TY - JOUR ID - SisLab1838 UR - http://doi.org/10.1007/978-3-319-38884-7_15 A1 - Nguyen, Hai Chau A1 - Le, Thi Ngoc Anh Y1 - 2016/// N2 - In 2007, repeated outbreaks of cholera in Hanoi have raised the need to have up-to-date evidence on the impact of factors on cholera epidemic, which is essential for developing an early warning system. We have successfully built models to predict cholera outbreaks in Hanoi from 2001 to 2012 using Random Forests method. We found that geographical factors - the number of cholera cases of a district of interest and its neighbours - are very important to predict accurately cholera cases besides the weather factors. Among weather factors, temperature and relative humidity are the most important. We also found that prediction accuracy of our models, measured in adjusted coefficient of determination, will decrease by 0.0076 if prediction length increases by one day. PB - Springer JF - Advanced Computational Methods for Knowledge Engineering Volume 453 of the series Advances in Intelligent Systems and Computing VL - 453 SN - 2194-5357 TI - Using Local Weather and Geographical Information to Predict Cholera Outbreaks in Hanoi, Vietnam SP - 195 AV - public EP - 212 ER -