Browsing by Subject "modeling"

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  • Mäkelä, Samppa (2018)
    The objective of this study is to develop a method of appraising rock aggregate resources, using open data and open source tools. The availability of aggregates in Finland is mostly determined by competing land use and restrictions on extraction. Therefore, it is important to determine the extent of available resources, especially near areas of high demand. The study area consists of the 14 municipalities in the Helsinki metropolitan area, a total of 3841 km(2). The data used are open access, provided by the Geological Survey of Finland and the National Land Survey. These are combined in a GIS to identify locations where extraction of aggregates is possible. Geology, limitations and the highest and lowest point of possible extraction are determined. These are used to estimate the available resources and locate the economically feasible sites. Data used include a digital elevation model and layers on geology and land use. The results show that competing land use has sterilized most aggregate locations in the area. Remaining locations are concentrated on the edges. However, some potential sites remain. Field evaluations and comparison to previous studies show that the method has potential in evaluating remaining resources and directing further study for prospective production areas. The model is fast in coarsely determining aggregate volume. It is highly suitable for focusing expert fieldwork. Land use in the area continues to sterilize new locations. To avoid economic and ecological damage, a plan should be implemented for securing this resource. This may include the reserving of locations, reducing use, checking legislation on production and recycling used aggregates.
  • Maeda, Eduardo; Haapasaari, Päivi; Helle, Inari; Lehikoinen, Annukka; Voinov, Alexey; Kuikka, Sakari (2021)
    Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations
  • Virtanen, Lari S.; Olkkonen, Maria; Saarela, Toni P. (2020)
    Color serves both to segment a scene into objects and background and to identify objects. Although objects and surfaces usually contain multiple colors, humans can readily extract a representative color description, for instance, that tomatoes are red and bananas yellow. The study of color discrimination and identification has a long history, yet we know little about the formation of summary representations of multicolored stimuli. Here, we characterize the human ability to integrate hue information over space for simple color stimuli varying in the amount of information, stimulus size, and spatial configuration of stimulus elements. We show that humans are efficient at integrating hue information over space beyond what has been shown before for color stimuli. Integration depends only on the amount of information in the display and not on spatial factors such as element size or spatial configuration in the range measured. Finally, we find that observers spontaneously prefer a simple averaging strategy even with skewed color distributions. These results shed light on how human observers form summary representations of color and make a link between the perception of polychromatic surfaces and the broader literature of ensemble perception.
  • Spjuth, Ola; Karlsson, Andreas; Clements, Mark; Humphreys, Keith; Ivansson, Emma; Dowling, Jim; Eklund, Martin; Jauhiainen, Alexandra; Czene, Kamila; Gronberg, Henrik; Sparen, Par; Wiklund, Fredrik; Cheddad, Abbas; Palsdottir, Porgerodur; Rantalainen, Mattias; Abrahamsson, Linda; Laure, Erwin; Litton, Jan-Eric; Palmgren, Juni (2017)
    Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences. Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform. Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.
  • Pitkänen, Timo P.; Sirro, Laura; Häme, Lauri; Häme, Tuomas; Törmä, Markus; Kangas, Annika (ScienceDirect, 2020)
    International Journal of Applied Earth Observation and Geoinformation 86 (2020)
    The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency.
  • Nieminen, Martta (Helsingfors universitet, 2013)
    The trend of energy policy in European Union as well as in international context has lately been to increase the share of renewable biofuels. The causes for this are global warming, shrinking reserves of fossil fuels and governments' aspiration for energy independence. Microalgae have shown to be a potential source of biofuels. Though cultivation of microalgae has a long history, has production for fuel yet been unprofitable. Production has become more effective as cultivation has shifted from open ponds to controlled photobioreactors but to achieve effective cultivation methods substantially more understanding on the ecophysiology of microalgae is needed. The aim of my thesis was to research the optimal light intensity and temperature of photosynthesis for three microalgae (Chlorella pyrenoidosa, Euglena gracilis and Selenastrum sp.), which are the main parameters limiting the level of photosynthesis in nutrient rich environments such as photobioreactor. The research strains were incubated in eight light intensities (0,15-250 µmol m-2 s-2) and in 5-6 temperatures (10-35 °C). Photosynthetic activity was determined with radiocarbon method which is based on the stoichiometry of photosynthesis. The purpose of radiocarbon method is to estimate how much dissolved carbon dioxide do the algae assimilate when photosynthesizing. In the method the algae are incubated in light and dark bottles where certain amount of radiocarbon (14C) has been added as a tracer. The algae fix 14C in the proportion to available 12C. 14C method has become the most common way to measure the photosynthesis of microalgae. All of the algal strains grew in 10-30 °C but C. pyrenoidosa was the only one which grew also in 35 °C. The data was analyzed by fitting them with two photosynthesis-light intensity relationship models and one photosynthesis-temperature relationship model and as a result values of essential parameters, i.e. optimal light intensity (Iopt) and temperature (Topt) for photosynthesis, could be estimated. The model which gave the best fit was chosen to describe the photosynthesis-light intensity relationship. The optimal light intensity for C. pyrenoidosa ranged between 121–242 µmol m-2 s-2 and optimal temperature was 15 °C. Corresponding values for E. gracilis were 117-161 µmol m-2 s-2 and 24,1 °C, and for Selenastrum sp. 126-175 µmol m-2 s-2 and 16,7 °C. Q10-values were also determined. With all research strains, the level of photosynthesis increased as light intensity and temperature grew until optimal values were reached. The strains tolerated higher light intensities in warmer temperatures but after reaching the optimal temperature, the level of photosynthesis did not increase any more with elevating temperature. Robust algal strains, i.e. strains, that are most adaptable in terms of light intensity and temperature, are the most prominent ones for biofuel production. From these research strains the most adaptable strain in terms of light intensity was C. pyrenoidosa and in terms of temperature Selenastrum sp. C. pyrenoidosa had superior carbon fixation rate in relation to cell size. Therefore it can be concluded that C. pyrenoidosa is the most suitable algal strains for biofuel applications of the strains assessed here.
  • Pomoell, Jens; Poedts, Stefaan (2018)
    The implementation and first results of the new space weather forecasting-targeted inner heliosphere model "European heliospheric forecasting information asset" (EUHFORIA) are presented. EUHFORIA consists of two major components: a coronal model and a heliosphere model including coronal mass ejections. The coronal model provides data-driven solar wind plasma parameters at 0.1AU by constructing a model of the coronal large-scale magnetic field and employing empirical relations to determine the plasma state such as the solar wind speed and mass density. These are then used as boundary conditions to drive a three-dimensional time-dependent magnetohydrodynamics model of the inner heliosphere up to 2 AU. CMEs are injected into the ambient solar wind modeled using the cone model, with their parameters obtained from fits to imaging observations. In addition to detailing the modeling methodology, an initial validation run is presented. The results feature a highly dynamic heliosphere that the model is able to capture in good agreement with in situ observations. Finally, future horizons for the model are outlined.
  • Scolini, C.; Chané, E.; Pomoell, J.; Rodriguez, L.; Poedts, S. (2020)
    Predictions of the impact of coronal mass ejections (CMEs) in the heliosphere mostly rely on cone CME models, whose performances are optimized for locations in the ecliptic plane and at 1 AU (e.g., at Earth). Progresses in the exploration of the inner heliosphere, however, advocate the need to assess their performances at both higher latitudes and smaller heliocentric distances. In this work, we perform 3-D magnetohydrodynamics simulations of artificial cone CMEs using the EUropean Heliospheric FORecasting Information Asset (EUHFORIA), investigating the performances of cone models in the case of CMEs launched at high latitudes. We compare results obtained initializing CMEs using a commonly applied approximated (Euclidean) distance relation and using a proper (great circle) distance relation. Results show that initializing high-latitude CMEs using the Euclidean approximation results in a teardrop-shaped CME cross section at the model inner boundary that fails in reproducing the initial shape of high-latitude cone CMEs as a circular cross section. Modeling errors arising from the use of an inappropriate distance relation at the inner boundary eventually propagate to the heliospheric domain. Errors are most prominent in simulations of high-latitude CMEs and at the location of spacecraft at high latitudes and/or small distances from the Sun, with locations impacted by the CME flanks being the most error sensitive. This work shows that the low-latitude approximations commonly employed in cone models, if not corrected, may significantly affect CME predictions at various locations compatible with the orbit of space missions such as Parker Solar Probe, Ulysses, and Solar Orbiter.
