1. ABILITIES OF NUMERICAL GROUND WATER MODELS · are very inhomogenious in qualitiy and age. ......

Knowledge-based ground water modeling

and parameter validation

T. Pfaff & J. Lindner

PFAFF & CO GmbH, Heinrich-Kleystr. 2,

D-8000 Miinchen 40, Germany

ABSTRACT

Groundwater flow and particle transport can be described by

mathematical groundwater simulation models, which turned out to be very

good tools for analysing complex groundwater scenaries. These

groundwater models gain in quality if they are combined with knowledge

based systems. The problem with groundwater modeling often is not the

connexion between model and knowledge based system rather than the

knowledge itself. Getting true values of the input parameters required by

the model can be very difficult, because these are correlated in a very

complex manner. By discussing the input parameter "macrodispersivity"

this paper discusses the importance of acquiring high quality input data for

groundwater simulation models. Preliminary results of such long time

large scale field experiments show that there is a functional relation

between dispersivity, length of flow path and geological parameters. If this

relation can be expressed quantitatively by evaluating a functionality very

compact knowledge will be gained which can be placed directly into an

algorithm for particle transport modeling.

Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517

404 Artificial Intelligence in Engineering

1. ABILITIES OF NUMERICAL GROUND WATER MODELS

Numerical groundwater models are an important tool in groundwater

protection and analysis of complex groundwater scenaries (KinzelbaclP).

This is mainly due to their ability of forecasting and calculating the worst

case. Therefore they are very valuable in planification, taxing hazardous

risks, analysing groundwater contaminations, security analyses for wells

e.g.. Managed by an expert they represent the state of the art in

groundwater modeling. Only by using such methods a complete view of the

groundwater flow can be gained as well as prognostical statements. In the

following the limits of traditional hydrogeological methods and in turn the

advantegeous abilities of numerical models in the field of simulating the

groundwater flow are discussed in brief by means of figures 1, 2 and 3

(Pfaff*).

Fig. 1: By applying conventional hydrogeological methods only alinear interpolation can be made between two known altitudes of agroundwater table. But the linear interpolation is faulty if a water tableis vaulted. For this reason a groundwater isohypse map often isinconsistent and of minor suitability for the examination of solutemovement.


Artificial Intelligence in Engineering 405

contamlnant source390-

Fig. 2: In the case of non-linear types of flow (which are the standardin nature) only the convective solute movement can be described withtraditional hydrogeological methods, which is insufficient forforecasting and worst case analyses. In contrast computer models leadto much more accurate descriptions of the propagation by consideringconvective and dispersive solute movement.



worst case

Fig. 3: By applying traditional methods in many cases only the mostprobable case of the solute movement can be predicted, whereas agroundwater model is able to show all possible cases. The practicalprofit of such worst-case-examinations is enormous considering thecosts, which might arise from terrain reconnaisance and analysis.Moreover, only worst case examinations are a confidential basis fordecisions even by court of justice.

Computer models are typically aplied in the following areas:

- Risk analyses for drinking water supplies: Drinking water supplies on

the one hand are endangered by locally fixed risks (e.g. agriculture or

industrial plants). On the other hand, a main danger are the mobile risks

as for instance transports of dangerous material. By applying groundwater

models it is possible to quantify these risks and to provide prophylactic

protection.

- Risk examination and sanitation of hazardous waste deposits: In

Germany thousands of hazardous waste deposits with a different potential

of influencing the groundwater exist. The cleanup has to be done



temporally differentiated for reasons of cost and capacity by setting

priorities with regard to the hazardous potential of the landfill. By

applying groundwater models it is practicable to evaluate the danger

potential and to calculate the most effective sanitation strategy.

Groundwater models are by this means very helpful in answering the

question, how a groundwater contaminant can be removed from the

underground as fast as possible and at the same time as economical as

possible.

- Recovery of drinking water: For the recovery of drinking water the

location of the well is often determined empirically. Any slight spatial

deviation from the optimum location results in a reduction of the well -

productiveness. Using groundwater models enables the qualified expert to

optimize the location with regard to well productiveness and groundwater

protection refering to groundwater contaminations.

- Accidents: By accidents and inexpert behaviour groundwater may be

contaminated. With the help of computer models the effect of such a

contamination can be evaluated and the corresponding propositions for

the sanitation strategy can be given.

