PERBANDINGAN MODEL SIR (SUSCEPTIBLE, INFECTIOUS, RECOVERED), EXPONENTIAL MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING PADA PERAMALAN COVID-19

Novel Coronavirus 2019 (COVID-19) is a disease caused by SARS-CoV-2, COVID-19 is a new type of coronavirus that can be transmitted from human to human. This virus can cause pneumonia, which is inflammation of the lung tissue that causes impaired oxygen exchange, resulting in shortness of breath. Currently it is not known when the Covid-19 pandemic will end, therefore a forecast is needed to predict the spread of Covid-19. This forecasting uses the SIR (Susceptible, Infectious, Recovered), Exponential Moving Average and Single Exponential Smoothing algorithm. Of the three algorithms, which data will be most suitable for forecasting the spread of covid-19 in Indonesia will be compared. The conclusion of the SIR model test results with the PSBB variable inhibits the spread of the virus, the exponential moving average test gets an error value of 24.28% and exponential smoothing gets an error value of 40.07%. So the suitable algorithms used for covid-19 data are the sir model and the exponential moving average.

Investigation of Sentinel-1 Derived Land Subsidence Using Wavelet Tools And Triple Exponential Smoothing Algorithm In Lagos, Nigeria

Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate private borehole and water well proliferation. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series is computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between –94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm which is approximately 23% increase.