@inproceedings{scholars7262,
           pages = {6--11},
            year = {2016},
           title = {Predicting localized dengue incidences using ensemble system identification},
         journal = {Proceeding - 2015 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends in the Era of Internet of Things, IC3INA 2015},
             doi = {10.1109/IC3INA.2015.7377737},
            note = {cited By 7; Conference of International Conference on Computer, Control, Informatics and Its Applications, IC3INA 2015 ; Conference Date: 5 October 2015 Through 7 October 2015; Conference Code:118992},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
            isbn = {9781479987733},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963655905&doi=10.1109\%2fIC3INA.2015.7377737&partnerID=40&md5=015a4e85be3d4153f892645cf5ff7fb6},
          author = {Loshini, T. and Asirvadam, V. S. and Dass, S. C. and Gill, B. S.},
        abstract = {Neighbouring regions do have an influence on the pattern of dengue incidences for a particular site. This study presents ensemble models, where dengue incidences of a district can be estimated using a dengue prediction model of that district together with its neighbouring districts. Seven districts of Selangor are chosen in this study and an ensemble dengue incidence prediction model is built based on the respective districts and its neighbours. Dengue incidence models for each district are developed using predictor variables which include previous dengue incidences and weather variables (namely, the mean temperature, relative humidity and cumulative rainfall). These predictors are found to have specific model order lag time. To measure the efficiency of ensemble models, the formed ensemble model for each district is compared with their respective single model using the Mean Square Error (MSE) criteria. It was found that out of seven districts, five districts had better prediction accuracy based on their ensemble models. Hence, we conclude that ensemble models predict dengue incidences well. {\^A}{\copyright} 2015 IEEE.},
        keywords = {Atmospheric humidity; Forecasting; Identification (control systems); Information science; Rain; Religious buildings, Cumulative rainfall; Dengue Incidences; Ensemble modeling; Ensemble systems; Mean temperature; Prediction accuracy; Prediction model; Predictor variables, Mean square error}
}