- Prognosis: Up to now hydrogeological investigation is usually considering

only the actual state. In many cases possible modifications in the

groundwater reservoir in the future, e.g. as a consequence of changes in

climate or utilization, are not taken into consideration in a quantitative way,

as required for environmental risk assessments. By use of groundwater

models both the probability and the extent of such changes can be

evaluated in order to establish the idea of security in the long term.

- Environmental risk assessment: Using computer models enables the

experts in charge for selecting a save industrial site to do this already in

the planning step, regarding possible failures of barriers in the long term

as well.



Modeling as well as simulating of groundwater flow and mass transport by

means of numerical computer models represents the state of the art in the

hydrogeological treatment of groundwater flow, although their capability

of solving actual problems has not yet been accepted by the majority of

hydrogeologists. In spite of the growing number of problems - especially in

the field of hazardous waste treatment and agriculture - and although the

economical advantage of such a model is provable still very little use is

made of models in order to simulate groundwater flow and contaminant

transport. For one reason it is usual to solve hydrogeological problems

step by step. The very low price in the beginnnig is the advantage of this

proceeding, but, however, in the following steps the costs are growing

exponentially. Compared to the step by step strategy groundwater models

are relatively expensive to begin with, but in the end this solution often is

much cheaper. These relatively high starting costs still deter the

authorities in charge from using this models in spite of the higher quality

and financial advantages of groundwater modeling.

2. ADVANTAGES OF KNOWLEDGE-BASED GROUNDWATER

MODELS

No groundwater model is better than its data base. Very often the problem

in modeling groundwater flow is that only data bases are available, which

are very inhomogenious in qualitiy and age. In consequence it is a very

tedious and time consuming but necessary work, to prove the consistency

of the used data base. Finally by using data bases, which are not adequate,

the only benefit of modeling might be to prove the inconsistency of the

used data. In this case the results from modeling are of no other practical

use than to show the lack of adequate and therefore reliable data.

Knowledge-based groundwater models are able to test the consistency of

data mostly by themselves and without any major help of a hydrogeologist

and they can even give hints about where and which data are still missing.

This way time consumed by investigations is shortened and quality of

predicts is guaranteed. In addition by increasing the quality the acceptance

of groundwater models can be increased.



Today by means of data processing expert - knowledge can be supplied to

and processed by groundwater models using dynamic and object-

orientated concepts. The main problem of using groundwater models is

not the feasability of the link between knowledge and groundwater model

rather than the availability of the knowledge itself. Up to now to a certain

extent equivalent expert - knowledge is still very small in quantity and

hydrogelogical expert work often is very inhomogenuous in quality. By

using a knowledge-based groundwater model this lack of quality can be

avoided.

3. DATA VALIDATION IN LARGE-SCALE FIELD EXPERIMENTS

Many phenomena in hydrogeology can not be described entirely by means

of groundwater formulas. This kind of phenomena have to be described

additionally by considering and determining the observable complex

parameter relations. In the following this will be discussed using the

phenomen DISPERSION as an example.

In hydrogeology dispersion is refered to as the decrease of a concentration

gradient as a result of a velocity distribution in the water saturated zone of

an aquifer. Dispersion is therefore an expression of the inhomogenity of

the aquifer (Fried*). Figures 4 and 5 show the influence of dispersion on

the spatial distribution of a groundwater contaminant.



Fig.4: Prediction of contaminant transport with a transversal dispersivity of O.lmusing a numerical model. In this case the water supply is not affected.

Fig. 5: Prediction of contaminant transport with a transversal dispersivity of 1.0musing a numerical model. In this case the water supply is affected.



The results of the numerical model shown in Figures 4 and 5 clearly

demonstrate, that the site-specific dispersion has to be known in order to

predict reliably the possible influence of a hazardous waste deposit on a

drinking water supply located downstream of the waste deposit.

The longer the flowpath of a contaminant particle the greater the number

and size of inhomogenities this particle possibly will 'see'. Therefore in

general dispersion is increasing with the length of the flowpath (Gelhar%).

In addition, as for instance Seiler et al.S pointed out, a relation between

dispersion and geological formation has to be taken into account, which

can be determined only empirically (Fig. 6).



dispersivity in m89 T

78--

68--

58 --

40 -.

30 --

20 --

10 --

* quaterny gravelso coarse sands+ fine sands

El E2 E3 E4

length of flow path in m

Fig. 6: Schematic representation of a relation between dispersion andgeological formation and length of flowpath.

