scholarly journals Machine learning approaches to identify thresholds in a heat‐health warning system context

2021 ◽  
Author(s):  
Pierre Masselot ◽  
Fateh Chebana ◽  
Céline Campagna ◽  
Éric Lavigne ◽  
Taha B.M.J. Ouarda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Learning Approaches ◽  
Health Warning ◽  
System Context

scholarly journals Predicting Subseasonal Hydrological Droughts for Swiss Lakes and Large Rivers – Combining Mesoscale Hydrological EPS and Machine Learning Approaches  

2021 ◽  
Author(s):  
Annie Yuan-Yuan Chang ◽  
Simone Jola ◽  
Konrad Bogner ◽  
Daniela I.V. Domeisen ◽  
Massimiliano Zappa
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
River Basins ◽  
Large River ◽  
Water Levels ◽  
Machine Learning Algorithms ◽  
Low Flow ◽  
Drought Event ◽  
Learning Approaches ◽  
Meteorological Forcing

<p>Although Switzerland is not commonly associated with the occurrence of droughts, in recent years, Switzerland has experienced several unprecedented drought events. Considering that many sectors in Switzerland depend heavily on its water resources, hydropower production, navigation and transportation, agriculture, and tourism, it is important to establish a reliable warning system for early drought recognition. Drought forecast at subseasonal timescales, particularly the onset of a drought event, remains a challenge which is linked to the limited skill of subseasonal meteorological forecasts especially in Europe. The goal of this research is to develop a model to produce skillful subseasonal prediction of low-flows in large river basins and water levels of the major lakes in Switzerland. The envisaged methodology combines monthly hydro-meteorological forecast outputs from the hydrological model PREVAH (Precipitation-Runoff-Evapotranspiration HRU model) with machine learning algorithms. An operational setup of PREVAH has been previously implemented for Switzerland with meteorological forcing from 51 ensemble members and 32 days lead time from the operational extended-range prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Although the PREVAH forecasts are considered semi-idealized (assuming natural flow conditions) and they do not go through an in-depth calibration process, they provide a robust representation of the hydrological processes at the catchment level. The proposed machine learning model is expected to mimic the flow routing mechanism and match PREVAH forecasts from its 300 catchments with measured streamflow and lake level in river basins. The proof-of-concept will focus on the river Aare until the station of Brügg-Aegerten, downstream of the lake of Biel. The findings of this work will highlight the potential of directly linking mesoscale hydro-meteorological forecasts with streamflow and providing subseasonal low-flow predictions in an operational set-up.</p>

scholarly journals An additive index model for monitoring weather in a health warning system context

2019 ◽  
Vol 3 ◽  
pp. 263-264
Author(s):  
Masselot P ◽  
Chebana F ◽  
Campagna C ◽  
Lavigne É ◽  
Ouarda T ◽  
...  
Keyword(s):  
Warning System ◽  
Health Warning ◽  
Index Model ◽  
System Context

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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