Rymd-, geo- och miljövetenskap (SEE) // Space, Earth and Environment (SEE)
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Vi utgår från observationer av universum och vår planet för att utveckla modeller och verktyg som möter globala utmaningar kring resurser, energiförsörjning och klimatpåverkan.
Vart är vi på väg? Var kommer vi ifrån? På vår institution söker vi svaren på de riktigt stora frågorna. I ett långt tidsperspektiv ger stjärnor och galaxers livscykler en inblick i universums, jordens och livets uppkomst – och framtid. Vi observerar också vår planet och samspelet mellan samhälle, teknik och natur för att kunna utveckla teknik, modeller och verktyg som kan möta globala utmaningar inom naturresurser, klimatpåverkan och energiförsörjning.
För forskning och forskningspublikationer, se https://research.chalmers.se/organisation/rymd-geo-och-miljoevetenskap/
Observes the universe and our planet, to develop models and tools that meet global challenges regarding resources, energy supply and climate impact.
Where do we come from and where are we going? At our department we search for answers to the really big questions. In a long time perspective, the lifecycles of stars and galaxies provide an insight into the origin and future of the universe, earth and life. We also observe our planet and the interaction between society, technology and nature in order to develop technologies, models and tools that can meet global challenges regarding natural resources, climate impact and energy supply.
Studying at the Department of Space, Earth and Environment at Chalmers
For research and research output, please visit https://research.chalmers.se/en/organization/space-earth-and-environment/
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Browsar Rymd-, geo- och miljövetenskap (SEE) // Space, Earth and Environment (SEE) efter Program "Complex adaptive systems (MPCAS), MSc"
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- PostA Bayesian machine learning approach to passive microwave precipitation retrievals(2019) Norrestad, Teodor; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and EnvironmentA machine learning-based approach to precipitation retrievals, using Quantile Regression Neural Networks (QRNNs), is developed for data from the Global Precipitation Measurement (GPM) mission. The retrievals are conducted within a Bayesian framework where the networks are trained to predict quantiles of the posterior distribution of rain rates, conditioned on passive microwave observations. In this way, rain rates are retrieved along with the associated retrieval uncertainties. The effects of including additional spatial information as input to the QRNNs are also investigated. Different QRNNs are trained and tested, first globally over oceans and then over the U.S Great Plains. In both cases, the performance of the QRNNs are compared to the Goddard Profiling Algorithm (GPROF), a state-of-the-art passive microwave retrieval algorithm. The primary results are those over oceans, where the QRNNs show great performance on similar levels as GPROF with respect to point estimate metrics such as the mean squared error. Furthermore, the QRNN retrievals are very fast, taking less than a millisecond per footprint on a standard computer. It turns out that extra spatial information improves the QRNNs, especially on making rain-no rain classifications with fractions of true positives and true negatives exceeding 0.67 and 0.96 respectively. Furthermore, the QRNNs manage to produce well calibrated quantiles, resulting in good confidence intervals to account for retrieval uncertainties. Over the Great Plains, the results are promising but are based on much smaller amounts of data and are thus less significant.
- PostA Complex Systems Approach to Human Cultural Evolution(2013) Allen, James; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentThis thesis will use two abstract computational models to investigate a number of outstanding questions related to human cultural evolution. Using simulations explanations for a number of phenomena within the archaeological record will be put forward. These will include the discontinuous cultural evolution patterns, the broadening of human diet and the extinction of the Neanderthals. The central theme throughout these findings is that it is the delity of transfer, and by extension the increase in complexity of early hominid culture, that constrains the subsistence strategies used within the Palaeolithic era, whilst the form of the resources dictates the form that these strategies will take. Key to these dynamics is the territorial competition between groups, with a more diverse strategy leading to more efficient groups that can encroach on the less efficient, reducing the carrying capacity and causing the population to move below the minimum group size allowed, thereby becoming extinct.
- PostA conceptual model for energy, trade and economy(2013) Nilsson, Olof; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentThis thesis focuses on introducing and incorporating an explicit model for energy trade into a conceptual model for economy and energy. The inclusion of an energy market into the model where countries can buy and/or sell energy to a market price is something that was not done in the model that was used as a starting point for this study. In general, energy systems models use less explicit mechanisms to determine trade, like e.g. shadow prices from optimisation. This kind of trading model is successfully developed and introduced into a more complex, dynamic model for economy and energy. The trading mechanism is analysed extensively in itself before introducing it into the more complex model. This to ensure that it exhibits the behaviours that one would expect to observe on a market. The effects of introducing energy trade into such a more complex, dynamic model for economy and energy is then explored and it is shown that when at least one country is sitting on large enough fossil fuel assets, the possibility of energy trade will inhibit the development of renewable energy for all countries. Moreover, the possibilities of avoiding definite time effects in model simulation by modifying the utility function that is subject to optimisation is studied. It is shown that it is possible to find a utility function that in many aspects avoid the definite time effects.
