Local area rainfall prediction using hybrid approach. (1st January 2013)

• Record Type: Journal Article
• Authors: Sarkar, Bikash Kanti
• Is Part Of: International journal of innovative computing and applications. Volume 5:Number 4(2013)
• Page Start: 213
• Page End: 227
• Publication Date: 2013-01-01
• Subjects: rainfall -- forecasting -- C4.5 -- genetic algorithm -- GA -- accurate; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Rainfall prediction is important in many aspects of our economy and general livelihood by preventing any serious natural disasters. Numerous methodologies have been introduced but most of them cannot provide transparency of predicted outcomes. Addressing this problem, a hybrid model combining C4.5 classifier and genetic algorithm (GA), is proposed in this study in order to forecast rainfall of Ranchi City in India. More specifically, C4.5 classifier is run on the past metrological data of local area to extract rule set (knowledge). The generated rule set is then refined by the proposed GA for accurate prediction of rainfall. The model is tested on the test dataset as well as the upcoming day to day. The results show that the integrated model can forecast satisfactorily rainy days and non-rainy days (along with amount of rainfall).