scholarly journals Exploring the complex origins of energy poverty in The Netherlands with machine learning

Energy Policy ◽  
2021 ◽  
Vol 156 ◽  
pp. 112373
Author(s):  
Francesco Dalla Longa ◽  
Bart Sweerts ◽  
Bob van der Zwaan
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Energy Poverty

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

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

PLoS ONE ◽  
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.

scholarly journals Humanising the Energy Transition: Towards a National Policy on Energy Poverty in the Netherlands

2021 ◽  
Vol 3 ◽  
Author(s):  
Mariëlle Feenstra ◽  
Lucie Middlemiss ◽  
Marlies Hesselman ◽  
Koen Straver ◽  
Sergio Tirado Herrero
Keyword(s):  
The Netherlands ◽  
Lived Experience ◽  
National Level ◽  
National Policy ◽  
Energy Transition ◽  
White Paper ◽  
Level Energy ◽  
Energy Poverty ◽  
National Agenda ◽  
Early Action

Energy poverty is emerging as a national agenda in the Netherlands. Local authority leadership and action on this agenda, and European Union reporting requirements around the energy transition have aligned to create an opportunity to establish a national agenda on this issue. Early action on energy poverty by local authorities stemmed from their recognition of the value of addressing environmental, health, social welfare and poverty goals through measures to address the problem. In contrast, the experiences of vulnerable energy consumers have limited recognition in national policy. Meanwhile EU requirements for climate reporting include a specification for measuring and monitoring energy poverty. This growing momentum has resulted in an emerging interest in energy poverty as a means to achieve a just transition at a national level, as reflected in the Dutch National Climate and Energy Plan. In this paper, we profile the case of the Netherlands, and outline the opportunity we see for the development of an energy poverty agenda in national energy transition policy, as part of a multi-level energy governance effort. We report on a national stakeholder workshop that we led, linking the lived experience of energy poverty in the Netherlands with policy solutions. Following the clear call for a national policy in this workshop, we also outline a strategy for engagement with energy poverty in the Netherlands, published recently in a white paper on this topic.

scholarly journals PREFACE

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 1-1
Author(s):  
A. Abdul Rahman ◽  
H. Rhinane
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
New South ◽  
Fine Arts ◽  
Online System ◽  
Research Institutes ◽  
South Wales ◽  
National University ◽  
Research Students ◽  
Sydney Australia

Abstract. This year the event of the Joint Geospatial Asia-Europe 2021 and GeoAdvances 2021 was held virtually from Casablanca, Morocco from 5 to 6th October. Sixty-two papers were received and 46 papers were accepted for the ISPRS International Archives. These papers could be categorized into three sub-disciplines – GIS, Geomatics, and Geo-computation (machine learning and applications). All accepted papers as revealed in this proceedings and presented at the conference. Several renowned researchers presented their works as keynotes, they are Prof Dr Peter van Oosterom (from TU Delft, the Netherlands), Prof Dr Volker Coors (from HfT Stuttgart, Germany), Dr Filip Biljecli (from National University of Singapore), Prof Dr Hassan Rhinane (from Hassan II University Casablanca, Morocco), Prof Dr Umit Isikdag (from Mimar Sinan Fine Arts University, Turkey), Assoc Prof Dr Gurcan Buyuksalih (Istanbul, Turkey), Prof Dr Sisi Zlatanova (from University of New South Wales, Sydney, Australia), Assoc Prof Dr Lars Bodum (Aalborg University, Denmark), Prof Dr Andreas Buerkert (from University of Kassel, Germany). Presentations from Invited Speakers from various universities and research institutes from Philippines, Malaysia, Poland, Switzerland, Qatar, Indonesia, and Germany enhanced the conference academic standing.We would like to thank all reviewers for their diligent works on the feedbacks and comments on the assigned papers.Last, but not least, gratitude to all the volunteers mainly our research students for making sure all the online system runs smoothly.Enjoy!

scholarly journals Assessing Resilience to Energy Poverty in Europe through a Multi-Criteria Analysis Framework