- PostA coupled immunological and epidemiological model for exploration of immunization strategies(2021) Rylander, Kevin; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Andersson, Claes; Andersson, Claes
- PostA network analysis of a company’s internal email communications(2021) Mur, Klaudia; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Andersson, Claes; Andersson, Claes
- PostA Simplified Agent Based Model of a Sinks and Faucets Resource Economy, Using a blind commitment market(2014) Dalsmo, Anton; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentThis masters thesis seeks to model a simple financial system by using an agent based model. It seeks to do this by constructing an as simple as possible market and agent dynamic. To do this the blind commitment market is introduced and the general model constructed around this. For this model a number of questions are then posed concerning the model's stability and effectiveness at modelling the core market dynamics. Further- more an analytical analog is created in order to analyse the equilibrium dynamics. From the analytical representation parallels are drawn to the agent based model. The agent based model is constructed in such a way that agents are encouraged to interact in order to meet all demands. The arising dependancy network of agents is then analysed using a graphical representation, which shows how much the different agents depend on each other. The general dynamics are also examined as the three main parameters of the model are varied. It is shown that the bind commitment market upholds supply and demand char- acteristics, and that the agent based model functions as an economy with cooperating agents, however these agents are not making any profit. Furtheromre it is shown that the analytical model is stable for all possible parameters of the allowed parameter space in the sense that feedback processes steer the supply and demand dynamics to a stable fix point, an equilibrium price. The agent based model also gravitates towards an equi- librium price, however minority game dynamics prevent the system from reaching, or staying, at the equilibrium price for long. The minority game dynamics ing the system away from the stable point, causing the system to restart its trajectory towards the equilibrium price, aided by the feedback processes. Lastly the three main parameters of the model are examined with respect to how much they influence the stability of prices inside the model.
- PostA software toolkit for generating ice and snow particle sharp data(2016) Rathsman, Torbjörn; Chalmers tekniska högskola / Institutionen för rymd- och geovetenskap; Chalmers University of Technology / Department of Earth and Space SciencesAbstract Ice and snow particle shape data are important for understanding the scattering of microwave radiation from a cloud. This thesis presents a software toolkit that can be used to generate such data, for use with Discrete Dipole Approximation calculations. The toolkit has been used to implement a Gillespie-based algorithm with overlap rejection. This algorithm, when used with hexagonal columns, has reproduced some of the properties of real snowflakes, namely their fractal dimension, and their size distribution. The toolkit uses ice crystal prototypes to construct aggregates. Ice crystal prototypes can be modeled in 3D modelling software. This makes it is easy to construct exotic shapes, as opposed to a system based on different classes for different prototypes. In order to keep the possibility of arbitrary parameterisation, the ice crystal prototypes specifies transformation rules that are used if the ice crystal prototype should be deformed. Aggregates and ice crystal prototypes can be merged in different ways to form larger aggregates. To feed a DDA calculation program, the toolkit also provides a rasterisation system, which fills geometry with voxels by using a 6-directional floodfill algorithm. A large part of the thesis discusses various ways of measuring particles. This has lead to a unit neutral way of testing whether or not a model simulates reality. The idea is to compare an averaged spherical volume fill ratio, which according to measurements should follow a particular equation, derived within this thesis. Besides giving an overview of the toolkit, and presenting simulation results, this thesis also serves as a reference manual on how to use the toolkit.
- PostA telegraph model for extension fluctuations of nano-confined DNA molecules(2018) Ödman, Daniel; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and Environment
- PostBayesian inference in networks of spiking neurons - On local learning(2019) Mikulasch, Fabian; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Lindgren, Kristian; Lindgren, Kristian
- PostBiodiesel fuels in Sweden:drivers, barriers, networks and key stakeholders(2014) Hult, Cecilia; Mendoza, Daniella; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentThis thesis consists of two parts. Part I describes the Challenge Lab method,which is a method developed for students to find a thesis research question to support transition into a sustainable society. The Challenge Lab method was carried out by 12 students during Spring 2014 and contains methodologies,tools, perspectives, theories and frameworks for students to work with complex problems related to sustainable development. This years’ work include backcasting,self-leadership, multi-level perspective, systems theory, systems transition,design-thinking, etc. Part II addresses the uncertainties of the barriers and drivers for development,diffusion and use of biodiesel fuel in Sweden and as well as the networks and key stakeholders connected to biodiesel fuels. In Sweden, biodiesel fuels have the biggest market share among the biofuels, 53.4%, but only hold a small market share out of the total vehicle fuels, 4.3% (based on energy content). The aim of this report is to increase awareness and encourage development, diffusion and use of biodiesel fuels (FAME, HVO and DME). The barriers and drivers were identified with the methodology of Functions of Innovation Systems and key stakeholders were identified through Social Network Analysis. The research method was semi-structured interviews with stakeholders as well as literature studies. The the main identified drivers are i) HVO commercialisation and, ii)EU policy on emission standards; and the main identified barriers are i) high production costs of biodiesels compared to fossil diesels, ii) limited state aid (mainly tax exemption) that lacks future oriented vision, iii) low visibility of biodiesels, iv) weak market for biodiesel fuels in a high-blend form, v) petition on regulatory change for HVO, vi) environmental concerns associated with biofuels, vii) feedstock limitations and viii) lack of strong advocacy coalitions. Furthermore, important stakeholders in the Swedish biodiesel market are Preem,Volvo Group and policy makers on national level.
