Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Implementing the New Structural Model of the Czech National Bank

The purpose of the paper is to introduce the new “g3†structural model of the Czech National Bank and illustrate how it is used for forecasting and policy analysis. As from January 2007 the model was regularly used for shadowing official forecasts, and in July 2008 it became the core model of the CNB. In the paper we highlight the most important and unusual features of the model and discuss tools and procedures that help us in forecasting and assessing the economy with the model. The paper is not meant to provide a full derivation of the model or the complete characteristics of its behavior and should not be regarded as model documentation. Rather, the paper demonstrates how the model is used and how it contributes to policy analysis.DSGE, filtering, forecasting, general equilibrium, monetary policy.

A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

This paper evaluates the market penetration of cars with alternative fuelpowertrain technologies in Italy under various scenarios. Seven cars on sale in 2013 are considered: the Ford Fiesta (diesel), the VW Polo (gasoline), the Fiat Punto Evo (bi-fuel \u2013 CNG), the Natural Power Alfa Romeo Mito (bi-fuel \u2013 LPG), the Toyota Yaris (hybrid \u2013 gasoline), the Peugeot iOn (BEV \u2013 owned battery), the Renault Zoe (BEV \u2013 leased battery). A Mixed Error Component Logit model is estimated based on data collected via a stated preference choice survey administered in 2013 in various Italian cities. The model's parameters are then used to build a Monte Carlo simulation model which allows evaluating, under different scenarios, the market penetration of the seven cars. The main findings are that (a) the subsidies enacted by the Italian government in favour of the low CO2 emitting cars appear to favour mostly the Ford Fiesta (diesel); (b) a three-fold increase in the BEVs range would not change their market share significantly (about 2%); and (c) only a combination of changes such as the introduction of a subsidy equal to \u20ac5000, the decrease of the purchase price for BEVs by \u20ac5000, the increase in the battery range, and the increase in the conventional fuel price would significantly increase the BEVs' market share, raising it to about 15%

A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries

Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon