Need For Input Data Research Articles

MODAL SPLIT ANALYSIS BY BEST-WORST METHOD AND MULTINOMINAL LOGIT MODEL

The behavioral features of the population are addressed in transport models by different levels of territorial disaggregation and the creation of demand strata in a territory. The need for input data grows exponentially with the demand for a detailed zonal system of the territory. The basic source is the mobility survey. This article deals with the comparison of the calculation of the probability of choosing a transport mode for trips using the classic multinominal logit model and the best-worst method. We used data from a mobility survey in the Žilina region as a basic sample. The analysis covered 11 districts and their gravity areas. The individual transport relations are evaluated in detail in the analysis. The results confirm the high degree of accuracy of the best-worst method in the calculation of mode choice on a regional scale. Despite the promising results of the agreement in the confrontation with the mobility survey, it is necessary to verify the modeled data with a more detailed area with disaggregation on-demand strata.

Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?

Abstract. Most terrestrial biosphere models (TBMs) rely on more or less detailed information about the properties of the local vegetation. In contrast, optimality-based models require much less information about the local vegetation as they are designed to predict vegetation properties based on general principles related to natural selection and physiological limits. Although such models are not expected to reproduce current vegetation behaviour as closely as models that use local information, they promise to predict the behaviour of natural vegetation under future conditions, including the effects of physiological plasticity and shifts of species composition, which are difficult to capture by extrapolation of past observations. A previous model intercomparison using conventional TBMs revealed a range of deficiencies in reproducing water and carbon fluxes for savanna sites along a precipitation gradient of the North Australian Tropical Transect (Whitley et al., 2016). Here, we examine the ability of an optimality-based model (the Vegetation Optimality Model, VOM) to predict vegetation behaviour for the same savanna sites. The VOM optimizes key vegetation properties such as foliage cover, rooting depth and water use parameters in order to maximize the net carbon profit (NCP), defined as the difference between total carbon taken up by photosynthesis minus the carbon invested in construction and maintenance of plant organs. Despite a reduced need for input data, the VOM performed similarly to or better than the conventional TBMs in terms of reproducing the seasonal amplitude and mean annual fluxes recorded by flux towers at the different sites. It had a relative error of 0.08 for the seasonal amplitude in ET and was among the three best models tested with the smallest relative error in the seasonal amplitude of gross primary productivity (GPP). Nevertheless, the VOM displayed some persistent deviations from observations, especially for GPP, namely an underestimation of dry season evapotranspiration at the wettest site, suggesting that the hydrological assumptions (free drainage) have a strong influence on the results. Furthermore, our study exposes a persistent overprediction of vegetation cover and carbon uptake during the wet seasons by the VOM. Our analysis revealed several areas for improvement in the VOM and the applied optimality theory, including a better representation of the hydrological settings as well as the costs and benefits related to plant water transport and light capture by the canopy. The results of this study imply that vegetation optimality is a promising approach to explain vegetation dynamics and the resulting fluxes. It provides a way to derive vegetation properties independently of observations and allows for a more insightful evaluation of model shortcomings as no calibration or site-specific information is required.

Application and performance evaluation of time series, neural networks and HARTT models in predicting groundwater level changes, Najafabad Plain, Iran

In this study, the application and performance of three models, including artificial neural networks, HARTT model and time series models for predicting the groundwater level in Najafabad plain were evaluated. For this purpose, SPSS, MATLAB and HARTT software packages were used to construct and evaluate each of these methods. Different parameters were used as inputs to estimate the groundwater level in each of these models. The results showed that all three models are capable of estimating the groundwater level with relatively good accuracy. However, the accuracy of seasonal ARIMA models and neural networks is higher than that of the HARTT model, though the performances of the two models are relatively similar. Based on the goodness of fit results, the seasonal ARIMA models are more appropriate for groundwater level prediction, because of the lower error, but the seasonal ARIMA models require some assumptions on the errors that make the modeling difficult, while the artificial neural network models have no such problems. But artificial neural networks have some limitations, such as the lack of a comprehensive theory and the need for a large number of observations due to the nonlinear structure of the networks. In addition, neural network inputs, like coefficients of regression models, are not interpretable and act as a black-box model. Therefore, they are only suitable for prediction and are not applicable for analyzing and interpreting the effects of each parameter on the groundwater level. Also one of the benefits of ARIMA models is that it can be used for short-term groundwater level prediction without the need for input data. This is especially important for areas without observation data or areas where data are limited. Furthermore, the results show that in addition to the ability of groundwater level prediction, the HARTT model can predict the effects of precipitation changes on groundwater level and the time delay between precipitation and its effect on the groundwater level, which this delay for different wells, based on the different effective parameters, is different.

