Building a geo-expert system integrating Remote Sensing and GIS
Groundwater Assessment Model
The purpose of this study is investigate how a knowledge based system could be developed which would be able to assist the groundwater resources assessment. The expert system 'AQUIFER' which helps to detect aquifers from Landsat MSS images (Peacegood et al., 1986) became the starting point of the study. In this system, the entire satellite image is considered as a single object and a class label in the form of an 'overall likelihood' is assigned for finding and aquifer in that area. The area covered by a satellite image cannot be treated as a single homogeneous unit for identification on an aquifer. Instead, on the basis of a hydrological model, the image can be subdivided in hydrogeological unit a label of occurrence of aquifer can be assigned to each of these units. Also the weight factors are introduced in the system to express 'strong', 'weak' etc by assigning some numerical values which are not based on the frequencies of co-occurrence between classes and attributes.
The basic goal for the present study is to provide a measure of probability for finding groundwater in an area on the identification and delineation of geohydrological units which may be shallow aquifers. The consultation starts with forward chaining process to identify main landscape types. Then the user is asked to process to identify main landscape types. Then the user is asked to subdivide each landscape type into homogeneous areas (regions) based on the elements relevant for identification of geo hydrologic units within that landscape type. These regions are the object and for each object, the system will run and determine the probability for the presence of gorundwater. This part of the procedure is done following a backward chaining procedure.
At the highest level, the user is asked to segment the image into alluvial and hardrcok areas. These areas must be further segmented into homogenous regions based on relief and stream characteristics. The system tries to find the presence of shallow aquifers for each of these regions. It asks the user the type of depositional environment such as miander belt, back swamp, braided, deltaic etc. under these environments, it looks for the favorable areas for groundwater. For example, in miander belt, the system asks the areal spread of sandy deposits like natural levees, points bars and abandoned channel areas. For each of these deposits, evidence for existence of shallow aquifer is found by the presence of dense vegetation irrigated arable land etc. The data organization in the system is shown in figure 1.
Fig. 1
Even though thus system is a simplification of the real world it helps to know the presence of shallow aquifers by identifying hydrological units.
There is always a measure of uncertainly between landscape units and geo-hydrologic units. For example, the large parcels and presence of arable land may not always indicate natural levee. Hence a posteriori probability for finding a unit on the basis of landscape elements is calculated following Bayes' rule.
The presence of levee based on the occurrences of elevated area, irrigated land, arable land, dense vegetation etc. can be calculated by
It gives the posteriori probability using multiple evidences with the assumption that the evidences are not correlated. The probabilities on the right hand side can be calculated by counting the pixels from the digital map of a training area. In the present study, these probabilities are estimated visually from a map of known area assuming that the observations are not correlated. Although this assumption violates the real situation, it became possible to implement Bayesian formula in such a way that the a priori probability of a hypothesis (presence of levee) is updated after each observation (which can be TRUE, FALSE or UNKNOWN). In a available in a further stage, when a large amount of reliable field data is available in a GIS, these probabilities can be checked and implemented in a large table.