2020 ◽  
Vol 12 (12) ◽  
pp. 4899 ◽  
Author(s):  
Apostolos Arsenopoulos ◽  
Vangelis Marinakis ◽  
Konstantinos Koasidis ◽  
Andriana Stavrakaki ◽  
John Psarras
Keyword(s):  
The Netherlands ◽  
Western Europe ◽  
Evaluation Criteria ◽  
European Countries ◽  
Economic Aspects ◽  
Analysis Framework ◽  
Energy Poverty ◽  
Multi Criteria Analysis

This study introduces a framework for assessing the resilience of different European countries against the problem of energy poverty. The proposed framework is established upon two major implementation pillars: capturing stakeholder knowledge and employing a multi-criteria analysis framework in order to provide valuable insights and objective results. The implicated evaluation criteria have been identified by the group of stakeholders and incorporate several socio-economic aspects of the problem beyond the energy dimension. The proposed methodology is largely dependent on the engaged stakeholders’ assessments, thus introducing nuggets of subjectivity into the whole analysis. However, it significantly differs from other energy poverty-based approaches, its novelty lying in that it directly attempts to evaluate a country according to its potential to deal with the problem as a whole, rather than deconstructing it in components and partial indicators. The proposed framework is demonstrated in countries in both Southern/Eastern and Northern/Western Europe (Austria, Belgium, Croatia, France, Greece, Ireland, Italy, Latvia, the Netherlands, Romania, Spain), exploiting diversities and particularities associated with their context.

scholarly journals Machine Learning Model for Identifying Dutch/Belgian Biodiversity

2019 ◽  
Vol 3 ◽  
Author(s):  
Laurens Hogeweg ◽  
Maarten Schermer ◽  
Sander Pieterse ◽  
Timo Roeke ◽  
Wilfred Gerritsen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
The Netherlands ◽  
Web Service ◽  
Species Identification ◽  
Mobile Apps ◽  
The Public ◽  
Number Of Species ◽  
Online Platforms ◽  
Species Groups

The potential of citizen scientists to contribute to information about occurrences of species and other biodiversity questions is large because of the ubiquitous presence of organisms and friendly nature of the subject. Online platforms that collect observations of species from the public have existed for several years now. They have seen a rapid growth recently, partly due to the widespread availability of mobile phones. These online platforms, and many scientific studies as well, suffer from a taxonomic bias: the effect that certain species groups are overrepresented in the data (Troudet et al. 2017). One of the reasons for this bias is that the accurate identification of species, by non-experts and experts, has been limited by the large number of species that exist. Even in the geographically limited area of the Netherlands and Belgium, the number of species that are regularly observed are in the thousands. This makes the ability to identify all those species difficult or impossible for an individual. Recent advances in species identification powered by deep learning, based on images (Norouzzadeh et al. 2018), suggest a large potential for a new set of digital tools that can help the public (and experts) to identify species automatically. The online observation platform Observation.org has collected over 93 million occurrences in the Netherlands and Belgium in the last 15 years. About 20% of these occurrences are supported by photographs, giving a rich database of 17 million photographs covering all major species groups (e.g., birds, mammals, plants, insects, fungi). Most of the observations with photos were validated by human experts at Observation.org, creating a unique database suitable for machine learning. We have developed a deep learning-based species identification model using this database containing 13,767 species, 1,530 species-groups, 734 subspecies and 117 hybrids. The model is made available to the public through a web service (https://identify.biodiversityanalysis.nl) and through a set of mobile apps (ObsIdentify). In this talk we will discuss our technical approach for dealing with the large number of species in a deep learning model. We will evaluate the results in terms of performance for different species groups and what this could mean to address part of the taxonomic bias. We will also consider limitations of (image-based) automated species identification and determine venues to further improve identification. We will illustrate how the web service and mobile apps are applied to support citizen scientists and the observation validation workflows at Observation.org. Finally, we will examine the potential of these methods to provide large scale automated analysis of biodiversity data.

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