Predicting intra‐urban well‐being from space with non‐linear machine learning

2021 ◽  
Author(s):  
Piotr Wójcik ◽  
Krystian Andruszek
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Non Linear ◽  
Linear Machine

scholarly journals A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

2021 ◽  
Vol 13 (4) ◽  
pp. 2054 ◽  
Author(s):  
Angela Gorgoglione ◽  
Alberto Castro ◽  
Vito Iacobellis ◽  
Andrea Gioia
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Computational Time ◽  
Self Organizing Map ◽  
Nutrient Runoff ◽  
Non Linear ◽  
Linear Technique ◽  
Linear Machine

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

scholarly journals Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning

2020 ◽  
Author(s):  
Christoph M. Kanzler ◽  
Ilse Lamers ◽  
Peter Feys ◽  
Roger Gassert ◽  
Olivier Lambercy
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Clinical Data ◽  
Linear Models ◽  
Digital Health ◽  
Learning Models ◽  
Rehabilitation Outcomes ◽  
Non Linear ◽  
Linear Machine ◽  
Machine Learning Models

AbstractBackgroundA personalized prediction of upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) promises to optimize the allocation of therapy and to stratify individuals for resource-demanding clinical trials. Previous research identified predictors on a population level through linear models and clinical data, including conventional assessments describing sensorimotor impairments. The objective of this work was to explore the feasibility of providing an individualized and more accurate prediction of rehabilitation outcomes in pwMS by leveraging non-linear machine learning models, clinical data, and digital health metrics characterizing sensorimotor impairments.MethodsClinical data and digital health metrics were recorded from eleven pwMS undergoing neurorehabilitation. Machine learning models were trained on data recorded pre-intervention. The dependent variables indicated whether a considerable improvement on the activity level was observed across the intervention or not (binary classification), as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT).ResultsIn a cross-validation, considerable improvements in ARAT or BBT could be accurately predicted (94% balanced accuracy) by only relying on patient master data. Considerable improvements in NHPT could be accurately predicted (89% balanced accuracy), but required knowledge about sensorimotor impairments. Assessing these with digital health metrics instead of conventional scales allowed increasing the balanced accuracy by +17% . Non-linear machine-learning models improved the predictive accuracy for the NHPT by +25% compared to linear models.ConclusionsThis work demonstrates the feasibility of a personalized prediction of upper limb neurorehabilitation outcomes in pwMS using multi-modal data collected before neurorehabilitation and machine learning. Information from digital health metrics about sensorimotor impairment was necessary to predict changes in dexterous hand control, thereby underlining their potential to provide a more sensitive and fine-grained assessment than conventional scales. Non-linear models outperformed ones, suggesting that the commonly assumed linearity of neurorehabilitation is oversimplified.clinicaltrials.gov registration number: NCT02688231

Multi-task learning framework for predicting water quality using non-linear machine learning technique

2021 ◽  
pp. 1-13
Author(s):  
D. Senthilkumar ◽  
D. George Washington ◽  
A.K. Reshmy ◽  
M. Noornisha
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Machine Learning Technique ◽  
Water Quality Prediction ◽  
Task Learning ◽  
Learning Technique ◽  
Non Linear ◽  
Linear Machine

Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features.

Prediction of near-surface temperatures using a non-linear machine learning post-processing model

2021 ◽  
Author(s):  
Emy Alerskans ◽  
Joachim Nyborg ◽  
Morten Birk ◽  
Eigil Kaas
Keyword(s):  
Machine Learning ◽  
Air Temperature ◽  
Weather Prediction ◽  
Model Output ◽  
Post Processing ◽  
Near Surface ◽  
Coarse Resolution ◽  
Non Linear ◽  
Nwp Model ◽  
Linear Machine

<p>Numerical weather prediction (NWP) models are known to exhibit systematic errors, especially for near-surface variables such as air temperature. This is partly due to deficiencies in the physical formulation of the model dynamics and the inability of these models to successfully handle sub-grid phenomena. Forecasts that better match the locally observed weather can be obtained by post-processing NWP model output using local meteorological observations. Here, we have implemented a non-linear post-processing model based on machine learning techniques with the aim of post-processing near-surface air temperature forecasts from a global coarse-resolution model in order to produce localized forecasts. The model is trained on observational from a network of private weather stations and forecast data from the global coarse-resolution NWP model. Independent data is used to assess the performance of the model and the results are compared with the performance of the raw NWP model output. Overall, the non-linear machine learning post-processing method reduces the bias and the standard deviation compared to the raw NWP forecast and produces a forecast that better match the locally observed weather.</p>

scholarly journals Combining Internal- and External-Training-Loads to Predict Non-Contact Injuries in Soccer

2020 ◽  
Vol 10 (15) ◽  
pp. 5261
Author(s):  
Emmanuel Vallance ◽  
Nicolas Sutton-Charani ◽  
Abdelhak Imoussaten ◽  
Jacky Montmain ◽  
Stéphane Perrey
Keyword(s):  
Machine Learning ◽  
External Load ◽  
Injury Risk ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Internal Load ◽  
Injury Prediction ◽  
Non Linear

The large amount of features recorded from GPS and inertial sensors (external load) and well-being questionnaires (internal load) can be used together in a multi-dimensional non-linear machine learning based model for a better prediction of non-contact injuries. In this study we put forward the main hypothesis that the use of such models would be able to inform better about injury risks by considering the evolution of both internal and external loads over two horizons (one week and one month). Predictive models were trained with data collected by both GPS and subjective questionnaires and injury data from 40 elite male soccer players over one season. Various classification machine-learning algorithms that performed best on external and internal loads features were compared using standard performance metrics such as accuracy, precision, recall and the area under the receiver operator characteristic curve. In particular, tree-based algorithms based on non-linear models with an important interpretation aspect were privileged as they can help to understand internal and external load features impact on injury risk. For 1-week injury prediction, internal load features data were more accurate than external load features while for 1-month injury prediction, the best performances of classifiers were reached by combining internal and external load features.

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