. Pictorial information for the diagnosis of Uroglena volvox.

I

EXPERT

SYSTEM

KNOWLEDGE

31

DELAOUA

BASE:MODULE

CONTROL

Symptoms

OF

ALGAL

BLOOMS

(CAB]

Thief ~ p y c a t a l o g s

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Alternltive technologies l~°m$

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sIS:

Algae specific

LT'%n'I I

in

Sym0t0ms

ter$

-

Strong

-

Str~g

o~yg~

or

¢otorak~

turbzdity

S

o1

d~line of in hy~li~i~

of ir~ and ~nganele if oxygen nae r the bott~ am~nts <- 4 ~/l

-

~iuti~

-

Iden tificati~ of do.inating aIqae species by microscope

af filtrati~ - 60 Z

-

Shortening time by 50

-

ImDairmeN~t o f by alqa~b~rn

-

Diminishing of cleane~ ~ a t e r by s m e l l , flavour, Colour and toxines

-

Incrustation

-

Incomplete

-

Reduc~

~ater

flocculatio~ substances

ot'

pipes

desinfection

plankton

re.oval

AJternat ire t e c h n o l o g i e s for wite¢ outlet

-- C h a n g e o f ~a~ . a t e r -

Blend

~Lth

level

Algae sDecif;c t r o l measures

for

of ra~ ~ater f.esh~ater

-- U s e o f a l t e r n a t i v e ~eserv~irs

-

-

Aeration

of

hypOt~ion

-

Aeration

of

--ate? body

-

Chemical aeratian d e n i t r i f Ication

-

Artificial

-

~ttet

Outlet of algae-rich water lamella into • torr~t

-

LimitatiQn

by

Ra~ ~ a t e r t r e a t m e n t C a ( O H ) 2 a n d KMnO4

-- F i l t r a t i o n s e e vi

by

-- ~ t x n O m y C e t e n -

A~terionella

-

Melasi~a

formosa

miCro-

-

Slo~

-

Fast filtration, flocculat~on and filtration

sang

by

filtration

-

Step~anodiscus

-

OsczliatorLa

rubescen~

-

Bscillatarta

agardh~

-

Anabaena flos--aquae

destratification

of

light

Chemical pho~ph=rus Drecipitation

-

-Utilizati=n Qf n a t u r a l pt~ssphorus precipitati~

Fast fiLtratian, f locculation and filtration; add£tion of AL2(504)3

-- A p h a n i z = m e n o n f l o s - a q ~ -

-

-

~ddition of AI2ISO41S and a c t i v e c~r~on Filtration by active carbon

Microcyst~s

-- S y n u r a u v e l l a -

Uroglena

-- O ~ n o b r y o n -

Ozonation -- u l t r a - - a n d nanoplankt~c

Fig. 12. Rule network of the module for control of algal blooms.

algae

32

F. R E C K N A G E L E T AL.

- knowledge is interpreted as a collection of elastic or, equivalently, fuzzy constraints on a collection of variables; inference is viewed as a process of propagation of elastic constraints.

