Computer Analysis of Twin Box Culverts, 1959

The paper presents a method of analyzing Rigid Frames by use of the Conjugate Beam Theory. The development of the method along with an example is given. This method has been used to write a computer program for the analysis of twin box culverts. The culverts may be analyzed under any fill height and any of the standard truck loadings. The wall and slab thickness are increased by the computer program as necessary. The final result is steel requirements both for moment and shear, and the slab and wall thickness.

Forecasting the Earth"s radiation belts and modeling solar energetic particle events: Recent results from SPACECAST

High-energy charged particles in the van Allen radiation belts and in solar energetic particle events can damage satellites on orbit leading to malfunctions and loss of satellite service. Here we describe some recent results from the SPACECAST project on modelling and forecasting the radiation belts, and modelling solar energetic particle events. We describe the SPACECAST forecasting system that uses physical models that include wave-particle interactions to forecast the electron radiation belts up to 3 h ahead. We show that the forecasts were able to reproduce the >2 MeV electron flux at GOES 13 during the moderate storm of 7-8 October 2012, and the period following a fast solar wind stream on 25-26 October 2012 to within a factor of 5 or so. At lower energies of 10- a few 100 keV we show that the electron flux at geostationary orbit depends sensitively on the high-energy tail of the source distribution near 10 RE on the nightside of the Earth, and that the source is best represented by a kappa distribution. We present a new model of whistler mode chorus determined from multiple satellite measurements which shows that the effects of wave-particle interactions beyond geostationary orbit are likely to be very significant. We also present radial diffusion coefficients calculated from satellite data at geostationary orbit which vary with Kp by over four orders of magnitude. We describe a new automated method to determine the position at the shock that is magnetically connected to the Earth for modelling solar energetic particle events and which takes into account entropy, and predict the form of the mean free path in the foreshock, and particle injection efficiency at the shock from analytical theory which can be tested in simulations.

Methods of Telecommunication Technology Forecasting

This master's thesis coversthe concepts of knowledge discovery, data mining and technology forecasting methods in telecommunications. It covers the various aspects of knowledge discoveryin data bases and discusses in detail the methods of data mining and technologyforecasting methods that are used in telecommunications. Main concern in the overall process of this thesis is to emphasize the methods that are being used in technology forecasting for telecommunications and data mining. It tries to answer to some extent to the question of do forecasts create a future? It also describes few difficulties that arise in technology forecasting. This thesis was done as part of my master's studies in Lappeenranta University of Technology.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Forecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, three-dimensional geostatistical methods are used to populate facies distributionwithin a densely drilled heterogeneous coal zone in the As Pontes Basin (NWSpain). Coal distribution in this zone is mainly characterized by coal-dominated areas in the central parts of the basin interfingering with terrigenous-dominated alluvial fan zones at the margins. The three-dimensional models obtained are applied to forecast coal resources and reserves. Predictions using subsets of the entire dataset are also generated to understand the performance of methods under limited data constraints. Three-dimensional facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a block can be recovered or not, and b) the capability of the modelling strategy to reproduce areal trends in coal proportions and splitting between coal-dominated and terrigenousdominated areas of the basin. Reserves predictions differ between the simulation methods, even with dense conditioning datasets. Simulation methods can be ranked according to the correlation of their outputs with predictions from the directly interpolated coal proportion maps: a) with low-density datasets sequential indicator simulation with trends yields the best correlation, b) with high-density datasets sequential indicator simulation with post-processing yields the best correlation, because the areal trends are provided implicitly by the dense conditioning data.

Introducing massively parallel computers into operational weather forecasting

Numerical weather prediction and climate simulation have been among the computationally most demanding applications of high performance computing eversince they were started in the 1950's. Since the 1980's, the most powerful computers have featured an ever larger number of processors. By the early 2000's, this number is often several thousand. An operational weather model must use all these processors in a highly coordinated fashion. The critical resource in running such models is not computation, but the amount of necessary communication between the processors. The communication capacity of parallel computers often fallsfar short of their computational power. The articles in this thesis cover fourteen years of research into how to harness thousands of processors on a single weather forecast or climate simulation, so that the application can benefit as much as possible from the power of parallel high performance computers. The resultsattained in these articles have already been widely applied, so that currently most of the organizations that carry out global weather forecasting or climate simulation anywhere in the world use methods introduced in them. Some further studies extend parallelization opportunities into other parts of the weather forecasting environment, in particular to data assimilation of satellite observations.

