Ground Water Quality Modelling for Irrigation Using Data Mining Technique and Spatio-Temporal Dates

摘要

Water availability and quality are the key factors which influence the development of agricultural sector. The quality of water is mainly depends on several parameters of water. Ground water quality may vary with respect to time and space. Ground water quality prediction is an important agricultural problem for farmers and it can be solved with the help of previously available data. Accordingly this paper emphasises prominence of data mining technique using spatio-temporal data to develop useful data models with high accuracy and relevant relationships to predict ground water quality. By and large, several physio-chemical parameters are influencing the quality of ground water; generally the quality of water is assessed based on the Total Dissolved Solids content present in water. For experimental purpose significant parameters such as EC, Na, Mg and Cl were taken as input. Linear regression technique is used in this study to develop data models and TDS is considered as the output of the model.