An Ensemble of Artificial Neural Networks in Rainfall Forecasting

Harshani Nagahamulla, Uditha Ratnayake, Asanga Ratnaweera

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.

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