Rainfall prediction for the Kerala state of India using artificial intelligence approaches. (August 2018)

• Record Type: Journal Article
• Authors: Dash, Yajnaseni; Mishra, Saroj K.; Panigrahi, Bijaya K.
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 66
• Page End: 73
• Publication Date: 2018-08
• Subjects: K-nearest neighbor (KNN) -- Artificial neural network (ANN) -- Extreme learning machine (ELM) -- Predictive modeling -- Artificial intelligence; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.06.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Three artificial intelligence approaches - K-nearest neighbor (KNN), artificial neural network (ANN), and extreme learning machine (ELM) - are used for the seasonal forecasting of summer monsoon (June-September) and post-monsoon (October-December) rainfall from 2011 to 2016 for the Kerala state of India and performance of these techniques are evaluated against observations. All the aforesaid techniques have performed reasonably well and in comparison, ELM technique has shown better performance with minimal mean absolute percentage error scores for summer monsoon (3.075) and post-monsoon (3.149) respectively than KNN and ANN techniques. The prediction accuracy is highly influenced by the number of hidden nodes in the hidden layer and more accurate results are provided by the ELM architecture (8-15-1). This study reveals that the proposed artificial intelligence approaches have the potential of predicting both summer monsoon and post-monsoon of the Kerala state of India with minimal prediction error scores.