Identification of influencing parameters of water quality and its analysis by using spatial graphs and autocorrelation function

Abstract

Spatial analysis or spatial statistics includes many techniques and study the entities like geographic properties. Spatial analysis has variety of techniques using different approaches applied to various fields most notably in the analysis of geographic data. Defining the spatial location of the entities being studied is the fundamental problem in the spatial analysis. Spatial graphs and mathematical tools play a vital role in the analysis of spatial data. The integration of Graph theory, statistical tools and remote sensing with GIS provides a scientific platform for developing an integrated database among all the different entities involved in environmental planning and management activities. In this connection, the advanced technology of remote sensing and GIS software linking with the water quality data to create the spatial distribution and spatial graphs for identification of water quality stretch zones and its impact is discussed. Specifically, considered spatial statistics, spatial autocorrelation, and spatial graph theory are considered to identify the water quality index. For effective management and sustainable development of water quality the influence of various parameters are studied.