Title: A classed approach towards rainfall forecasting: machine learning method

Authors: Shanu Khan; Vikram Kumar; Sandeep Chaurasia

Addresses: Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur 302018, India ' Department of Hydrology, Indian Institute of Technology, Roorkee, 247667, India ' Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University, Jaipur, 303007, India

Abstract: The interest for precipitation anticipating has turned into a huge element in the outline of rainfall runoff and other hydrological models. As of now the artificial neural system (ANN) is the most well-known model that is utilised to evaluate rainfall using different climatic parameters. However, classed approach, called the extreme learning machine (ELM) algorithm, has been introduced in this present paper and ELM-based learning framework is used to predict rainfall-runoff forecasting. Extreme learning machine algorithm is much faster as compared to the artificial neural system, and outcomes in a high generalisation competence. In view of these outcomes we assert that out of the machine learning calculations tried, the ELM was the more expeditious tool for the forecast of rainfall and its related properties.

Keywords: data mining; artificial neural network; ANN; extreme learning machine; ELM; rainfall-runoff prediction.

DOI: 10.1504/IJSI.2018.091416

International Journal of Swarm Intelligence, 2018 Vol.3 No.4, pp.276 - 289

Received: 03 Jun 2017
Accepted: 05 Sep 2017

Published online: 30 Apr 2018 *

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