PREDICTING SURFACE WATER LEVEL ESTIMATION BY USING DATA MINING TECHNIQUES

Abstract

To auspicious get exact pixel water surface extent data through remote detecting is to a great degree noteworthy to the environmental reclamation in inland waterway bowls and for the exact administration of water assets. In regard to the insufficient extraction of water surface extent data show in pixels in the greater part of the ebb and flow water data models, a straightforward model Enhanced Water Index (EWI) in view of Modified Standardized Difference Water Index (MNDWI) has been presented. EWI, which is arranged toward the sub-pixel level examination of water surface extent mapping of inland stream bowl, has been advanced in light of the examination of run of the mill ghostly marks for example, forsake, soil, and vegetation alongside MNDWI in agreement with the Landsat TM band highlights. The examination is done by utilizing strategies for pixel-based EWI esteem with various water extents which are dissected through the presentation of the straight crossover reenactment between the water body and the comparing foundation. In conclusion, the impact of EWI demonstrate has been tried in the medium and lower ranges. The amendment coefficient for sub-pixel level water surface extent anticipated by the EWI show and the test information. Results demonstrated that the model could viably remove the data about pixel water surface extent in inland stream bowls. This investigation demonstrates that EWI show has awesome potential in its application for water extent mapping applications.