  • Niemelä, Kirsi (Helsingfors universitet, 2011)
    The aim of this study was to develop mathematical energy balance models for early and middle lactation period of dairy cows. The traits for predicting were information of diet, feed, milk production, milk composition, body weight and body condition score. This study was a part of development work of KarjaKompassi-project. The data used in this study was based on 12 feeding experiments performed in Finland. The complete data from the studies included 2647 weekly records from multiparous dairy cows and 1070 weekly records from primiparous dairy cows. The data was collected from calving to 8-28 weeks of lactation. Three-fourths of the totals of 344 dairy cows were Finnish Ayshire cows and the rest of the cows were Friesian Cattle. The cows were fed by the Finnish feeding standards. The data was handled by the Mixed-procedure of the SAS-programme. The outliers were removed with Tukey´s method. The relationship between energy balance and predictor traits was studied with correlation analysis. The regression analysis was used to predicting energy balance. To quantify the relationship of lactation day to energy balance, 5 functions were fitted. The random factor was a cow in the experiment. The model fit was assessed by residual mean square error, coefficient of determination and Bayesian information criterion. The best models were validated in the independent data. Ali-Schaeffer achieved the highest fit functions. It was used by the basal model. The error in every model grew after the 12th lactation week, because the number of records decreased and energy balance turned positive. The proportion of concentrate in the diets and concentrate dry matter intake index were the best predictors of energy balance from traits of diet. Milk yield, ECM, milk fat and milk fat-protein ratio were good predictors during lactation period. The RMSE was lower when ECM was standardized. The body weight and body condition score didn’t improve the predictive value of the basal model. The models can be used to predict energy balance in the herd level, but they are not applicable for predicting individual cow energy balance.
  • Javanainen, Arto; Muinos, Henrique Vazquez; Nordlund, Kai; Djurabekova, Flyura; Galloway, Kenneth F.; Turowski, Marek; Schrimpf, Ronald D. (2018)
    Heavy ion irradiation increases the leakage current in reverse-biased SiC Schottky diodes. This letter demonstrates, via molecular dynamics simulations, that a combination of bias and ion-deposited energy is required to produce the degradation.
  • Fransner, Filippa; Gustafsson, Erik; Tedesco, Letizia; Vichi, Marcello; Hordoir, Robinson; Roquet, Fabien; Spilling, Kristian; Kuznetsov, Ivan; Eilola, Kari; Morth, Carl-Magnus; Humborg, Christoph; Nycander, Jonas (2018)
    High inputs of nutrients and organic matter make coastal seas places of intense air-sea CO2 exchange. Due to their complexity, the role of coastal seas in the global air-sea CO2 exchange is, however, still uncertain. Here, we investigate the role of phytoplankton stoichiometric flexibility and extracellular DOC production for the seasonal nutrient and CO2 partial pressure (pCO(2)) dynamics in the Gulf of Bothnia, Northern Baltic Sea. A 3-D ocean biogeochemical-physical model with variable phytoplankton stoichiometry is for the first time implemented in the area and validated against observations. By simulating non-Redfieldian internal phytoplankton stoichiometry, and a relatively large production of extracellular dissolved organic carbon (DOC), the model adequately reproduces observed seasonal cycles in macronutrients and pCO(2). The uptake of atmospheric CO2 is underestimated by 50% if instead using the Redfield ratio to determine the carbon assimilation, as in other Baltic Sea models currently in use. The model further suggests, based on the observed drawdown of pCO(2), that observational estimates of organic carbon production in the Gulf of Bothnia, derived with the 14C method, may be heavily underestimated. We conclude that stoichiometric variability and uncoupling of carbon and nutrient assimilation have to be considered in order to better understand the carbon cycle in coastal seas.
  • Kiczko, Adam; Västilä, Kaisa; Kozioł, Adam; Kubrak, Janusz; Kubrak, Elzbieta; Krukowski, Marcin (EGU, 2020)
    Hydrology and Earth System Sciences 24 8 (2020)
    Despite the development of advanced process-based methods for estimating the discharge capacity of vegetated river channels, most of the practical one-dimensional modeling is based on a relatively simple divided channel method (DCM) with the Manning flow resistance formula. This study is motivated by the need to improve the reliability of modeling in practical applications while acknowledging the limitations on the availability of data on vegetation properties and related parameters required by the process-based methods. We investigate whether the advanced methods can be applied to modeling of vegetated compound channels by identifying the missing characteristics as parameters through the formulation of an inverse problem. Six models of channel discharge capacity are compared in respect of their uncertainty using a probabilistic approach. The model with the lowest estimated uncertainty in explaining differences between computed and observed values is considered the most favorable. Calculations were performed for flume and field settings varying in floodplain vegetation submergence, density, and flexibility, and in hydraulic conditions. The output uncertainty, estimated on the basis of a Bayes approach, was analyzed for a varying number of observation points, demonstrating the significance of the parameter equifinality. The results showed that very reliable predictions with low uncertainties can be obtained for process-based methods with a large number of parameters. The equifinality affects the parameter identification but not the uncertainty of a model. The best performance for sparse, emergent, rigid vegetation was obtained with the Mertens method and for dense, flexible vegetation with a simplified two-layer method, while a generalized two-layer model with a description of the plant flexibility was the most universally applicable to different vegetative conditions. In many cases, the Manning-based DCM performed satisfactorily but could not be reliably extrapolated to higher flows.
