Journal articles: 'Model Transferability'

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Koppelman, Frank S., and Eric I. Pas. "Multidimensional choice model transferability." Transportation Research Part B: Methodological 20, no. 4 (August 1986): 321–30. http://dx.doi.org/10.1016/0191-2615(86)90046-9.

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MacDougall, A. H., B. A. Wheler, and G. E. Flowers. "Assessment of glacier melt-model transferability: comparison of temperature-index and energy-balance models." Cryosphere Discussions 4, no. 4 (October 20, 2010): 2143–67. http://dx.doi.org/10.5194/tcd-4-2143-2010.

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Abstract. Transferability of glacier melt models is necessary for reliable projections of melt over large glacierized regions and over long time-scales. The transferability of such models has been examined for individual model types, but inter-comparison has been hindered by the diversity of validation statistics used to quantify transferability. We apply four common types of melt models – the classical degree-day model, an enhanced temperature-index model, a simplified energy-balance model and a full energy-balance model – to two glaciers in the same small mountain range. The transferability of each model is examined in space and over two melt seasons. We find that the full energy balance model is consistently the most transferable, with deviations in estimated glacier-wide surface ablation of ≤ 35% when the model is forced with parameters derived from the other glacier and/or melt season. The other three models have deviations in glacier-wide surface ablation of ≥ 100% under the same forcings. In addition, we find that there is no simple relationship between model complexity and model transferability.

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Abdelwahab, Walid M. "Transferability of intercity disaggregate mode choice models in Canada." Canadian Journal of Civil Engineering 18, no. 1 (February 1, 1991): 20–26. http://dx.doi.org/10.1139/l91-003.

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In many transportation studies, the time span of data collection, model development, and analysis is often too long to be responsive to the needs of policy analysts and decision makers. This problem is often exacerbated in situations with severely constrained analysis resources. Therefore, it is often useful to transfer a model from one area to another. Model transfer is defined as the application of a model developed in one area to describe the corresponding behavior in another area. This paper examines the transferability of a class of models used in intercity travel demand analysis. Specifically, disaggregate mode choice models of the multinomial logit type are developed for two regions in Canada, and some established measures of transferability are applied to assess the potential of calibrating these models in one region and applying them in the other. Comparison of mode choice models estimated on data sets from the two regions yielded inconclusive results regarding model transferability. In general, transferred models were found to be 18–23% less accurate than local models in predicting modal shares. Adjusting models' parameters to reflect observed modal shares in the application context improved the predictive ability of the models by about 10%. Key words: transferability, mode choice, disaggregate, travel behavior, multinomial logit, intercity.

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Flavia, Anyiko, and Charisma Choudhury. "Temporal Transferability of Vehicle Ownership Models in the Developing World: Case Study of Dhaka, Bangladesh." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (March 2019): 722–32. http://dx.doi.org/10.1177/0361198119836760.

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Temporal transferability of model parameters is a critical issue, especially in the context of developing countries where data and resources for transport model development are extremely limited. This study investigates the temporal transferability of vehicle ownership models with special emphasis on exploring the effect of model structure on temporal transferability. The performance of potential updating methods for making the models more transferable are also compared. The household survey data collected from Dhaka, Bangladesh in 2005 and 2010 have been used in this regard. Different forms of random utility and count regression models of car, motorcycle, and bicycle ownership have been developed using income and household size, and number of workers, children, and licensed drivers as explanatory variables. The temporal transferability of each model between the two time periods has been compared rigorously using statistical tests. Results indicate that the multinomial logit model has better temporal transferability than the count regression models. In relation to model updating, the combined transfer estimation method for model updating is found to perform better than the Bayesian updating. The findings can provide useful guidance during application of a pre-existing model in the context of a developing country.

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Bowman, John L., and Mark Bradley. "Testing Spatial Transferability of Activity-Based Travel Forecasting Models." Transportation Research Record: Journal of the Transportation Research Board 2669, no. 1 (January 2017): 62–71. http://dx.doi.org/10.3141/2669-07.

