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Title: Runoff prediction using Big Data analytics based on ARIMA Model
Authors: Dhote, Vijay
Mishra, Satanand
Shukla, Jai Prakash
Pandey, S. K.
Keywords: Big Data;Hadoop;MapReduce;HDFS;Time series analysis;ARIMA;Data mining
Issue Date: Nov-2018
Publisher: NISCAIR-CSIR, India
Abstract: Big data Analytics is used in the study of developing forecasting models for prediction of runoff in Narmada river basin. Big data and data mining used as an advanced technique for storing and managing the large data set of runoff. Hadoop technique is used for storing and processing the large data. A new concept of big data processing is known as MapReduce it is a programming model. MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Autoregressive Integrated Moving Average (ARIMA) modelling is used for the prediction of time series runoff. Historical runoff data, which is large in size is stored in big database. The main objective of the time series modelling is to carefully collect and rigorously study the past observation of time series and to develop an appropriate model that predict the future runoff in hydrological time series.
Page(s): 2163-2170
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.47(11) [November 2018]

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