Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

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Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

10.1101/2021.11.01.21265527 ◽

2021 ◽

Author(s):

Bruno Barbosa Miranda de Paiva ◽

Polianna Delfino Pereira ◽

Claudio Moises Valiense de Andrade ◽

Virginia Mara Reis Gomes ◽

Maria Clara Pontello Barbosa Lima ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

State Of The Art ◽

Laboratory Data ◽

Machine Learning Algorithms ◽

Training Data ◽

Learning Models ◽

Learning Methods ◽

Meta Learning ◽

Machine Learning Models

Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning

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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Computer Graphics Forum ◽

10.1111/cgf.14034 ◽

2020 ◽

Vol 39 (3) ◽

pp. 713-756 ◽

Cited By ~ 1

Author(s):

A. Chatzimparmpas ◽

R. M. Martins ◽

I. Jusufi ◽

K. Kucher ◽

F. Rossi ◽

...

Keyword(s):

Machine Learning ◽

State Of The Art ◽

The State ◽

Learning Models ◽

Machine Learning Models

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A Systematised State-of-the-Art Review of Machine Learning Models to Aid Clinical Decision-Making in Epilepsy

SSRN Electronic Journal ◽

10.2139/ssrn.3541140 ◽

2020 ◽

Author(s):

Edward Jonathan Han-Burgess ◽

Richard J. Stevens

Keyword(s):

Machine Learning ◽

Decision Making ◽

Clinical Decision Making ◽

State Of The Art ◽

Clinical Decision ◽

Learning Models ◽

Machine Learning Models

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Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model

Information Sciences ◽

10.1016/j.ins.2019.01.076 ◽

2019 ◽

Vol 484 ◽

pp. 302-337 ◽

Cited By ~ 17

Author(s):

Antonio Rafael Sabino Parmezan ◽

Vinicius M.A. Souza ◽

Gustavo E.A.P.A. Batista

Keyword(s):

Machine Learning ◽

Time Series ◽

State Of The Art ◽

Time Series Prediction ◽

The State ◽

Learning Models ◽

Machine Learning Models

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Decoding defect statistics from diffractograms via machine learning

npj Computational Materials ◽

10.1038/s41524-021-00539-z ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Cody Kunka ◽

Apaar Shanker ◽

Elton Y. Chen ◽

Surya R. Kalidindi ◽

Rémi Dingreville

Keyword(s):

Machine Learning ◽

Spatial Distribution ◽

State Of The Art ◽

Structural Information ◽

Atomistic Simulations ◽

Learning Models ◽

Machine Learning Model ◽

Electron Diffractograms ◽

Material Information ◽

Machine Learning Models

AbstractDiffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

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A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5626 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2451-2458

Author(s):

Akansha Bhardwaj ◽

Jie Yang ◽

Philippe Cudré-Mauroux

Keyword(s):

Machine Learning ◽

Real World ◽

Event Detection ◽

State Of The Art ◽

Regularization Parameter ◽

Learning Models ◽

Training Process ◽

Model Training ◽

Real World Datasets ◽

Machine Learning Models

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation of the distribution – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.

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A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data

PLoS ONE ◽

10.1371/journal.pone.0256858 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256858

Author(s):

Giovanni De Toni ◽

Cristian Consonni ◽

Alberto Montresor

Keyword(s):

Machine Learning ◽

The Netherlands ◽

State Of The Art ◽

Data Sources ◽

Learning Models ◽

Personalized Pagerank ◽

Web Searches ◽

The Impact ◽

Machine Learning Models ◽

General Method

Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Estimating in a fast and reliable way the impact of an illness on a given country is essential to plan and organize effective countermeasures, which is now possible by leveraging unconventional data sources like web searches and visits. In this study, we show the feasibility of exploiting machine learning models and information about Wikipedia’s page views of a selected group of articles to obtain accurate estimates of influenza-like illnesses incidence in four European countries: Italy, Germany, Belgium, and the Netherlands. We propose a novel language-agnostic method, based on two algorithms, Personalized PageRank and CycleRank, to automatically select the most relevant Wikipedia pages to be monitored without the need for expert supervision. We then show how our model can reach state-of-the-art results by comparing it with previous solutions.

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