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This paper reports results from the second phase of a two-phase FHWA-sponsored project to test and demonstrate empirically the transferability of activity-based model (ABM) systems between regions. With data obtained from the 2008–2009 National Household Travel Survey, researchers estimated ABMs simultaneously for 13 metropolitan regions in seven U.S. states. Statistical tests were used to test transferability, including tests of regional differences in the model coefficients, likelihood ratio tests of model equivalence, and transferability indexes, which measure the degree of model differences. In addition, differences in prediction sensitivity between locally estimated and transferred models were tested. The project overall found evidence in favor of transferability. It also found that parameters associated with land use, logsum accessibilities, and travel time and cost caused the biggest problems with transferability. Finally, the study found that transferring within a state or between regions with similar urban density improves transferability. This paper presents the data, models, and testing methods used in the project and includes details of all tests and results related to the improved transferability associated with model transfers from regions within the same state or with similar urban density. The conclusion of the study was that agencies considering transfer of an ABM from another region would do well to find a region within the same state or with similar urban density that has a model that is well supported by a large household travel survey data set.

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Kumarage, Amal S., and S. C. Wirasinghe. "Transferability of aggregate intercity total demand models." Canadian Journal of Civil Engineering 22, no. 2 (April 1, 1995): 283–91. http://dx.doi.org/10.1139/l95-037.

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Over the last 15 years, extensive research has been done on the transferability of travel demand models. However, much of this work has been concentrated towards investigating the transferability of disaggregate mode choice models. The transferability of an aggregate total demand model for intercity travel is examined. Model transfer is possible only when a number of preconditions for transferability are satisfied. One of the principal obstacles to the successful transfer of intercity demand models is the inability to overcome the contextual differences between calibration and application. Here, the components of the intercity total demand model are separated into exogenous and intrinsic (contextual) factors. The latter is thereafter classified as being either transferable or nontransferable. It is shown that transferable attributes can accompany a model during transfer. Nontransferable attributes, on the other hand, will free the model of city or city-pair specific contextual characteristics which should not be transferred to other city pairs. The issues involved in transferring an aggregate model are also investigated. Aggregate data on interdistrict travel by public transportation in Sri Lanka have been used to successfully calibrate a total demand model with a number of transferable and nontransferable attributes that represent both temporal and spatial contextual factors. It is shown that the forecasting ability of this model is far superior to a counterpart model without the intrinsic variables. Key words: travel demand, aggregate, forecasting, transferability, intercity, Sri Lanka.

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Ko, Namuk, Byeongki Jeong, Wonchul Seo, and Janghyeok Yoon. "A transferability evaluation model for intellectual property." Computers & Industrial Engineering 131 (May 2019): 344–55. http://dx.doi.org/10.1016/j.cie.2019.04.011.

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Gao, Hongkai, Tianding Han, Youcun Liu, and Qiudong Zhao. "Use of auxiliary data of topography, snow and ice to improve model performance in a glacier-dominated catchment in Central Asia." Hydrology Research 48, no. 5 (December 2, 2016): 1418–37. http://dx.doi.org/10.2166/nh.2016.242.

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Whether coupling auxiliary information (except for conventional rainfall–runoff and temperature data) into hydrological models can improve model performance and transferability is still an open question. In this study, we chose a glacier catchment to test the effect of auxiliary information, i.e., distributed forcing input, topography, snow-ice accumulation and melting on model calibration–validation and transferability. First, we applied the point observed precipitation and temperature as forcing data, to test the model performance in calibration–validation and transferability. Second, we took spatial distribution of forcing data into account, and did the same test. Third, the aspect was involved to do an identical experiment. Finally, the snow–ice simulation was used as part of the objective function in calibration, and to conduct the same experiment. Through stepwisely accounting these three pieces of auxiliary information, we found that a model without involving forcing data distribution, local relief, or snow–ice data can also perform well in calibration, but adding forcing data distribution and topography can dramatically increase model validation and transferability. It is also remarkable that including the snow–ice simulation into objective function did not improve model performance and transferability in this study. This may be because the well-gauged hydro-meteorological data are sufficient to constrain a well-designed hydrological model.

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Kontogianni, Areti, Dimitris Damigos, Michail Skourtos, Christos Tourkolias, Eleanor Denny, Ibon Galarraga, Steffen Kallbekken, and Edin Lakić. "Model Validity and Transferability Informing Behavioral Energy Policies." Energies 14, no. 11 (May 27, 2021): 3122. http://dx.doi.org/10.3390/en14113122.

