Prediction and Analysis of Water Resources using Machine Learning Algorithm
Sarakutty T. K.1, Ravikumar K.2, Hanumanthappa M3
1Sarakutty T. K.*, Research Scholar, Rayalaseema University, Kurnool, India.
2Ravikumar K., Research Scholar, Kalinga Institute of Industrial Management, Bhubaneshwar, India.
3Hanumanthappa M., Department of Computer Science and Applications, Bangalore University, Bengaluru, India.
Manuscript received on November 26, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3970-3974 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4509129219/2019©BEIESP | DOI: 10.35940/ijeat.B4509.129219
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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 demand prediction plays an important role in urban and environmental planning, ecological development, decision-making processes and optimum utilization of water resources. A precise water demand prediction has a key job in the forecasting, design, process, and organisation of water resources frameworks. The under stress natural resources and the ever increasing population size makes it dominant to accurately and efficiently forecast water demand in the urban area which is possible by applying data mining techniques on the huge volumes of available water data. This paper focuses on building precise predictive models for water demand prediction using support vector machine which takes care of the nonlinear changeability of water demand at diverse levels for optimal operations.
Keywords: Data Mining, Machine Learning, Support Vector Machine.