Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future is also needed. Rainfall included category of time series data. One methods that be used Autoregressive Integrated Moving Average (ARIMA) Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. factors affect wind spe...

The necessity for transportation research has been prompted by the exponential rise in vehicle use, research in the field has expanded and diversified accordingly. Despite substantial work having been carried out in Macroscopic modelling, the theory is far from complete [1]. The paper presents a hybrid model which combines the strengths of macroscopic models; the continuum method with forecasti...

Padang city has been in the red zone (high risk) and orange (medium against transmission of Covid-19 virus for several months. This is due to lack community discipline complying with health protocols. The existence Andalas University Hospital Laboratory City which tools issue SWAB test results also data being obtained very quickly collection more accurate. To predict number new cases patients, ...

India is basically an agricultural country and the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. The average annual or seasonal rainfall at a place does not give sufficient information regarding its capacity to support crop production. Rainfall distribution pattern is the most important. The rainfall forecasting is scientificall...

The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airl...