scholarly journals Towards observation based gridded runoff estimates for Europe

2014 ◽  
Vol 11 (11) ◽  
pp. 12883-12932 ◽  
Author(s):  
L. Gudmundsson ◽  
S. I. Seneviratne
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Spatial Scales ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Continental Scale ◽  
Learning Techniques ◽  
Terrestrial Water ◽  
Model Training ◽  
Small Catchments

Abstract. Terrestrial water variables are the key to understanding ecosystem processes, feed back on weather and climate, and are a prerequisite for human activities. To provide context for local investigations and to better understand phenomena that only emerge at large spatial scales, reliable information on continental scale freshwater dynamics is necessary. To date streamflow is among the best observed variables of terrestrial water systems. However, observation networks have a limited station density and often incomplete temporal coverage, limiting investigations to locations and times with observations. This paper presents a methodology to estimate continental scale runoff on a 0.5° spatial grid with monthly resolution. The methodology is based on statistical up-scaling of observed streamflow from small catchments in Europe and exploits readily available gridded atmospheric forcing data combined with the capability of machine learning techniques. The resulting runoff estimates are validated against (1) runoff from small catchments that were not used for model training, (2) river discharge from nine continental scale river basins and (3) independent estimates of long-term mean evapotranspiration at the pan-European scale. In addition it is shown that the produced gridded runoff compares on average better to observations than a multi-model ensemble of comprehensive Land Surface Models (LSMs), making it an ideal candidate for model evaluation and model development. In particular, the presented machine learning approach may help determining which factors are most relevant for an efficient modelling of runoff at regional scales. Finally, the resulting data product is used to derive a comprehensive runoff-climatology for Europe and its potential for drought monitoring is illustrated.

scholarly journals Towards observation-based gridded runoff estimates for Europe

2015 ◽  
Vol 19 (6) ◽  
pp. 2859-2879 ◽  
Author(s):  
L. Gudmundsson ◽  
S. I. Seneviratne
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Spatial Scales ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Continental Scale ◽  
Learning Techniques ◽  
Terrestrial Water ◽  
Model Training ◽  
Small Catchments

Abstract. Terrestrial water variables are the key to understanding ecosystem processes, feed back on weather and climate, and are a prerequisite for human activities. To provide context for local investigations and to better understand phenomena that only emerge at large spatial scales, reliable information on continental-scale freshwater dynamics is necessary. To date streamflow is among the best-observed variables of terrestrial water systems. However, observation networks have a limited station density and often incomplete temporal coverage, limiting investigations to locations and times with observations. This paper presents a methodology to estimate continental-scale runoff on a 0.5° spatial grid with monthly resolution. The methodology is based on statistical upscaling of observed streamflow from small catchments in Europe and exploits readily available gridded atmospheric forcing data combined with the capability of machine learning techniques. The resulting runoff estimates are validated against (1) runoff from small catchments that were not used for model training, (2) river discharge from nine continental-scale river basins and (3) independent estimates of long-term mean evapotranspiration at the pan-European scale. In addition it is shown that the produced gridded runoff compares on average better to observations than a multi-model ensemble of comprehensive land surface models (LSMs), making it an ideal candidate for model evaluation and model development. In particular, the presented machine learning approach may help determining which factors are most relevant for an efficient modelling of runoff at regional scales. Finally, the resulting data product is used to derive a comprehensive runoff climatology for Europe and its potential for drought monitoring is illustrated.

scholarly journals Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

1999 ◽  
Vol 6 (5-6) ◽  
pp. 253-265 ◽  
Author(s):  
R.E. Abdel-Aal ◽  
M. Raashid
Keyword(s):  
Machine Learning ◽  
Online Monitoring ◽  
Model Development ◽  
Low Frequency ◽  
Vibration Monitoring ◽  
Machine Learning Techniques ◽  
Vibration Signals ◽  
Output Performance ◽  
Learning Techniques ◽  
The Status

Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50-channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks.

scholarly journals Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

2017 ◽  
Author(s):  
Qi Wang ◽  
Xia Zhao ◽  
Jincai Huang ◽  
Yanghe Feng ◽  
Zhong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Concept Drift ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Robust Learning ◽  
Human Interactions ◽  
Learning Techniques ◽  
History Of

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

scholarly journals Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

2017 ◽  
Author(s):  
Qi Wang ◽  
Xia Zhao ◽  
Jincai Huang ◽  
Yanghe Feng ◽  
Jiahao Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Concept Drift ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Robust Learning ◽  
Human Interactions ◽  
Learning Techniques ◽  
History Of

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

scholarly journals A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

Sports ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Alessio Rossi ◽  
Luca Pappalardo ◽  
Paolo Cintia
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Testing Machine ◽  
Narrative Review ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Sports Science ◽  
Box Models ◽  
Learning Techniques ◽  
Black Box Models

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.

scholarly journals mosquitoes.pdf

2019 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

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