Water Hazard Prediction using Machine Learning
D. Dakshin1, V.R. Rupesh2, S. Praveen Kumar3
1D. Dakshin, Student Currently Perusing Computer Science and Engineering in SRM Institute Of Science And Technology located in Chennai, Tamil Nadu, India.
2V. R. Rupesh, Student Currently Perusing Computer Science and Engineering in SRM Institute Of Science And Technology located in Chennai, Tamil Nadu, India.
3S. Praveen Kumar, Student Currently Perusing Computer Science and Engineering in SRM Institute Of Science And Technology located in Chennai, Tamil Nadu, India.
Manuscript received on October 15, 2019. | Revised Manuscript received on 26 October, 2019. | Manuscript published on November 10, 2019. | PP: 1451-1457 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4245119119/2019©BEIESP | DOI: 10.35940/ijitee.A4245.119119
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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 the most essential need of all life forms. This essential need can also create hazards to us which comes in the form of water hazards (flood and drought). Catastrophic events, for example, flood is respected to be brought about by outrageous climate conditions just as changes in worldwide and territorial atmosphere. If precautions are not taken beforehand it becomes more and more difficult to control when it occurs. This study aimed to forecast both flood and drought using Machine Learning (ML). So as to have a clear and precise forecast of flood and drought hazard is fundamental to play out a specific and multivariate analysis among the various kinds of data sets. Multi variate Analysis means that all measurable strategies will concurrently analyses manifold variables. Among multi variate investigation, ML will give expanding levels of exactness, accuracy, and productivity by finding designs in enormous and variegated data sets. Basically, ML methods naturally acquires proficiency data from dataset. This is finished by the way toward learning, by which the calculation can sum up past the models given via preparing information in info. AI is intriguing for forecasts since it adjusts the goal methodologies to the highlights of the data set. This uniqueness can be utilized to foresee outrageous from high factor information, as on account of the risks. This paper proposes systems and contextual analysis on the application on ML calculations on water hazard occurrence forecast. Especially the examination will concentrate on the utilization of Support Vector Machines and Artificial Neural Networks on a multivariate arrangement of information identified with water level of lakes in and around Chennai and measurement of rainfall in the lakes.
Keywords: Machine Learning, Artificial Neural Network, Support Vector Machine, Flood, Drought, Prediction.
Scope of the Article: Machine Learning