scholarly journals Random Forest versus Logit Models: Which Offers Better Early Warning of Fiscal Stress?

2021 ◽  
Author(s):  
Barbara Jarmulska
Keyword(s):  
Random Forest ◽  
Early Warning ◽  
Fiscal Stress ◽  
Logit Models

scholarly journals Signalling Fiscal Stress in the Euro Area -- A Country-Specific Early Warning System

2014 ◽  
Author(s):  
Pablo Hernández de Cos ◽  
Gerrit B. Koester ◽  
Enrique Moral-Benito ◽  
Christiane Nickel
Keyword(s):  
Euro Area ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Fiscal Stress ◽  
Country Specific

scholarly journals Seasonal forecasting of hydrological drought in the Limpopo basin: A comparison of statistical methods.

2016 ◽  
Author(s):  
Mathias Seibert ◽  
Bruno Merz ◽  
Heiko Apel
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Early Warning ◽  
Linear Models ◽  
Regression Trees ◽  
Forecast Skill ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Operating Characteristics ◽  
Random Forest Regression

Abstract. The Limpopo basin in southern Africa is prone to droughts, which affect the livelihoods of millions of people in South Africa, Botswana, Zimbabwe, and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed with statistical approaches. Three methods (Multiple Linear Models, Artifical Neural Networks, Random Forest Regression Trees) are compared in terms of their ability to forecast streamflow with up to 12 months lead time. The following four main findings result from the study. 1) There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high interstation differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2) A large range of potential predictors is considered in this study, comprising well established climate indices, customised teleconnection indices derived from sea surface temperatures, and antecedent streamflow as proxy of catchment conditions. El-Niño and customised indices, representing sea surface temperature in the Atlantic and Indian Ocean, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3) Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and Random Forest regression trees, despite their capabilities to represent non-linear relationships. 4) Employed in early warning the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROC). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them complementary to existing forecasts in order to strengthen preparedness for droughts.

scholarly journals Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

2017 ◽  
Vol 21 (3) ◽  
pp. 1611-1629 ◽  
Author(s):  
Mathias Seibert ◽  
Bruno Merz ◽  
Heiko Apel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Early Warning ◽  
Linear Models ◽  
Regression Trees ◽  
Forecast Skill ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Random Forest Regression

Abstract. The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.

scholarly journals Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

2021 ◽  
Vol 12 ◽  
Author(s):  
Shakti Davis ◽  
Lauren Milechin ◽  
Tejash Patel ◽  
Mark Hernandez ◽  
Greg Ciccarelli ◽  
...  
Keyword(s):  
Random Forest ◽  
Early Warning ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Wearable Devices ◽  
Contact Tracing ◽  
Physiological Data ◽  
Non Invasive ◽  
Pathogen Exposure ◽  
Human Primate

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices.Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device.Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001.Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.

scholarly journals Signalling Fiscal Stress in the Euro Area: A Country-Specific Early Warning System

2014 ◽  
Author(s):  
Pablo Hernnndez de Cos ◽  
Enrique Moral-Benito ◽  
Gerrit B. Koester ◽  
Christiane Nickel
Keyword(s):  
Euro Area ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Fiscal Stress ◽  
Country Specific

scholarly journals Early-Warning System for Cyclone-Induced Wave Overtopping Aided by a Suite of Random Forest Approaches

2020 ◽  
pp. 437-451
Author(s):  
Jeremy Rohmer ◽  
Sophie Lecacheux ◽  
Rodrigo Pedreros ◽  
Deborah Idier ◽  
François Bonnardot
Keyword(s):  
Random Forest ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Wave Overtopping
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