-

By applying fuzzy logic to the modeling of algal blooms we will be able to consider uncertainties, vagueness and ambiguities by using fuzzy and linguistic variables, to which empirical membership functions can be allocated. We started to construct a fuzzy model for the prediction of algal blooms by means of the representative data base over 15 years and the comprehensive knowledge (cf. Horn and Horn, 1990) on the Saidenbach reservoir (see Fig. 2). Additionally relevant literature (e.g., Reynolds, 1984; Sommer et al., 1986) was utilized. The following steps were taken: (1) definition of characteristics by which a phytoplankton peak may be detected as an algal bloom, (2) investigation of the time series of the data base for algal blooms with respect to the characteristics thus defined, (3) investigation of conditions by which an algal bloom may be caused and formulation of rules by means of selected fuzzy variables, and (4) definition of empirical membership functions for the fuzzy variables. In its present version, the model considers seven functional algae groups by use of fuzzy variables for season of the year, stratification, trophic state and content of silicate in the water. In Fig. 8 the membership functions of the fuzzy variables season of the year and trophic preference of selected algae species are represented. The first results of the fuzzy model (Fig. 9) showed significant differences between observed and predicted possibilities of specific algal blooms in the months May and June. The 100% possibility means that in 15 of 15 observed years the specific algal group has formed a peak. The fuzzy model was written in N E X P E R T OBJECT where the user menu (see Fig. 10) was designed with ToolBook's OpenScript. At present the data base is investigated for further rules such as influences of zooplankton composition on specific algal blooms. The next step will be to extract rules from the data with neural networks and to combine these rules with the fuzzy model. In order to arrange convenient therapeutic measures when an algal bloom has just occurred, water quality specialists must quickly diagnose which species or groups have caused the bloom. This diagnosis can be based on macroscopic symptoms such as color of the water and on microscopic symptoms such as taxonomic characteristics. Recently a knowledge base for assisting in the diagnosis of 17 algal species mainly by

Fig. 13. Rule network of the module for control of infections of waters by pathogens.

EXPERT

SYSTEM

KNOWLEDGE

33

DELAQUA

BASE:MODULE BY

CONTROL OFINFECTIONS

PATHOGEN

5¥mploms

BACTERIA

AND

OF WATERS

VIRUSES

O~agno~is

Therapy

S~m~toms in the tegi0~ ol er s u p p l y

(CSV)

Infection ol w&let by aathogen

¢atllog#

Uses ot

I

alternative water s

1

b =© ter~= a ~ d / o r Sym ptomsmn

M m $ $ u r e s in

waste water and ge treat merit

waste w~ter ttea|menl

Meas.re$ ~o, desinflct;on of sludge

T4easure$

~o,

]

desin~ect~on ks

Symptoms

~n ~mste w a t e r a n d qe t r e a t m e m

Symptoms

in t h e

,eg,on of e,

suo~l~

+

Territorial concurrence b e t w e e n region o f e D ~ d e m , c anG r e g i o n o f water Supply

-

Ewoonpntia[

-

~nfec~lou5

increase ~esease~

G~mS ef ~fec~us deseases (bacterium s~ecxe, serum ty~, v~rus specie o~ ~rus type) ~re d~tected in .atPr

,n

Measu,es

*aste . a t e , tment

-

O~sordered functioning of desinfection of sl~dge Unlawfu| inflow or soraying of waste water in the dra~nag~ basin

-

Measures

O~5ordered funct~on,ng preciD~ tarpon

of

of -

-

Disordered functzon~ng of desinfectlon of waste Hater

U n l a w f u l u s i n g or storing o f s l u d g e ~n the drainage basln

Measu,e. for des~nfect~on

for

desln~ec tion Judge

Post- treatment stab~ [izati~

by waste ponds

-

Treatment

Iter

by

ga

a

-

*orks

ChLorination

radiati~

- Ozona t i o ~ -

S[o~

sand

filtration

Chlor inat~on Ozonat ion Ther~ic ~a~te trea~men t

water

-

Wh~ t o - w a s h i n g

-

Treatment

-

C o m p o lt i n g

-

Pa~teurlzation

by

-

TreaSonS tad iation

by q a ~ a -

-

Treatment radiation

by UV-

f~Itration

a~onia

-

Slow sand

T ~ e a t m e n t by gammaradiation

-

Filtration by active carbon

FL1trat~on by active carbon

-

T r e a t m Q n t by s o d i u m hypochlorlde

-

TreaSonS ~m~ne

by

Chlorine

-

Treat~t ~iox~de

by

chlorine

-

;locculation by m e t a l salts or f l o c c u l a t i o n and f i l t r a t i o n