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A number of microeconomic choice models are currently applied to demonstrate systematic biases in energy consumer behavior. The models highlight the hidden potential of energy savings from policies that target the so-called behavioral anomalies. Nevertheless, whether these patterns are repeatable or not is not clear, because the efforts to determine the transferability or generalizability of these models are practically nonexistent. This paper uses a unique collection of empirical data from five EU countries collected within the CONSEED project to refine and develop further the standard consumer decision model, validate it for policy purposes, and elaborate on its transferability between countries. The pooled samples allow for a more reliable investigation of the relative importance of the factors influencing consumers’ attitudes and beliefs towards energy investment decisions. Based on the statistical tests conducted to evaluate the “transferability” of the pooled models (i.e., the possibility of creating a “universal” model of EE from the pooled model), it can be argued that the models are transferable in specific cases since attitudinal factors and demographic characteristics play a significant role. Although the pooled models are validated, any extrapolation of the above-mentioned findings to specific populations in terms of “space” (i.e., country) and “target” (e.g., sectors and technologies) should be approached with caution from a policy perspective.

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MacDougall, Andrew H., and Gwenn E. Flowers. "Spatial and Temporal Transferability of a Distributed Energy-Balance Glacier Melt Model." Journal of Climate 24, no. 5 (March 1, 2011): 1480–98. http://dx.doi.org/10.1175/2010jcli3821.1.

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Abstract Modeling melt from glaciers is crucial to assessing regional hydrology and eustatic sea level rise. The transferability of such models in space and time has been widely assumed but rarely tested. To investigate melt model transferability, a distributed energy-balance melt model (DEBM) is applied to two small glaciers of opposing aspects that are 10 km apart in the Donjek Range of the St. Elias Mountains, Yukon Territory, Canada. An analysis is conducted in four stages to assess the transferability of the DEBM in space and time: 1) locally derived model parameter values and meteorological forcing variables are used to assess model skill; 2) model parameter values are transferred between glacier sites and between years of study; 3) measured meteorological forcing variables are transferred between glaciers using locally derived parameter values; 4) both model parameter values and measured meteorological forcing variables are transferred from one glacier site to the other, treating the second glacier site as an extension of the first. The model parameters are transferable in time to within a <10% uncertainty in the calculated surface ablation over most or all of a melt season. Transferring model parameters or meteorological forcing variables in space creates large errors in modeled ablation. If select quantities (ice albedo, initial snow depth, and summer snowfall) are retained at their locally measured values, model transferability can be improved to achieve ≤15% uncertainty in the calculated surface ablation.

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Klodzinski, Jack, and Haitham M. Al-Deek. "Transferability of a Stochastic Toll Plaza Computer Model." Transportation Research Record: Journal of the Transportation Research Board 1811, no. 1 (January 2002): 40–49. http://dx.doi.org/10.3141/1811-05.

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Econopouly, T. W., D. R. Davis, and D. A. Woolhiser. "Parameter transferability for a daily rainfall disaggregation model." Journal of Hydrology 118, no. 1-4 (October 1990): 209–28. http://dx.doi.org/10.1016/0022-1694(90)90259-z.

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Carswell, C., P. McWilliams, K. A. Williamson, and D. Faulds. "PRM8 General Transferability of Model-Based Economic Evaluations." Value in Health 15, no. 4 (June 2012): A160. http://dx.doi.org/10.1016/j.jval.2012.03.865.

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Wang, Xuesong, Zhigui Chen, Qiming Guo, Andrew Tarko, Cristhian Lizarazo, and Xiaomeng Wang. "Transferability analysis of the freeway continuous speed model." Accident Analysis & Prevention 151 (March 2021): 105944. http://dx.doi.org/10.1016/j.aap.2020.105944.

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Xiao, Yindong, Xueqian Huang, and Ke Liu. "Model Transferability from ImageNet to Lithography Hotspot Detection." Journal of Electronic Testing 37, no. 1 (February 2021): 141–49. http://dx.doi.org/10.1007/s10836-021-05925-5.

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Curreri, Francesco, Luca Patanè, and Maria Gabriella Xibilia. "Soft Sensor Transferability: A Survey." Applied Sciences 11, no. 16 (August 21, 2021): 7710. http://dx.doi.org/10.3390/app11167710.

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Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation of real-time control and monitoring of the plants and present other advantages in terms of costs and efforts. Given the complexity of industrial processes, these models are generally designed with data-driven black-box machine learning (ML) techniques. ML methods work well only if the data on which the prediction is performed share the same distribution with the one on which the model was trained. This is not always possible, since plants can often show new working conditions. Even similar plants show different data distributions, making SSs not scalable between them. Models should then be created from scratch with highly time-consuming procedures. Transfer Learning (TL) is a field of ML that re-uses the knowledge from one task to learn a new different, but related, one. TL techniques are mainly used for classification tasks. Only recently TL techniques have been adopted in the SS field. The proposed survey reports the state of the art of TL techniques for nonlinear dynamical SSs design. Methods and applications are discussed and the new directions of this research field are depicted.

