relation: https://khub.utp.edu.my/scholars/7262/
title: Predicting localized dengue incidences using ensemble system identification
creator: Loshini, T.
creator: Asirvadam, V.S.
creator: Dass, S.C.
creator: Gill, B.S.
description: 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. © 2015 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Loshini, T. and Asirvadam, V.S. and Dass, S.C. and Gill, B.S.  (2016) Predicting localized dengue incidences using ensemble system identification.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963655905&doi=10.1109%2fIC3INA.2015.7377737&partnerID=40&md5=015a4e85be3d4153f892645cf5ff7fb6
relation: 10.1109/IC3INA.2015.7377737
identifier: 10.1109/IC3INA.2015.7377737