- PostCharacterisation of collective motion(2015) Kjellman, Cecilia; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentSelf-organisation and emergence is a widespread and fundamental aspect of biological systems. Fish schools, insect swarms, bird ocks, colonies of bac- teria and human crowds are familiar examples of systems of very different levels of complexity and scale. To determine what governs interaction in the many biological systems is of importance. This thesis mainly focuses on comparing information usage for modelling collective motion, comparing using the distance to neighbouring individuals and time to collision. The thesis begins with analysing gathered sh school data in light of recent work in the eld of human crowd behaviour. The method uses a pair distribution function and a possible interaction energy to compare the two characteristics distance to neighbouring individuals and time to collision. The result differs in an interesting way from the original article on human crowd behaviour. The later part of the thesis describes existing collective behaviour models, and model adjustments, using either distance or time to collision as the most important attribute. Suitable existing measurements are touched upon. Fi- nally simulations using the included models are discussed but no conclusion regarding any decisive variable is made.
- PostComparison of artificial intelligence algorithms for Pokémon battles(2019) Norström, Linus; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Lindgren, Kristian; Lindgren, Kristian
- PostDeployment planning for artillery hunting radar systems using high-resolution geospatial data(2018) Larsson, Kevin; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment
- PostEvolutionary voting games(2018) Fredriksson, Carl; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment
- PostEvolved ecosystems - A simulation of an emerging complex adaptive system(2017) Johansson, Joakim; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment
- PostExploring the role of blockchain technology in Mobility as a Service - Towards a fair Combined Mobility Service(2017) Torstensson, Joel; Andersson, Patrik; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentMobility as a Service is a novel approach to mobility. There have been several efforts to incorporate the approach by trying to bring multiple mobility providers (MPs) together in one platform. In most of these efforts, one organization has acted as a broker for all of the MPs and the customers. This broker might have vested interest and could be biased toward well established MPs. A new technology called blockchain provides a viable method of coordination of parties that do not trust each other without the need of a central authority. This report explores the intersection of these two developments, Mobility as a Service and blockchain technology, and asks: How can a Combined Mobility Service platform that benefits all of the involved stakeholders be designed? We explore both the technical viability as well as possible economic impact on MPs of such a platform by combining qualitative and quantitative approaches. By interviewing multiple stakeholders, the central requirements were identified. To investigate the socio-economic impact, an agent-based model was constructed that simulates the effect of user behaviour with and without the platform. These simulations shows that while it might not be very clear if the revenue of a MP increases by joining the platform, the existence of the platform will decrease the revenue of MPs that do not join if the platform gets sufficient network effects. The main result of the study is a simplified technical specification of how a platform could be constructed which provides a solid description of how it could be implemented. This specification takes into consideration the needs of existing MPs, but also maintains neutrality so that new MPs can join on an equal footing with the existing ones. While it does not take all of the technical possibilities into consideration, it can still be seen as a success since it suggests a solution to the main problem of combined mobility platforms. The simulations provide additional confirmation that this approach is viable. The next step in this research is to implement a proof-of-concept of the platform, showing its viability on a more practical level.
- PostFormation of compact obscured nuclei. Uncovering the most hidden objects in the universe(2023) Robeling, Nils-Martin; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Aalto, Susanne; Aalto, SusanneTo begin with, I would like to extend a deep thank you to my supervisor and examiner, Susanne Aalto. Through many discussions, you have provided me with guidance both in my research for this thesis as well as for my future career. I would also like to thank you for providing me with the opportunity to present my research in Italy and for sending me to the UK (where I was very well received by Dimitra Rigopoulou, thank you), for a cooperative effort on simulating CONs. Further, I would like to thank all the people on the CONquest team, Clare, Mark, Mamiko, Kyoko, Chentao, and Sabine. You have all helped me by deepening my knowledge, providing excellent company, and answering my annoying questions (especially you, Clare, and Mark). I also hold much gratitude to the Department of Space, Earth, and Environment at Chalmers for providing me with office space as well as the possibility to visit Onsala Space Observatory and work there. Finally, I would like to thank my family, who have listened to my ranting for countless hours, provided me with emotional support, and encouraged me to pursue research.
