ISSN 2394-5125
 

Research Article 


Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models

M.Saravanan, Nagoor Meeran.A.R.

Abstract
Water scarcity becoming major problem in many countries like india, Recent problems of critical shortage of water in so many parts of our country due to population, lack of water harvesting plans, excess wastage of water and extreme pollution of water bodies, bringing inaccurate estimation of ground water available in these parts. Even though many water resoureces available majorly dependent on ground water,So estimation of groud level water becomig difficulty in some occations for the goverment, Even many authors proposed water resource managment ,Still we facing challenge in our country for prediction of ground level water based on the various factors like rain fall,inorder to address this issue in this work developed analysis of various machine learning models to predict the ground level water for the data set available from the goverment side.This work deals with
dataset considered from india official site and hydrological parameters consideration for different states of India. We performed a data cleaning step to make sure that the dataset used had no null values, so that it could provide the best optimal result in the final stage. After preprocessing the dataset , we performed training on different machine learning models such as SVM (Support Vector Machine), KNN (K-nearest neighbors), Gausian Naive Bayes(GNB), Logistic Regression, Decision-Tree Classifier Algorithm.All the models trained individualy and tested with the different dataset and finaly accuracy of individual model compared with each other. From the comparision of all models Decision Tree Classifier model provides the best improved prediction results. Hence, it can be used to analyze groundwater level properties, so that the government can take preventive measures beforehand.

Key words: water harvesting plans, SVM (Support Vector Machine), Gausian Naive Bayes(GNB)


 
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Pubmed Style

M.Saravanan, Nagoor Meeran.A.R. Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . JCR. 2020; 7(4): 1044-1052. doi:10.31838/jcr.07.04.195


Web Style

M.Saravanan, Nagoor Meeran.A.R. Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . http://www.jcreview.com/?mno=99144 [Access: May 30, 2021]. doi:10.31838/jcr.07.04.195


AMA (American Medical Association) Style

M.Saravanan, Nagoor Meeran.A.R. Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . JCR. 2020; 7(4): 1044-1052. doi:10.31838/jcr.07.04.195



Vancouver/ICMJE Style

M.Saravanan, Nagoor Meeran.A.R. Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . JCR. (2020), [cited May 30, 2021]; 7(4): 1044-1052. doi:10.31838/jcr.07.04.195



Harvard Style

M.Saravanan, Nagoor Meeran.A.R (2020) Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . JCR, 7 (4), 1044-1052. doi:10.31838/jcr.07.04.195



Turabian Style

M.Saravanan, Nagoor Meeran.A.R. 2020. Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . Journal of Critical Reviews, 7 (4), 1044-1052. doi:10.31838/jcr.07.04.195



Chicago Style

M.Saravanan, Nagoor Meeran.A.R. "Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models ." Journal of Critical Reviews 7 (2020), 1044-1052. doi:10.31838/jcr.07.04.195



MLA (The Modern Language Association) Style

M.Saravanan, Nagoor Meeran.A.R. "Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models ." Journal of Critical Reviews 7.4 (2020), 1044-1052. Print. doi:10.31838/jcr.07.04.195



APA (American Psychological Association) Style

M.Saravanan, Nagoor Meeran.A.R (2020) Improving the Analysis rate of Ground Level Water Prediction using different Machine learning models . Journal of Critical Reviews, 7 (4), 1044-1052. doi:10.31838/jcr.07.04.195