Comparison Between a Detailed Pinch Analysis and the ‘Heat Load Model for Pulp and Paper’ – Case Study for a Swedish Thermo-Mechanical Pulp and Paper Mill

Pinch analysis has been used for several decades as a tool for making industrial processes more energy efficient by identifying process integration opportunities. Hakala et al. (2008) recognise that pinch analysis is a powerful tool when it comes to improving energy efficiency in mechanical pulp and paper mills, however often very time consuming due to the extensive need for input data. The heat load model for pulp and paper (HLMPP) tool was developed at Aalto University in Finland as a means of providing a flexible tool for a first quick scan of process integration potential. The intention of this study is to evaluate if the model can accurately estimate the data necessary for performing a pinch analysis for a Swedish thermo-mechanical pulp (TMP) and paper mill. Jonsson et al. (2010) used the HLMPP tool to evaluate the potential for steam savings for four Scandinavian TMP mills. It was found that the minimum steam demands were 2-20 % lower than the current consumptions in the mills. In this study, a detailed pinch analysis was carried out for one of the studied mills described by Jonsson (the mill with the lowest energy savings potential according to the HLMPP screening) to identify strengths and shortcomings of the HLMPP tool. An initial comparison shows that the pinch temperature and demand for hot and cold utility predicted by the HLMPP tool, as presented by Jonsson, differs from the detailed pinch analysis. However, further investigation showed that the HLMPP results can be aligned to the detailed data with good accuracy if more time and knowledge about the process is put in to the model.

Verification of Appropriateness of Selected Pedotransfer Functions for the Basic Use in Agriculture of the Czech Republic

Soil is a huge reservoir of water used by plants in periods without precipitation and significantly affects the hydrological balance of any territory. To evaluate the hydrological balance of any piece of the land given, it is therefore necessary to carry out the analysis of the hydro-physical conditions. To speed up and simplify the determination of basic hydro-physical properties of soil man developed and began to use the so-called pedotransfer functions (PTF). Comparison of domestic and foreign works, however, faces problems such as the definition of available water supplies in different countries. In the research project “Specification of the available supplies of nitrogen and water in the soil profile and determining the effective depth of crops’ roots” we have dealt with the selection of suitable pedotransfer functions necessary for basic agricultural production with the requirement of minimal amount of input data. For our research we chose several PTFs developed and used in the Czech Republic for a long time, with a minimum of input data, and several new PTFs from foreign authors with greater correlation, but also a greater need of input data, and we compared each other. The best correlation between values and the pedotransfer function for the field water capacity and for the wilting point seems to be the PTF according to Tomasella and PTF according to Batjes. Pedotransfer function according to Váša, in terms of volume of input data, appears better.

Component failure rate data sources for probabilistic safety and reliability

AbstractProbabilistic safety methods, which are being used in the chemical, manufacturing, and energy industries, create a basic need for input data on failure rates of the mechanical, electrical, instrumentation and control, and other components that comprise the engineering systems in a facility. Some companies have data stored and easy to retrieve. Other companies hire consultants who use their own databases to perform safety assessments. For analysts who do not have either of these options available, this article presents data sources that are retrievable from the literature. The accessibility of data documents via the internet is also described. © 2010 American Institute of Chemical Engineers Process Saf Prog, 2010

Modelling drainage fluxes in managed and natural forests in the Dinaric karst: a model comparison study

Two models for calculating the forest water balance were applied to different development stages of managed and non-managed forests in the Dinaric Karst for two hydrologically contrasting growing seasons. A simple model WATBAL, which calculates water balance on a monthly basis, and the BROOK90 model, which calculates water balance on daily basis, were used. Differences between calculated drainage fluxes between the models were less pronounced in the drier growing season and were lower in the forest stands compared to forest gaps. Average calculated drainage fluxes of the two growing seasons were highest in the gaps and lowest in the stand in the virgin forest remnant, followed by the mature stand in the managed forest. According to model fitting, testing the calibration robustness and sensitivity analysis the BROOK90 model was considered best at simulating the water balance of the various research sites. The difference in model behaviour is considered to be mainly the result of the difference in model time step and the inclusion of macropore flow in BROOK90. The greater complexity of the BROOK90 model meant it could be parameterized to describe more fully the complexity of the horizontal and vertical structure of forest stand and soil properties. A disadvantage of the BROOK90 model is the greater need of input data. WATBAL, however, was useful for obtaining rougher estimates of the water balance components and can be applied to areas where there is less data available. Choice of model is therefore determined by data availability.