Mobile Communications within the European Union: The Role of Location in the Evolution and Forecasting of the Diffusion Process

In the European Union, the importance of mobile communications was realized early on. The process of mobile communications becoming ubiquitous has taken time, as the innovation of mobile communications diffused into the society. The aim of this study is to find out how the evolution and spatial patterns of the diffusion of mobile communications within the European Union could be taken into account in forecasting the diffusion process. There is relatively lot of research of innovation diffusion on the individual (micro) andthe country (macro) level, if compared to the territorial level. Territorial orspatial diffusion refers either to the intra-country or inter-country diffusionof an innovation. In both settings, the dif- fusion of a technological innovation has gained scarce attention. This study adds knowledge of the diffusion between countries, focusing especially on the role of location in this process. The main findings of the study are the following: The penetration rates of the European Union member countries have become more even in the period of observation, from the year 1981 to 2000. The common digital GSM system seems to have hastened this process. As to the role of location in the diffusion process, neighboring countries have had similar diffusion processes. They can be grouped into three, the Nordic countries, the central and southern European countries, and the remote southern European countries. The neighborhood effect is also domi- nating in thegravity model which is used for modeling the adoption timing of the countries. The subsequent diffusion within a country, measured by the logistic model in Finland, is af- fected positively by its economic situation, and it seems to level off at some 92 %. Considering the launch of future mobile communications systemsusing a common standard should implicate an equal development between the countries. The launching time should be carefully selected as the diffusion is probably delayed in economic downturns. The location of a country, measured by distance, can be used in forecasting the adoption and diffusion. Fi- nally, the result of penetration rates becoming more even implies that in a relatively homoge- nous set of countries, such as the European Union member countries, the estimated final pene- tration of a single country can be used for approximating the penetration of the others. The estimated eventual penetration of Finland, some 92 %, should thus also be the eventual level for all the European Union countries and for the European Union as a whole.

Relationships between climate and winter cereal grain quality in Finland and their potential for forecasting

Selostus: Sään vaikutus syysviljojen hehtolitran painoon ennusteen laadinnan näkökulmasta

Using time and stochastic models for software reliability forecasting and analysis

Tämä työ luo katsauksen ajallisiin ja stokastisiin ohjelmien luotettavuus malleihin sekä tutkii muutamia malleja käytännössä. Työn teoriaosuus sisältää ohjelmien luotettavuuden kuvauksessa ja arvioinnissa käytetyt keskeiset määritelmät ja metriikan sekä varsinaiset mallien kuvaukset. Työssä esitellään kaksi ohjelmien luotettavuusryhmää. Ensimmäinen ryhmä ovat riskiin perustuvat mallit. Toinen ryhmä käsittää virheiden ”kylvöön” ja merkitsevyyteen perustuvat mallit. Työn empiirinen osa sisältää kokeiden kuvaukset ja tulokset. Kokeet suoritettiin käyttämällä kolmea ensimmäiseen ryhmään kuuluvaa mallia: Jelinski-Moranda mallia, ensimmäistä geometrista mallia sekä yksinkertaista eksponenttimallia. Kokeiden tarkoituksena oli tutkia, kuinka syötetyn datan distribuutio vaikuttaa mallien toimivuuteen sekä kuinka herkkiä mallit ovat syötetyn datan määrän muutoksille. Jelinski-Moranda malli osoittautui herkimmäksi distribuutiolle konvergaatio-ongelmien vuoksi, ensimmäinen geometrinen malli herkimmäksi datan määrän muutoksille.

