A classed approach towards rainfall forecasting: machine learning method. (2018)

• Record Type: Journal Article
• Authors: Khan, Shanu; Kumar, Vikram; Chaurasia, Sandeep
• Is Part Of: International journal of swarm intelligence. Volume 3:Number 4(2018)
• Page Start: 276
• Page End: 289
• Publication Date: 2018
• Subjects: data mining -- artificial neural network -- ANN -- extreme learning machine -- ELM -- rainfall-runoff prediction; Swarm intelligence -- Periodicals; 006.3
• Languages: English
• ISSNs: 2049-4041

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.