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Integrated Remote Sensing and factor analytic GIS model for evaluating groundwater pollution potential


Conclusions
For over all developments of a region reliable estimate of groundwater quality and quantity is of paramount importance. Generally sufficient data required for groundwater pollution potential mapping are not available for Indian watersheds. Satellite data can be analyzed to generate database required for GWPP studies. Generated database can be put to FAM for extracting the most influential composite and subsequently the variable loading. Using the proposed FAM the study area was classified in to different classes in terms of their potential to pollute the groundwater. The model efficiency was tested by carrying out field surveys and found to above 80 percent. The model can be used for evaluating the GPP in any area after calibration. The added advantage of the proposed approach is that it compresses the data up to 70% that helps in efficient analysis and prediction.

Table.1 Variable Loading (RIW)
Goal Level
Level I Level II Level III
Feature RIW Feature RIW Feature RIW
GWPP Surface (0.65) Land use (0.44) Agriculture
Water Body
Barren Land
Thin forest
Thick forest
Settlement		 0.40
0.25
0.18
0.10
0.05
0.02
  Land Slope (0.26) Low Slope
Mild Slope
Milder Slope		 0.72
0.21
0.07
  Distance from Paleo Channel (0.18) Less than 50m
More than 50m		 0.90
0.10
  Distance from Flood Plain 0.04 Up to 50m
More than 50m		 0.90
0.10
  Soil 0.06 Sand
Sandy
loamLoamy
sandClay		 0.56
0.27
0.13
0.04
  Distance from Urban areas 0.02 Less than 0.5 km
0.5km - 1.0m
More than 1.0 km		 0.65
0.28
0.07
Sub Surface 0.24 Aquifer Media (0.5) Sand and Boulder
Sand Boulder and Clay
Sand and Clay		 0.63
0.28
0.09
  Permeability in Vertical Direction (0.5) High
Low		 0.90
0.04
Ground Water (0.11) Groundwater Depth (0.60) < 5m
5-15m
> 15m		 0.73
0.19
0.08
  Rainfall Recharge (0.32) High
Medium
Low		 0.65
0.28
0.07
  Water Quality (0.08) SAR Value Low
SAR Value High		 0.75
0.25

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