Nowcasting to Predict Economic Activity in Real Time: The Cases of Belize and El Salvador

2021 ◽  
Author(s):  
Juan José Barrios ◽  
Julia Escobar ◽  
Janelle Leslie ◽  
Lucia Martin ◽  
Werner Peña
Keyword(s):  
Machine Learning ◽  
El Salvador ◽  
Real Time ◽  
Machine Learning Techniques ◽  
Monitoring Tools ◽  
Information Gap ◽  
Learning Techniques ◽  
Ongoing Work ◽  
Macroeconomic Indicators ◽  
Economic Forecasts

This paper presents machine learning models fitted to nowcast or predict quarterly GDP activity in real time for Belize and El Salvador. The initiative is part of the Inter-American Development Bank's (IDB) ongoing effort to develop timely economic monitoring tools following the shock of the Covid-19 pandemic. Nowcasting techniques offer an effective tool to fill the information gap between the end of a quarter and the official publication of macroeconomic indicators that are generally lagged by 60 to 90 days, by exploiting the availability of other indicators that are published more frequently. The results show that machine learning techniques can produce accurate quarterly GDP forecasts for two structurally different economies within economic contexts marked by extreme degrees of volatility and uncertainty at both the national and international levels. Because the calibration of nowcasting exercises is a dynamic process that is refined over time, at the IDB, we trust that this document will help support the ongoing work of the governments and statistical agencies of Belize and El Salvador in securing better economic forecasts to inform agile policy decisions.

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

scholarly journals Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1268 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo ◽  
Yang Li ◽  
Yin Chang
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Real Time ◽  
Yangtze River Basin ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques ◽  
The Yangtze River Basin ◽  
Spatio Temporal

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

scholarly journals Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems

2019 ◽  
Vol 9 (18) ◽  
pp. 3885 ◽  
Author(s):  
Bruno da Silva ◽  
Axel W. Happi ◽  
An Braeken ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Urban Environments ◽  
Machine Learning Techniques ◽  
Embedded Devices ◽  
Sound Classification ◽  
Learning Techniques ◽  
Urban Monitoring ◽  
Constrained Devices

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.

Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization

2021 ◽  
Author(s):  
Asad Mustafa Elmgerbi ◽  
Clemens Peter Ettinger ◽  
Peter Mbah Tekum ◽  
Gerhard Thonhauser ◽  
Andreas Nascimento
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Real Time ◽  
Predictive Models ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Time Rate ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Learning Techniques

Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.

Predicting ocean activity from seismic data using machine learning techniques

2020 ◽  
Author(s):  
Susana Custódio ◽  
Francisco Bolrão ◽  
Tan Bui ◽  
Céline Hadziioannou ◽  
Miguel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Ground Motion ◽  
Seismic Data ◽  
Ambient Noise ◽  
Transfer Functions ◽  
Ocean Waves ◽  
Machine Learning Techniques ◽  
Classical Approach ◽  
Learning Techniques

&lt;p&gt;The most pervasive seismic signal recorded on our planet &amp;#8211; microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. Such imaging is of interest both to climate studies &amp;#8211; by extending the record of oceanic activity back into the early instrumental seismic record &amp;#8211; and to real-time monitoring &amp;#8211; where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.&lt;/p&gt;&lt;p&gt;In our work, we develop empirical transfer functions between seismic observations and ocean activity observations. We start by following the classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy. We explore further developments by considering other seismic data observations &amp;#8211; such as the polarization of seismic ambient noise &amp;#8211; and other indicators of ocean activity observations, including the spectra of ocean waves.&lt;/p&gt;&lt;p&gt;In addition to employing the classical approach of empirical transfer functions, we further present preliminary tests using machine learning techniques to: 1) infer which seismic and ocean activity observables are better predictors of each other, and 2) to predict ocean activity given observed ground motion.&lt;/p&gt;&lt;p&gt;The analysis is made using selected datasets around the North Atlantic, namely using seismic data from North America (west Atlantic), the Azores (central Atlantic) and Portugal (east Atlantic).&lt;/p&gt;&lt;p&gt;This work is supported by FCT through projects UIDB/50019/2020 &amp;#8211; IDL and UTAP-EXPL/EAC/0056/2017 - STORM.&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;div&gt;Bromirski, P. D., Flick, R. E., &amp; Graham, N. (1999). Ocean wave height determined from inland seismometer data: Implications for investigating wave climate changes in the NE Pacific. &lt;em&gt;Journal of Geophysical Research: Oceans&lt;/em&gt;, &lt;em&gt;104&lt;/em&gt;(C9), 20753-20766.&lt;/div&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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