NDLI: An ensemble of Artificial Neural Networks in Rainfall Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Nagahamulla, H.R.K. Ratnayake, U.R. Ratnaweera, A.
Copyright Year	2012
Description	Author affiliation: Dept of Civil Engineering - Dept of Mechanical Engineering, University of Peradeniya, Sri Lanka (Ratnayake, U.R.; Ratnaweera, A.) \|\| Dept of Computing & Information Systems, Wayamba University of Sri Lanka, Kuliyapitiya, Sri Lanka (Nagahamulla, H.R.K.)
Abstract	Accurate weather forecasts are essential for various human activities. Weather forecasting is a complex process that can exhaust the resources of many computational devices. Out of numerous weather forecasting techniques Artificial Neural Networks (ANN) methodology is one of the most widely used techniques. In this study the application of Neural Network Ensembles in Rainfall Forecasting is investigated by using an Ensemble Neural Network (ENN) to forecast the rainfall in Colombo, Sri Lanka. The ensemble consist of a combination of Multi Layer Feed Forward Network with Back Propagation Algorithm (BPN), Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN). The performance of ensemble is compared with the performance of BPN, RBFN and GRNN. The ANNs are trained, validated and tested using daily observed weather data for 41 years. The results of our experiment show that the performance of the ensemble model is better than the performance of the other models for this application.
Starting Page	176
Ending Page	181
File Size	785333
Page Count	6
File Format	PDF
ISBN	9781467355292
e-ISBN	9781467355308
DOI	10.1109/ICTer.2012.6423032
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	General Regression Neural Network (GRNN) Ensemble Neural Network (ENN) Artificial Neural Networks (ANN) Multi Layer Feed Forward Network (MLFFN) Humidity Artificial neural networks Back Propagation Algorithm (BPN) Radial Basis Function Network (RBFN) Rainfall forecasting
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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