Random Forest Advice for Water Quality Prediction in the Regions of Kadapa District
S.V.S. Ganga Devi

V.S. Ganga Devi, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India.

Manuscript received on 15 April 2019 | Revised Manuscript received on 22 April 2019 | Manuscript Published on 26 July 2019 | PP: 1464-1466 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12980486S419/19©BEIESP | DOI: 10.35940/ijitee.F1298.0486S419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Water is essential to all basic needs of Human being. The quality of water is significant on the earth for everyone. Machine learning methods concentrates much on data rather than methods. Classification technique uses the past history data to predict the class of new sample(s). The present work collects water samples in the regions of Kadapa district, Andhra Pradesh. Those samples are given to the Laboratory to perform an analysis on physico- chemical properties of ground water, whether they are suitable for drinking or not. In this paper, Random Forest approach is considered to predict the water quality in the regions of Study area and classify the regions into 3 classes whether they are Excellent, Good or Poor for drinking purposes.

Keywords: Ground water, Conditional Interference tree, Random Forest model.
Scope of the Article: Knowledge Systems and Engineering