Water Level Prediction In Water Shed Management Utilizing Machine Learning

Due to uneven rainfall, nowadays the amount of rain to be showered in a monthis getting showered in few days. The massive wastage of water occurs due to irregularheavy rainfall and water released from dams. To avoid this, the proposal suggests an ideato develop a watershed and to predict the water level measurement by Bayesianclassification, clustering, and optimization techniques. Artificial Neural Network is one ofthe previous techniques used to predict water level which gives approximate result only. Toovercome the disadvantage, this proposal suggests an idea to develop the watershed byusing different machine learning techniques.The level of water that can be stored is calculated using Bayes Network which will classifythe data into labels according to the condition of the capacity of the minimum andmaximum storage level of the watershed. The standardized data considered for theclassification are normalized using the z-score normalization. Classification will representthe result by means of the instances that are correctly classified. The output of theclassified data is fed into clustering algorithm where the labels are grouped into differentclusters. The K-Mean algorithm is utilized for clustering which iteratively assign data pointto one of the k group according to the given attribute. The clustered output gives the resultof how many instances are correctly clustered. The clustered output will be refined forfurther process such that the data will be extracted as ordered dataset of year wise andmonth wise data.For the extracted data gradient descent algorithm is applied for reducing the error andpredicting the amount of water stored in watershed for upcoming years by means ofcalculating the actual and prediction value. Later the result will be visualized in the formof graph. The obtained output is considered as an input for posterior probability that usesJ48 algorithm which gives the result of probability of event happened after all the evidenceis taken for consideration and gives the accurate result. The above methodology provideshigh performance and efficient result.