TY  - CONF
Y1  - 2021///
SN  - 9781665435925
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126726924&doi=10.1109%2fICSIPA52582.2021.9576776&partnerID=40&md5=c48f70a56717720b7fa7d64872369969
A1  - Thiruchelvam, L.
A1  - Dass, S.C.
A1  - Mathur, N.
A1  - Asirvadam, V.S.
A1  - Gill, B.S.
AV  - none
N2  - This study aimed to build best dengue cases prediction model for Petaling district, in Selangor. Linear Least Square estimation method is used to build the models and Mean Square Error (MSE) and Akaike Information Criterion (AIC) value is used as tool of comparison between models. Prior to model development, the respective variables are first normalized, using 0-1 normalization procedure. Next, significant predictors are identified from weather variables namely mean temperature, relative humidity, and rainfall. Thirdly, feedback data was included and identified if could yield better prediction models. Few model orders of lag time are built simultaneously, and the most accurate prediction model was selected for Petaling district. Study found dengue prediction models including all three climate variables of mean temperature, relative humidity, cumulative rainfall and together with previous dengue cases to have the lowest MSE and AIC values. This is aligned with previous studies which selected model with climate and previous dengue cases models as best model fit. Thus, study proposed future studies to incorporate all three climate variables and previous dengue cases while developing dengue prediction models. © 2021 IEEE
N1  - cited By 1; Conference of 7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021 ; Conference Date: 13 September 2021 Through 15 September 2021; Conference Code:176997
KW  - Climate models; Forecasting; Least squares approximations; Rain
KW  -  Akaike information criteria; Akaike's information criterions; Cumulative rainfall; Dengue prediction model; Estimation methods; Least square estimation method; Least squares estimation; Mean square error; Mean temperature; Means square errors; Prediction modelling
KW  -  Mean square error
ID  - scholars15353
TI  - Inclusion of Climate Variables for Dengue Prediction Model: Preliminary Analysis
ER  -