IMPLEMENTATION OF THE STEP FUNCTION INTERVENTION AND EXTREME LEARNING MACHINE FOR FORECASTING THE PASSENGER’S AIRPORT IN SORONG

This study aims to forecast the number of passengers departing at the domestic departure terminal at Domine Eduard Osok Sorong Airport in 2022 using the Autoregressive Integrated Moving Average (ARIMA) method, ARIMA with Step Function Intervention, and Extreme Learning Machine (ELM). The knowledge of the number of passengers can help the airport prepare facilities. The residual ARIMA model (0,1,0) has no serial correlation (random walk) based on the Ljung-Box test. The MAPE value of the ARIMA model (0,1,0) is 65.47% which means poorly fitted. Because of it, the researchers propose an intervention in the ARIMA model. The RMSE and MAPE ARIMA Intervention ​​(1,0,0) (0,1,0) [12] were 9,027.671 and 35.86%, respectively. Besides, this study also employed the ELM method, which has a MAPE error measurement value of 30.64%. The ELM method has the lowest error measurement results among the three methods. Therefore, the ELM method is suitable for forecasting the number of passengers with predicted values ​​from June to September 2022 as follows: 47985, 37821, 31247, and 33578. On the other hand, intervention in ARIMA can reduce MAPE by 45%

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Improved Streamflow Simulation through Ensemble and Stochastic Conceptual Data-driven Approaches

To better understand the complexities of water movement on earth, hydrologists have developed process-based hydrological models (HMs) and data-driven models (DDMs), both of which have been applied to a host of water resources applications (e.g., flood forecasting, reservoir operations, drought monitoring, hydraulic design). HMs attempt to simplify hydrological processes of interest (e.g., snowmelt, sub-surface flow), while DDMs estimate statistical relationships between explanatory/input and response/target variables using historical data. Traditionally, HMs and DDMs have been developed independently, however, there has been growing interest in using DDMs to improve HM simulations. Among various approaches for combining process-based theory with DDMs, the conceptual data-driven approach (CDDA) was recently proposed, where DDMs are used to correct the residuals (errors) stemming from ensemble HMs. The CDDA was shown to substantially reduce the simulation uncertainty. Since the CDDA only accounts for the HM parameter uncertainty, a subsequent study introduced the stochastic CDDA (SCDDA) to account for various sources of uncertainty (i.e., input data, input variable selection, parameters, and model output). However, the (original) SCDDA used HMs as input to the DDMs within a stochastic framework, thus, estimating the uncertainty of the DDMs, not the CDDA. Here, a new SCDDA is introduced where the CDDA uncertainty is estimated instead of the DDM uncertainty (as in the original SCDDA) by taking advantage of the multiple parameter sets generated by the CDDA through a stochastic framework. Hence, the new SCDDA serves as the second stage in post-processing HMs, where the stochastic framework can be used to improve the CDDA simulations. The new SCDDA is tested in a daily streamflow simulation case study using three Swiss catchments where it is benchmarked against the CDDA as well as ensemble and stochastic HMs. In total, nine HM-DDM combinations (variants) are explored within the CDDA and SCDDA based on three popular HMs and three state-of-the-art DDMs. The ensemble and stochastic HMs are based on the same three HMs used in the CDDA and SCDDA. A total of 34 years of daily streamflow, precipitation, maximum, minimum, and mean air temperatures, and potential evapotranspiration time series were partitioned into warm-up, calibration/training, validation, and test sets for model development and assessment. Several deterministic (mean absolute error, root mean squared error, Nash Sutcliffe Efficiency, Kling Gupta Efficiency (KGE), and percent bias) and probabilistic (mean continuous ranked probability score (CRPS), alpha index (α_R), and average width) performance metrics, as well as graphical tools (e.g., time series plots, raincloud plots, coverage probability plots (CPP)), were used to assess the simulations and compare the various models. The CDDA improved the CRPS of the ensemble HM by 18-69%, and the new SCDDA further improved the CRPS of the CDDA by up to 15%. However, it was found that the SCDDA could not improve the reliability of any CDDA variants that had an α_R above 0.85. Since the computational requirements of the CDDA and SCDDA can be significant, the effect of ensemble size on model performance was analyzed and revealed that approximately 100 (ensemble) members, for both ensemble and stochastic models, could be used without sacrificing performance. Finally, to test whether the CDDA and SCDDA can account for important processes missing within an HM, both approaches adopt an HM with and without a snow routine and are tested in a snow-driven catchment. It is found that both cases (with and without snow) had negligible difference in performance suggesting that the CDDA and SCDDA may account for missing processes in HMs