scholarly journals A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas

2022 ◽  
Vol 12 (2) ◽  
pp. 583
Author(s):  
Alessandro Rocchi ◽  
Andrea Chiozzi ◽  
Marco Nale ◽  
Zeljana Nikolic ◽  
Fabrizio Riguzzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Developed Countries ◽  
Disaster Mitigation ◽  
Machine Learning Techniques ◽  
Hazard Management ◽  
Hazard Risk ◽  
Reduction Strategies ◽  
Emilia Romagna Region ◽  
Risk Reduction Strategies

Communities are confronted with the rapidly growing impact of disasters, due to many factors that cause an increase in the vulnerability of society combined with an increase in hazardous events such as earthquakes and floods. The possible impacts of such events are large, also in developed countries, and governments and stakeholders must adopt risk reduction strategies at different levels of management stages of the communities. This study is aimed at proposing a sound qualitative multi-hazard risk analysis methodology for the assessment of combined seismic and hydraulic risk at the regional scale, which can assist governments and stakeholders in decision making and prioritization of interventions. The method is based on the use of machine learning techniques to aggregate large datasets made of many variables different in nature each of which carries information related to specific risk components and clusterize observations. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of relative levels of combined risk. The proposed approach proves to be robust and delivers a very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling at the regional scale.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

2018 ◽  
Vol 27 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Athanasios Tagaris ◽  
Dimitrios Kollias ◽  
Andreas Stafylopatis ◽  
Georgios Tagaris ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disorder ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Network Architectures ◽  
Imaging Data ◽  
Learning Techniques ◽  
Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

scholarly journals A scoring test on probabilistic seismic hazard estimates in Italy

2015 ◽  
Vol 15 (1) ◽  
pp. 171-186 ◽  
Author(s):  
D. Albarello ◽  
L. Peruzza ◽  
V. D'Amico
Keyword(s):  
Seismic Hazard ◽  
Computational Models ◽  
Regional Scale ◽  
Return Time ◽  
Basic Element ◽  
Suitable Model ◽  
Average Return ◽  
Reduction Strategies ◽  
Risk Reduction Strategies ◽  
Better Than

Abstract. Probabilistic estimates of seismic hazard represent a basic element for seismic risk reduction strategies and they are a key element of seismic regulation. Thus, it is important to select the most effective estimates among the available ones. An empirical scoring strategy is described here and is applied to a number of time-independent hazard estimates available in Italy both at national and regional scale. The scoring test is based on the comparison of outcomes provided by available computational models at a number of accelerometric sites where observations are available for 25 years. This comparison also allows identifying computational models that, providing outcomes that are in contrast with observations, should thus be discarded. The analysis shows that most of the hazard estimates proposed for Italy are not in contrast with observations and some computational models perform significantly better than others do. Furthermore, one can see that, at least locally, older estimates can perform better than the most recent ones. Finally, since the same computational model can perform differently depending on the region considered and on average return time of concern, no single model can be considered as the best-performing one. This implies that, moving along the hazard curve, the most suitable model should be selected by considering the specific problem of concern.

Predictive modelling of groundwater nitrate pollution at a regional scale using machine learning and feature selection

2020 ◽  
Author(s):  
Aaron Cardenas-Martinez ◽  
Victor Rodriguez-Galiano ◽  
Juan Antonio Luque-Espinar ◽  
Maria Paula Mendes
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Vegetation Index ◽  
Regional Scale ◽  
Developed Countries ◽  
Quality Standard ◽  
Nitrate Pollution ◽  
Learning Approaches ◽  
Environmental Measures

<p>The establishment of the sources and driven-forces of groundwater nitrate pollution is of paramount importance, contributing to agro-environmental measures implementation and evaluation. High concentrations of nitrates in groundwater occur all around the world, in rich and less developed countries.</p><p>In the case of Spain, 21.5% of the wells of the groundwater quality monitoring network showed mean concentrations above the quality standard (QS) of 50 mg/l. The objectives of this work were: i) to predict the current probability of having nitrate concentrations above the QS in Andalusian groundwater bodies (Spain) using past time features, being some of them obtained from satellite observations; ii) to assess the importance of features in the prediction; iii) to evaluate different machine learning approaches (ML) and feature selection techniques (FS).</p><p>Several predictive models based on an ML algorithm, the Random Forest, were used, as well as, FS techniques. 321 nitrate samples and respective predictive features were obtained from different groundwater bodies. These predictive features were divided into three groups, regarding their focus: agricultural production (phenology); livestock pressure (excretion rates); and environmental settings (soil characteristics and texture, geomorphology, and local climate conditions). Models were trained with the features of a year [YEAR (t<sub>0</sub>)], and then applied to new features obtained for the next year – [YEAR(t<sub>0+1</sub>)], performing k-fold cross-validation. Additionally, a further prediction was carried out for a present time – [YEAR(t<sub>0+n</sub>)], validating with an independent test. This methodology examined the use of a model, trained with previous nitrates concentrations and predictive features, for the prediction of current nitrates concentrations based on present features. Our findings showed an improvement in the predictive performance when using a wrapper with sequential search for FS when compared to the use alone of the Random Forest algorithm. Phenology features, derived from remotely sensed variables, were the most explanative features, performing better than the use of static land-use maps or vegetation index images (e.g., NDVI). They also provided much more comprehensive information, and more importantly, employing only extrinsic features of groundwater bodies.</p>

scholarly journals Age of the entrepreneurial decision: Differences among developed, developing, and non- developed countries

2018 ◽  
Vol 7 (1) ◽  
pp. 36 ◽  
Author(s):  
Alicia Coduras ◽  
Jorge Velilla ◽  
Raquel Ortega
Keyword(s):  
Machine Learning ◽  
Social Norms ◽  
Survey Data ◽  
Population Survey ◽  
Adult Population ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Entrepreneurial Decision ◽  
The Relationship

Although entrepreneurship is widely considered an engine of growth, it is not clear whether policies, de facto, promote it, and knowing which individuals are willing to become entrepreneurs could help in the design of those policies. In this paper, we study how individuals become entrepreneurs at different ages, according to the degree of development of the country of residence. We make use of the GEM 2014 Adult Population Survey data, against a background where social norms are controlled, to find that the relationship between entrepreneurship and age follows an inverted U-shape, according to machine learning techniques, and that younger individuals are the most willing to become entrepreneurs.

scholarly journals Air Temperature Forecasting Using Machine Learning Techniques: A Review

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4215 ◽  
Author(s):  
Jenny Cifuentes ◽  
Geovanny Marulanda ◽  
Antonio Bello ◽  
Javier Reneses
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Regional Scale ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Industrial Sectors ◽  
Learning Techniques ◽  
Temperature Forecasting ◽  
The Government

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

scholarly journals Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 339 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Yangtze River ◽  
Quality Evaluation ◽  
Yangtze River Basin ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Polluted Water ◽  
The Yangtze River ◽  
Water Quality Evaluation

Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful.

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