- PostGeneration of atmospheric cloud fields using generative adversarial networks(2020) Rilemark, Rebecka; Svensson, Carl; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Eriksson, Patrick; Pfreundschuh, SimonCloud fields have a great impact on the weather and climate of the Earth. Modern climate models that include feedback from cloud fields present large uncertainties in their predictions. To improve models of climate systems large amounts of high-quality data of cloud fields are necessary. The Cloud Profiling Radar (CPR) on the CloudSat satellite provides high-quality vertical data of cloud fields but is limited in its geographical coverage. The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite provides data that only discerns the top layer of clouds but has far greater spatial coverage. This project implements two generative adversarial networks (GAN), with the intention of extending the current available CPR data set. One network generates vertical cloud data using random noise as input. The other network uses MODIS data as input to generate vertical cloud data tied to a specific geolocation, this network is accordingly a conditional generative adversarial network (CGAN). The two neural networks are compared to a common method used for generating synthetic cloud fields, the Iterative Amplitude Adjusted Fourier Transform (IAAFT). The methods are compared in regard to multiple different statistical and physical properties, including ice water path, cloud-top height, and spatial autocorrelation. The results of both GAN are promising with regard to generating realistic cloud fields. Individual generated cloud scenes from GAN and CGAN show a stronger visual resemblance to real radar scenes than scenes generated with IAAFT. The three different methods vary in performance when analysed based on the statistics of their output. For example, the IAAFT method outperforms both the GAN and the CGAN when it comes to recreating the large scale vertical cloud distributions, but shows a clear weakness when it comes to capturing the internal structure of cloud fields, measured by the autocorrelation. A reoccurring problem with the GAN is the overfitting of the training data and mode collapse. This aspect needs further improvement through re-training of the networks with a larger data set and possibly uneven weighting between the generator and discriminator. After the inclusion of such improvements, the two GAN developed in this project are expected to show promising results for including generated radar data in applications.
- PostGroundwater and gravity modelling using recurrent neural networks(2021) Våge, Magnus; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Haas, Rüdiger; Mouyen, MaximeArtificial intelligence has gained a lot of interest in recent years due to its impressive performance on a variety of prediction and classification tasks. These results are in large part a result of the increase in computational capacity and the availability of large data sets. Here we apply artificial intelligence on the problem of predicting local variations of gravity due to local hydrological effects at the Onsala Space Observatory. Hydrological effects imply redistribution of mass in close proximity to the measuring equipment, and therefore contributes to the variations seen in the local strength of gravity. The objective of this thesis is to model groundwater and residual gravity levels based on data from a normal weather station, giving us the ability to construct useful simulated groundwater and residual gravity data measurements without the actual equipment in place. The modelling is performed using recurrent neural networks containing LSTM units, and with some convolutional preprocessing of the input data. The resulting models perform very well for predicting groundwater levels, reaching an RMSE between observed and predicted values of 0.046 m over the course of 2019 which is comparable to other results using neural networks to predict hourly groundwater levels ([2], [3]). The neural networks initially performed quite poorly when predicting residual gravity. However, by utilizing our good results from the groundwater predictions, we extended our available data set from about 3 to almost 8 years. The best model trained on the 8 year data set achieved an RMSE of 5.961 nms−2 on the test set, a more than 6-fold improvement over the networks trained on the 3 year data set. The conclusion is that recurrent neural networks are suitable for modelling groundwater levels and residual gravity, but their performance is highly dependent on the amount of data available and the preprocessing applied to the data.
- PostIdentifying apartment buildings with potential heating issues(2011) Rooij, Joris van; Chalmers tekniska högskola / Institutionen för energi och miljö; Chalmers University of Technology / Department of Energy and EnvironmentThe residential sector in Sweden uses a large amount of energy for heating and hot water. Sweden as well as all other European countries need to reduce its energy consumption with 9% by 2016. It is important to make sure that this heating is done in an optimal way in order to meet this demand. A method to identify buildings with possible heating problems is described in this thesis. The method uses analysis on the energy signature of buildings in order to construct parameters that point out buildings with possible problems. The method is applied to a set of 352 apartment buildings located in Gothenburg. The results show that a very large part of the set shows signs of possible heating problems.