Projecting conflict risk following the Shared Socioeconomic pathways: what role for water stress and climate?

2021 ◽  
Author(s):  
Sophie de Bruin ◽  
Jannis Hoch ◽  
Nina von Uexkull ◽  
Halvard Buhaug ◽  
Nico Wanders
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Related Factors ◽  
Socioeconomic Impacts ◽  
Learning Techniques ◽  
The Future ◽  
Conflict Risk

<p>The socioeconomic impacts of changes in climate-related and hydrology-related factors are increasingly acknowledged to affect the on-set of violent conflict. Full consensus upon the general mechanisms linking these factors with conflict is, however, still limited. The absence of full understanding of the non-linearities between all components and the lack of sufficient data make it therefore hard to address violent conflict risk on the long-term. </p><p>Although it is neither desirable nor feasible to make exact predictions, projections are a viable means to provide insights into potential future conflict risks and uncertainties thereof. Hence, making different projections is a legitimate way to deal with and understand these uncertainties, since the construction of diverse scenarios delivers insights into possible realizations of the future.  </p><p>Through machine learning techniques, we (re)assess the major drivers of conflict for the current situation in Africa, which are then applied to project the regions-at-risk following different scenarios. The model shows to accurately reproduce observed historic patterns leading to a high ROC score of 0.91. We show that socio-economic factors are most dominant when projecting conflicts over the African continent. The projections show that there is an overall reduction in conflict risk as a result of increased economic welfare that offsets the adverse impacts of climate change and hydrologic variables. It must be noted, however, that these projections are based on current relations. In case the relations of drivers and conflict change in the future, the resulting regions-at-risk may change too.   By identifying the most prominent drivers, conflict risk mitigation measures can be tuned more accurately to reduce the direct and indirect consequences of climate change on the population in Africa. As new and improved data becomes available, the model can be updated for more robust projections of conflict risk in Africa under climate change.</p>

Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database

2021 ◽  
Author(s):  
Nikos Fazakis ◽  
Elias Dritsas ◽  
Otilia Kocsis ◽  
Nikos Fakotakis ◽  
Konstantinos Moustakas
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques

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 Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3358
Author(s):  
Patrik Sleziak ◽  
Roman Výleta ◽  
Kamila Hlavčová ◽  
Michaela Danáčová ◽  
Milica Aleksić ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Hydrological Modeling ◽  
Performance Metrics ◽  
Climate Change Scenarios ◽  
Reference Period ◽  
Changing Climate ◽  
Time Horizons ◽  
The Future

The changing climate is a concern with regard to sustainable water resources. Projections of the runoff in future climate conditions are needed for long-term planning of water resources and flood protection. In this study, we evaluate the possible climate change impacts on the runoff regime in eight selected basins located in the whole territory of Slovakia. The projected runoff in the basins studied for the reference period (1981–2010) and three future time horizons (2011–2040, 2041–2070, and 2071–2100) was simulated using the HBV (Hydrologiska Byråns Vattenbalansavdelning) bucket-type model (the TUW (Technische Universität Wien) model). A calibration strategy based on the selection of the most suitable decade in the observation period for the parameterization of the model was applied. The model was first calibrated using observations, and then was driven by the precipitation and air temperatures projected by the KNMI (Koninklijk Nederlands Meteorologisch Instituut) and MPI (Max Planck Institute) regional climate models (RCM) under the A1B emission scenario. The model’s performance metrics and a visual inspection showed that the simulated runoff using downscaled inputs from both RCM models for the reference period represents the simulated hydrological regimes well. An evaluation of the future, which was performed by considering the representative climate change scenarios, indicated that changes in the long-term runoff’s seasonality and extremality could be expected in the future. In the winter months, the runoff should increase, and decrease in the summer months compared to the reference period. The maximum annual daily runoff could be more extreme for the later time horizons (according to the KNMI scenario for 2071–2100). The results from this study could be useful for policymakers and river basin authorities for the optimum planning and management of water resources under a changing climate.

