Water-Level Prediction Utilizing Datamining Techniques in Watershed Management

Abstract

The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. Many statistical methods are of the previous techniques used to predict water level, which give approximate results. To overcome this disadvantage, gradient descent algorithm has been used. This gives more accurate results and provides higher performance. K-means algorithm is used for clustering, which iteratively assigns each data point to one of the k groups according to the given attribute. The clustered output will be refined for further processing in such a way that the data will be extracted as ordered datasets of year-wise and month-wise data. Clustering accuracy has been improved to 90.22%. Gradient descent algorithm is applied for reducing the error. It also helps in predicting the amount of water to be stored in watershed for future usage. Watershed development appears to be helpful in terms of groundwater recharge, which benefits the farmers. It can also be used for domestic purposes.