  • Pyörälä, Jiri; Liang, Xinlian; Vastaranta, Mikko; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppä, Juha (2018)
    State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter > 15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67 degrees (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2 degrees (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.
  • Hakola, Tuulia (1999)
    The pension system forms a highly significant part of the whole social welfare system. The impact of the pension system is not limited to the financial aspects, but, for example, labour markets, savings, capital accumulation and income distribution can be affected. This study considers the effects of the pension system on the labour supply of the elderly. As the Finnish pension system is mainly a Pay-As-You-Go system, the concurrent change in the demographic structure, compounded by increased early retirements, is likely to yield strong pressures on the financing of the current pension system. The study evaluates early withdrawals by the elderly from the labour force. It assesses the significance of the implicit economic incentives, provided by the pension system, to retire early. The incentives are measured by an option value to retirement. Transition probabilities are considered in a random effects probit model, using the data from the Employment Registry of the Statistics Finland. The empirical results provide evidence that economic incentives matter also to the Finnish labour force. The results reveal that if an individual gains financially by postponing his retirement, he is more likely to do so. Simulations show that two of the implemented and contemplated policy reforms have a desired impact on the retirement probabilities.
  • Zaidan, Martha A.; Surakhi, Ola; Fung, Pak Lun; Hussein, Tareq (2020)
    Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R2=0.77) and TDNN for hourly averaged data (with R2=0.66) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.
  • Jarvi, Leena; Havu, Minttu; Ward, Helen C.; Bellucco, Veronica; McFadden, Joseph P.; Toivonen, Tuuli; Heikinheimo, Vuokko; Kolari, Pasi; Riikonen, Anu; Grimmond, C. Sue B. (2019)
    There is a growing need to simulate the effect of urban planning on both local climate and greenhouse gas emissions. Here, a new urban surface carbon dioxide (CO2) flux module for the Surface Urban Energy and Water Balance Scheme is described and evaluated using eddy covariance observations at two sites in Helsinki in 2012. The spatial variability and magnitude of local-scale anthropogenic and biogenic CO2 flux components at high spatial (250 m x 250 m) and temporal (hourly) resolution are examined by combining high-resolution (down to 2 m) airborne lidar-derived land use data and mobility data to account for people's movement. Urban effects are included in the biogenic components parameterized using urban eddy covariance and chamber observations. Surface Urban Energy and Water Balance Scheme reproduces the seasonal and diurnal variability of the CO2 flux well. Annual totals deviate 3% from observations in the city center and 2% in a suburban location. In the latter, traffic is the dominant CO2 source but summertime vegetation partly offsets traffic-related emissions. In the city center, emissions from traffic and human metabolism dominate and the vegetation effect is minor due to the low proportion of vegetation surface cover (22%). Within central Helsinki, human metabolism accounts for 39% of the net local-scale emissions and together with road traffic is to a large extent responsible for the spatial variability of the emissions. Annually, the biogenic emissions and sinks are in near balance and thus the effect of vegetation on the carbon balance is small in this high-latitude city.
  • Laurila, Terhi; Sinclair, Victoria; Gregow, Hilppa (2020)
    On 22 September 1982, an intense windstorm caused considerable damage in northern Finland. Local forecasters noted that this windstorm potentially was related to Hurricane Debby, a category 4 hurricane that occurred just 5 days earlier. Due to the unique nature of the event and lack of prior research, our aim is to document the synoptic sequence of events related to this storm using ERA-Interim reanalysis data, best track data, and output from OpenIFS simulations. During extratropical transition, the outflow from Debby resulted in a ridge building and an acceleration of the jet. Debby did not reintensify immediately in the midlatitudes despite the presence of an upper-level trough. Instead, ex-Debby propagated rapidly across the Atlantic as a diabatic Rossby wave-like feature. Simultaneously, an upper-level trough approached from the northeast and once ex-Debby moved ahead of this feature near the United Kingdom, rapid reintensification began. All OpenIFS forecasts diverged from reanalysis after only 2 days indicating intrinsic low predictability and strong sensitivities. Phasing between Hurricane Debby and the weak trough, and phasing of the upper- and lower-level potential vorticity anomalies near the United Kingdom was important in the evolution of ex-Debby. In the only OpenIFS simulation to correctly capture the phasing over the United Kingdom, stronger wind gusts were simulated over northern Finland than in any other simulation. Turbulent mixing behind the cold front, and convectively driven downdrafts in the warm sector, enhanced the wind gusts over Finland. To further improve understanding of this case, we suggest conducting research using an ensemble approach.