Quantitative Analysis of Microstructure and Modeling of Sintering

Microstructure is the key scale to understand and describe sintering mechanisms and their consequences at the macroscopic level. As modeling techniques are continuously developing, the need for input data and comparison with more and more accurate descriptions of the evolution is expected to create a growing demand for quantitative microstructure data. Image analysis is the classic way to get these data. This paper reviews the practical use and progresses of this old technique in the sintering literature during the past and recent years. The place of basic tools and more recent ones, such as 3D imaging, are discussed from a practical point of view accounting from sintering models needs: mean size and size distributions in pores and grains, homogeneity, sintering trajectories…

Probabilistic seismic hazard assessment of the Pyrenean region

A unified probabilistic seismic hazard assessment (PSHA) for the Pyrenean region has been performed by an international team composed of experts from Spain and France during the Interreg IIIA ISARD project. It is motivated by incoherencies between the seismic hazard zonations of the design codes of France and Spain and by the need for input data to be used to define earthquake scenarios. A great effort was invested in the homogenisation of the input data. All existing seismic data are collected in a database and lead to a unified catalogue using a local magnitude scale. PSHA has been performed using logic trees combined with Monte Carlo simulations to account for both epistemic and aleatory uncertainties. As an alternative to hazard calculation based on seismic sources zone models, a zoneless method is also used to produce a hazard map less dependant on zone boundaries. Two seismogenic source models were defined to take into account the different interpretations existing among specialists. A new regional ground-motion prediction equation based on regional data has been proposed. It was used in combination with published ground-motion prediction equations derived using European and Mediterranean data. The application of this methodology leads to the definition of seismic hazard maps for 475- and 1,975-year return periods for spectral accelerations at periods of 0 (corresponding to peak ground acceleration), 0.1, 0.3, 0.6, 1 and 2 s. Median and percentiles 15% and 85% acceleration contour lines are represented. Finally, the seismic catalogue is used to produce a map of the maximum acceleration expected for comparison with the probabilistic hazard maps. The hazard maps are produced using a grid of 0.1°. The results obtained may be useful for civil protection and risk prevention purposes in France, Spain and Andorra.

A nonsymmetric state-variable decomposition for modal analysis

A modal decomposition strategy based on state-variable ensembles is formulated. A nonsymmetric, generalized eigenvalue problem is constructed. The data-based eigenvalue problem is related to the generalized eigenvalue problem associated with free-vibration solutions of the state-variable formulation of linear multi-degree-of-freedom systems. For linear free-response data, the inverse-transpose of the eigenvector matrix converges to the state-variable modal eigenvectors, and the eigenvalues of the nonsymmetric eigenvalue problem approximate those of the state-variable model. As such, the eigenvalues lead to estimates of frequencies and modal damping. The interpretation holds for linear systems with multi-modal free responses, whether damping is small or somewhat large, modal or nonmodal, and without the need of input data.

A cloud screening algorithm for daytime AVHRR data using dynamic thresholds

Cloud screening algorithms often use fixed thresholds for reflectances and temperatures and are not applicable to regions with a great varibility in surface reflectance characteristics. A dynamic threshold algorithm was developed for application to a broad spectrum of earth surfaces, such as desert areas with very high reflectances and areas with dense vegetation. No a priori information is needed to activate the unsupervised algorithm. Using the maximum Normalized Difference Vegetation Index (NDVI) composites of a ten-day period, the corresponding reflectance and temperature maps are used as dynamic thresholds. Empirically derived nadir correction factors reduce the influence of surface anisotropy and tighten the algorithm considerably. Thus the only use of reflectance data results in a reliable, surface independent, cloud screening. Additionally, tests with thermal channels can be applied for confirmation and classification of questionable mixed pixels. Since there is no need for input data, the method can be used on a near global scale. The algorithm was developed for NDVI evaluation. No attempts were made so far to distinguish between clouds and snow or ice.