-

O e s ~ n f e c t i o n and f l u s h i n g o f O~Des a n d t a n k s

-- T h e r m i c d r y i n g

- Chemical

Or~C~DLtat~on

34

F. R E C K N A G E L E T AL.

morphological and ecological characteristics has been prepared (Arnscheidt, 1992). It also includes information on algal toxins and substances which impair water treatment technologies in water works like flocculation processes. It will be implemented in an object-oriented manner by N E X P E R T OBJECT and ToolBook where pictorial information is also made available (Fig. 11). Therapy in the sense of an operational control of algal blooms is based on a catalogue of measures prepared as a knowledge base (Hintersdorf, 1989). It permits the derivation of recommendations for general control measures, which might be performed permanently when such an event occurs, as well as specific control measures, which have proved to be efficient for the control of algal species or groups. In Fig. 12 the present contents of this module are represented roughly. Prophylactic water quality m a n a g e m e n t to prevent algal blooms to a certain degree can also be assisted by scenario analyses using SALMO. Algal growth and primary production can be investigated depending on nutrient concentrations, mixing conditions, zooplankton grazing, light and temperature conditions and appropriate recommendations for control policies can be derived.

2. 3.3. Problem domain infection of waters by pathogens A great number of pathogenic bacteria and viruses are transmitted by water. Viruses showing a virulence of up to 400 days in particular in clean waters have proved to be especially malicious. Therefore the risk of infectious disease caused by drinking water from reservoirs must be minimized. This target can be met by assuring proper treatment of waste water and sewage sludge in the catchment area, a sufficient retention time of reservoir water, and a proper desinfection of the raw water in water works. In order to provide alternative recommendations when faults or inefficiencies in the sterilization measures suddenly occur, a knowledge base (Fig. 13) has been designed by means of valid standards (Boehmer, 1990). The diagnosis of the infection of a water body is founded mainly on symptoms which are indicative of faults in wastewater and sewage sludge treatment or handling in the catchment area. Therapeutic measures include desinfection methods in the water works which are in common practice or recomm e n d e d in literature. For the time being the knowledge base is implemented in P R O L O G . 3. CONCLUSIONS Architecture and functionality of the hybrid expert system D E L A Q U A , which may serve as a toolkit for assisting in water quality control of lakes

EXPERT SYSTEM D E L A Q U A

35

and reservoirs, are discussed. As a platform for implementations the shell N E X P E R T O B J E C T has proved to be convenient, providing a network-like integration of data and knowledge bases, well-tried numerical models and a fuzzy model written in different languages. Design and construction of the user interface was facilitated by ToolBook, which allows object-oriented programming in OpenScript. Contents of knowledge bases and the structure of the data base result from r e c o m m e n d a t i o n s of experts and literature investigations. In practice these must be validated and completed iteratively in the frame of prototype sessions with water quality specialists. The first test results of the very simple fuzzy model referred to the Saidenbach reservoir and showed that qualitative predictions of algal blooms correspond with m e a s u r e m e n t s to a certain degree. F u r t h e r investigations of the time-series of the Saidenbach reservoir will be used to create fuzzy variables considering the influence of the zooplankton community on seasonal succession of phytoplankton. To exploit the information content of historical water quality data for decision making, further rules and facts on well-defined water quality problems will be incorporated into the knowledge bases of D E L A Q U A from long-term time-series of different reservoirs by means of neural networks. ACKNOWLEDGMENTS We would like to thank Takehiro Fukushima for helpful discussions and Rick Weisburd for his useful comments on the first draft. REFERENCES Arnscheidt, J., 1992. Beitrag zur Strukturierung einer Wissensbasis zur computergestiitzten Taxonomie ausgew~ilter Algenarten. TU Dresden, Institut fi~r Hydrobiologie, Literaturbeleg, 79 pp. Ball, G.L. and Gimblett, R., 1992. Spatial dynamic emergent hierarchies simulation and assessment system. Ecol. Modelling, 62: 107-121. Benndorf, J., 1979. Kausalanalyse, theoretische Synthese und Simulation des Eutrophierungsprozesses in stehenden und gestauten Gew~issern. Dissertation, TU Dresden, Fakult~it Bau-, Wasser- und Forstwesen, 165 pp. Bernhardt, H. and Clasen, J., 1981. Oligotrophication of the Wahnbach Reservoir. In: R.A. Vollenweider (Editor), Eutrophication: A Global Problem. Part 1. Water Qual. Bull., 6: 74-78. Boehmer, Chr., 1990. Methoden der Abwasser- und Schlamm-behandlung zur Eliminierung pathogener Bakterien und Viren. TU Dresden, Institut fiir Hydrobiologie, Literaturbeleg, 89 pp.