Only if complex relations as discussed above are evaluated reliable and

described quantitatively so that they can be used in a mathematic

groundwater model as an expert system it will be possible to omit

extensive investigations of the site-specific dispersion partially or entirely

(Fig. 7).



dispersion - coefficient

known site (gravels)

unknown site (coarse sand)

known site (fine sand)

particle diameter

Fig. 7: Use of interpolating schemes in groundwater models withartificial intelligence.

Therefore the actual problems in groundwater protection can only be

solved by groundwater models containing imminent knowledge of

dispersion and similar phenomena. Because there is at the present time

still a lack of data we are trying to improve the knowledge base as being

the most urgent work by carrying out large scale field experiments, which

have been supported by the Bavarian State Ministery of Environmental

Protection. In these field experiments we are investigating the scale

dependent variation both of the longitudinal and the transversal

dispersivity. The principle is to carry out groundwater tracer tests at very

good investigated test sites, where every hydrogeological parameter except

the dispersion is known. During the experiment the tracer concentration is

observed via a close net of groundwater observation wells (Fig. 8).

Subsequently the values for dispersion can be evaluated by means of



analytical as well as numerical methods and the model itself can betrained.

If we will be able to reliably quantify the effect of the flow length on

dispersivity we might as well predict mass transport in groundwater very

exactly for the investigated area as well as for every other site with similar

geology. Preliminary results of these experiments indicate that dispersivity

is not a constant but increasing with flow length up to a maximum value

which has not yet been evaluated exactly. It should be noted however, that

these preliminary results also indicate generally lower values for

dispersion as reported up to now in the literature.

injection well

AV

obser

O

0

200m

vatior

0

©_

OO

i well

direction of

groundwater flow

aquifer

tracer distribution

water saturatedzone

bottom ofthe aquifer

water unsaturatedzone

Fig. 8: Schematic view at (top of figure 8) and profile (bottom of figure8) of the experimental setup for large scale tracer tests, to be carriedout at several sites with different geology.



The common practice in site investigation is not very economic since in

most studies only the parameters themselves are investigated but not the

relations between these parameters. Because of the lack of reliable

relations between these parameters the results of site specific

investigations are not transferable to other sites without strong restrictions

and the effort spent for the investigated site is of marginal benefit for

other sites only.

In order to obtain transferable results we are carrying out field

experiments at different test-sites with different geology. Subsequently to

the field experiments, data evaluation and modeling we will evaluate

whether the relation between geological parameters as e.g. grain size or

geological facies on the one side and dispersivity on the other side can be

quantified.

For any further sites under investigation the site specific work could be

decreased to a minimum due to the use of a knowledge-based

groundwater model. It would be possible for the investigators to omit a

lot of detailed and very expensive site-specific investigations already

imminent in the model. Furthermore it would be possible to save a lot of

time, which would be of great benefit in the case of an accident especially

in an area investigated not very well. Only knowledge-based groundwater

models can give trustable results for prediction in such a case.

4. CONCLUSION

The actual problems in groundwater protection can only be solved by

knowledge-based groundwater models. The involved knowledge base

should be broadened as fast and as far as possible, which is not an

informatic but a hydrogeological problem. Informatic can give a lot of

hints about deficits of information to guide further hydrogeological work.

Hydrogeological research work has to focus much at such problems and in

practice the use of groundwater models should be increased to train the

models as well as to avoid quality problems in expert opinions.



UTERATURE

1. Fried, JJ., Groundwater Pollution, Developments In Water Science

Vol.4, Elsevier, Amsterdam, 1975

2. Gelhar, L.W., 'Stochastic subsurface hydrology from theory to

applications', Water Ressour. Res. Vol.22, pp. 135-145,1986

3. Kinzelbach, W., Groundwater Modelling, Developments In Water

Science Vol. 25, Elsevier, Amsterdam, 1986

4. Pfaff, T., 'Einsatzmoglichkeiten und Grenzen intelligenter,

wissensbasierter Simulationsmodelle im Grundwasserschutz', in

Informatik -Fachberichte Vol.296, pp.618-627, Proceedings of the 6th

Symposium of Computer Science for Environmental Protection,

Miinchen, Germany, 1991. Springer, Berlin, 1991

5. Seiler, K.P., Maloszewski, P. & Behrens, H., 'Hydrodynamic

dispersion in karstified limestones and dolomites in the upper Jurassic

of the franconian alb, f.r.g.% Journal of Hydrogeology Vol.108, pp. 235-

247; Amsterdam, 1989


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