Short-term forecasting of power demand in the Nord Pool market

Sähkönkulutuksen lyhyen aikavälin ennustamista on tutkittu jo pitkään. Pohjoismaisien sähkömarkkinoiden vapautuminen on vaikuttanut sähkönkulutuksen ennustamiseen. Aluksi työssä perehdyttiin aiheeseen liittyvään kirjallisuuteen. Sähkönkulutuksen käyttäytymistä tutkittiin eri aikoina. Lämpötila tilastojen käyttökelpoisuutta arvioitiin sähkönkulutusennustetta ajatellen. Kulutus ennusteet tehtiin tunneittain ja ennustejaksona käytettiin yhtä viikkoa. Työssä tutkittiin sähkönkulutuksen- ja lämpötiladatan saatavuutta ja laatua Nord Poolin markkina-alueelta. Syötettävien tietojen ominaisuudet vaikuttavat tunnittaiseen sähkönkulutuksen ennustamiseen. Sähkönkulutuksen ennustamista varten mallinnettiin kaksi lähestymistapaa. Testattavina malleina käytettiin regressiomallia ja autoregressiivistä mallia (autoregressive model, ARX). Mallien parametrit estimoitiin pienimmän neliösumman menetelmällä. Tulokset osoittavat että kulutus- ja lämpötiladata on tarkastettava jälkikäteen koska reaaliaikaisen syötetietojen laatu on huonoa. Lämpötila vaikuttaa kulutukseen talvella, mutta se voidaan jättää huomiotta kesäkaudella. Regressiomalli on vakaampi kuin ARX malli. Regressiomallin virhetermi voidaan mallintaa aikasarjamallia hyväksikäyttäen.

Wireless e-Business Applications - Methods for Forecasting the Demand of Wireless Services and Technologies

Tämä tutkimus oli osa sähköistä liiketoimintaa ja langattomia sovelluksia tutkivaa projektia ja tutkimuksen tavoitteena oli selvittää ennustamisen rooli päätöksenteko- ja suunnitteluprosessissa ja määrittää parhaiten soveltuvat ja useimmin käytetyt teknologian ennustusmenetelmät. Ennustusmenetelmiä tarkasteltiin erityisesti uuden teknologian ja pitkän aikavälin ennustamisen näkökulmasta. Tutkimus perustui teknologista ennustamista, pitkän aikavälin suunnittelua ja innovaatioprosesseja käsittelevän kirjallisuuden analysointiin. Materiaalin perusteella kuvataan teknologian ennustamista informaation hankkimisvälineenä organisaatioiden suunnitteluprosessin apuna. Työssä arvioidaan myös seuraavat teknologisen ennustamisen menetelmät: trendianalyysi-, Delfoi-, cross-impact analyysi-, morfologinen analyysi- ja skenaario analyysimenetelmä. Työ tuo esille jokaisen ennustusmenetelmä ominaispiirteet, rajoitukset ja sovellusmahdollisuudet. Käyttäen esiteltyjä menetelmiä, saadaan kerättyä hyödyllistä informaatiota tulevaisuuden näkymistä, joita sitten voidaan käyttää hyväksi organisaatioiden suunnitteluprosesseissa.

Preface: Extreme events induced by weather and climate change: evaluation, forecasting and proactive planning

This special issue of Natural Hazards and Earth System Sciences (NHESS) contains eight papers presented as oral or poster contributions in the Natural Hazards NH-1.2 session on"Extreme events induced by weather and climate change: evaluation, forecasting and proactive planning", held at the European Geosciences Union (EGU) General Assembly in Vienna, Austria, on 13-18 April 2008. The aim of the session was to provide an international forum for presenting new results and for discussing innovative ideas and concepts on extreme hydro-meteorological events, including: (i) the assessment of the risk posed by the extreme events, (ii) the expected changes in the frequency and intensity of the events driven by a changing climate and by multiple human- induced causes, (iii) new modelling approaches and original forecasting methods to predict extreme events and their consequences, and (iv) strategies for hazard mitigation and risk reduction, and for a improved adaptation to extreme hydro-meteorological events ...

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper