An Ensemble-based Neural Network Model for Natural Disaster in 2019

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.