OPTIONS FOR ASSESSING THE SUITABILITY OF URBAN ENVIRONMENTAL ACUPUNCTURE

2021 ◽  
Author(s):  
Barbara Vojvodíková ◽  
Jiří Kupka ◽  
Adéla Brázdová ◽  
Radim Fojtík ◽  
Iva Tichá
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Mitigation Measures ◽  
Small Scale ◽  
Financial Sustainability ◽  
Technical Solution ◽  
The City

To increase their resilience to climate change, cities are looking to apply elements of urban environmental acupuncture. The essence of such measures is many smaller sites that is functioning as mitigation measures. Many of these small places then create a large overall effect. The advantage of these small-scale measures is that they can be in densely populated areas The assessment tool described in this paper is designed for city representatives and is an aid to assess the suitability of applying a particular measure based on the parameters described. The evaluation itself then helps to decide whether the solution is suitable for a particular site or whether any of the parameters need to be adjusted to make it suitable, or whether it would be appropriate to change the proposed solution. The intention of the evaluation is not to assess the technical solution but relies primarily on the location, long-term (especially financial) sustainability and acceptance by the citizens of the city. The paper presents an example of the application of the evaluation to four sites in city Liptovský Mikuláš, describing the results and identifying parameters that can be improved to ensure the urban environmental acupuncture is accepted by citizens and thus future-proofed.

Towards Games for Knowledge Acquisition and Modeling

2010 ◽  
Vol 2 (4) ◽  
pp. 48-66 ◽  
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Machine Learning Techniques ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Modelling Approach ◽  
Representation Of Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

scholarly journals Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies

2020 ◽  
Vol 10 (19) ◽  
pp. 6878
Author(s):  
Ammara Nusrat ◽  
Hamza Farooq Gabriel ◽  
Sajjad Haider ◽  
Shakil Ahmad ◽  
Muhammad Shahid ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
River Basins ◽  
Precipitation Change ◽  
Future Climate ◽  
Machine Learning Techniques ◽  
Future Climate Change ◽  
Four Seasons ◽  
Learning Techniques ◽  
Change Impact

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.

scholarly journals The potential value of seasonal forecasts in a changing climate in southern Africa

2014 ◽  
Vol 18 (4) ◽  
pp. 1525-1538 ◽  
Author(s):  
H. C. Winsemius ◽  
E. Dutra ◽  
F. A. Engelbrecht ◽  
E. Archer Van Garderen ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Southern Africa ◽  
Weather Conditions ◽  
Stress Conditions ◽  
Mitigation Measures ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Dry Spells ◽  
The Future

Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

scholarly journals MODELLING LONG TERM IMPLICATION OF CLIMATE CHANGE PROJECTION ON SHORE MORPHOLOGY OF NORTH NORFOLK, UK, COMBINING TOMAWAC AND SCAPE

2011 ◽  
Vol 1 (32) ◽  
pp. 61 ◽  
Author(s):  
Nicolas Chini ◽  
Peter Stansby ◽  
Mike Walkden ◽  
Jim Hall ◽  
Judith Wolf ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Large Scale ◽  
Numerical Models ◽  
Global Climate ◽  
Stationary Processes ◽  
Climate Change Projections ◽  
Future Evolution ◽  
The Future

Assessment of nearshore response to climatic change is an important issue for coastal management. To predict potential effects of climate change, a framework of numerical models has been implemented which enables the downscaling of global projections to an eroding coastline, based on TOMAWAC for inshore wave propagation input into SCAPE for shoreline modelling. With this framework, components of which have already been calibrated and validated, a set of consistent global climate change projections is used to estimate the future evolution of an un-engineered coastline. The response of the shoreline is sensitive to the future scenarios, underlying the need for long term large scale offshore conditions to be included in the prediction of non-stationary processes.

scholarly journals A large, open source dataset of stroke anatomical brain images and manual lesion segmentations

2017 ◽  
Author(s):  
Sook-Lei Liew ◽  
Julia M. Anglin ◽  
Nick W. Banks ◽  
Matt Sondag ◽  
Kaori L. Ito ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Stroke Recovery ◽  
Machine Learning Techniques ◽  
Lesion Segmentation ◽  
Brain Images ◽  
Learning Techniques ◽  
Segmentation Methods ◽  
Segmentation Algorithms

AbstractStroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

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