Water Level Prediction In Water Shed Management Utilizing Machine Learning

Due to uneven rainfall, nowadays the amount of rain to be showered in a month is getting showered in few days. The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. To avoid this, the proposal suggests an idea to develop a watershed and to predict the water level measurement by Bayesian classification, clustering, and optimization techniques. Artificial Neural Network is one of the previous techniques used to predict water level which gives approximate result only. To overcome the disadvantage, this proposal suggests an idea to develop the watershed by using different machine learning techniques. The level of water that can be stored is calculated using Bayes Network which will classify the data into labels according to the condition of the capacity of the minimum and maximum storage level of the watershed. The standardized data considered for the classification are normalized using the z-score normalization. Classification will represent the result by means of the instances that are correctly classified. The output of the classified data is fed into clustering algorithm where the labels are grouped into different clusters. The K-Mean alg