36

F. R E C K N A G E L E T AL.

Camara, A.S., Ferreira, F.C., Loucks, D.P. and Seixas, M.J., 1990. Multidimensional simulation applied to water resources management. Water Resour. Res., 26: 1877-1886. Haefner, J.W., 1991. Food-web simulation on parallel computers: inter-processor communication benchmarks. Ecol. Modelling, 54: 73-79. Hintersdorf, J., 1989. Wissenserfassung fiir den Baustein Havariebek~impfung des Expertensystems Wasserbeschaffenheit Standgew~isser. Diplomarbeit, TU Dresden, Institut fiir Hydrobiologie, 94 pp. Horn, W. and Horn, H., 1990. Long term relationships between phyto- and zooplankton in the meso-eutrophic reservoir Saidenbach. Arch. Hydrobiol. Beih., 33: 749-762. Lai, W. and Berthouex, P.M., 1991. Testing expert system for activated sludge process control. J. Environ. Eng., 116: 890-909. Nix, S.J. and Collins, A.G., 1991. Expert systems in water treatment plant operation. J. AWWA, 83: 43-51. Recknagel, F., 1980. Systemtechnische Prozedur zur Modellierung und Simulation von Eutrophierungsprozessen in stehenden Gew~issern. Dissertation, TU Dresden, Fakult~it fiir Bau-, Wasser- und Forstwesen, 169 pp. Recknagei, F., 1989. Applied Systems Ecology. Approach and Case Studies in Aquatic Ecology. Akademie-Verlag, Berlin, 138 pp. Recknagel, F., Beuschold, E. and Petersohn, U., 1991. DELAQUA - a prototype expert system for operational control and management of lake water quality. Water Sci. Technol., 24: 283-290. Recknagel, F., Hosomi, M., Fukushima, T. and Kong, D.-S., 1993. Simulation study on short- and long-term effects of control of the external and internal phosphorus loads in eutrophic lakes. Proc. of the Int. Conf. on Modelling Change in Environmental and Socioeconomic Systems. Perth, 6-10 December 1993 (in press). Reynolds, C.S., 1984. The Ecology of Freshwater Phytoplankton. Cambridge University Press, 384 pp. Sequeira, R.A., Sharpe, P.J.H., Stone, N.D., EI-Zik, K.M. and Makela, M.E., 1991. Objectoriented simulation: plant growth and discrete organ to organ interactions. Ecol. Modelling, 58: 55-89. Sommer, U., Gliwicz, Z.M., Lampert, W. and Duncan, A., 1986. The PEG-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol., 106: 433-471. TGL 27885/01, 1982. Fachbereichstandard Nutzung und Schutz der Gew~isser. Stehende Binnengew~isser. Klassifizierung. Berlin, 16 pp. Uhlmann, D., 1979. Hydrobiology. A Text for Engineers and Scientists. Wiley & Sons, Chichester. Vollenweider, R.A., 1976. Advances in defining critical loading levels for phosphorus in lake eutrophication. Mem. Ist. Ital. Idrobiol., 33: 53-83. Walker, W., 1979. Use of hypolimnic oxygen depletion rate as a Trophic State Index for lakes. Water Resour. Res., 15: 1463-1470. Zadeh, L.A., 1989. Knowledge representation in fuzzy logic. IEEE Trans. Knowledge Data Eng., 1: 89-100.