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Takle, E. S., J. Roads, B. Rockel, W. J. Gutowski, R. W. Arritt, I. Meinke, C. G. Jones, and A. Zadra. "Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget." Bulletin of the American Meteorological Society 88, no. 3 (March 1, 2007): 375–84. http://dx.doi.org/10.1175/bams-88-3-375.

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A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

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van der Heijden, R. E. C. M., and H. J. P. Timmermans. "The Spatial Transferability of a Decompositional Multiattribute Preference Model." Environment and Planning A: Economy and Space 20, no. 8 (August 1988): 1013–25. http://dx.doi.org/10.1068/a201013.

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An empirical test is made of the spatial transferability of a decompositional multiattribute preference model, in the context of spatial shopping behaviour. The model which is estimated for the city of Maastricht is used to predict shopping patterns in a part of the city of Eindhoven. The results indicate that the goodness of fit of the model reduces only slightly. This supports the assumption that decompositional preference models may be used to uncover utility or preference functions which are independent of spatial structure.

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Nassar, S. A., F. F. Saccomanno, and J. H. Shortreed. "Selection of model structure for road accident severity." Canadian Journal of Civil Engineering 22, no. 5 (October 1, 1995): 981–91. http://dx.doi.org/10.1139/l95-114.

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The presence of rare events in the accident data and transferability of error from one severity level to the other are just two of the issues in modelling accident severity. This paper explores the implications of different model structures and evaluation criteria using a common data set. Evaluation criteria are statistical fit of submodels, stability and validity of risk factors, overall model prediction, and case-specific model predictions. Model structures considered were the sequence of injury severity in the model structure, sequential versus nonsequential models, degree of aggregation, and case control approach. The study concluded that all the different models produce similar results. There is no indication of transferability of error between submodels in the sequential model. It was concluded that a disaggregate model is preferred to an aggregate model given the similarity of results. Key words: road accident severity, personal injury, logit, model choice.

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Morey, Amy C., and Robert C. Venette. "Minimizing Risk and Maximizing Spatial Transferability: Challenges in Constructing a Useful Model of Potential Suitability for an Invasive Insect." Annals of the Entomological Society of America 113, no. 2 (February 11, 2020): 100–113. http://dx.doi.org/10.1093/aesa/saz049.

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Abstract Forecasting the spread and potential impacts of invasive, alien species is vital to relevant management and policy decisions. Models that estimate areas of potential suitability are useful to guide early detection and eradication, inform effective budget allocations, and justify quarantine regulations. Machine-learning is a rapidly emerging technology with myriad applications, including the analysis of factors that govern species’ distributions. However, forecasts for invasive species often require extrapolation into novel spaces, which may severely erode model reliability. Using the popular machine-learning platform, MaxEnt, we integrate numerous tools and recommendations to demonstrate a method of rigorous model development that emphasizes assessment of model transferability. Our models use Lymantria dispar dispar (L.) (Lepidoptera: Erebidae), an insect brought to the United States in the late 1860s from Europe and subsequently well monitored in spread. Recent genetic analyses provide evidence that the eastern North American population originated in Germany, France, and northern Italy. We demonstrate that models built and assessed using typical methodology for invasive species (e.g., using records from the full native geographic range) showed the smallest extent of extrapolation, but the worst transferability when validated with independent data. Conversely, models based on the purported genetic source of the eastern North American populations (i.e., a subset of the native range) showed the greatest transferability, but the largest extent of extrapolation. Overall, the model that yielded high transferability to North America and low extrapolation was built following current recommendations of spatial thinning and parameter optimization with records from both the genetic source in Europe and early North American invasion.

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Sequeira, Ana M. M., Camille Mellin, Hector M. Lozano-Montes, Jessica J. Meeuwig, Mathew A. Vanderklift, Michael D. E. Haywood, Russell C. Babcock, and M. Julian Caley. "Challenges of transferring models of fish abundance between coral reefs." PeerJ 6 (April 17, 2018): e4566. http://dx.doi.org/10.7717/peerj.4566.

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Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% <R2 < 50.6% for Acanthuridae) compared to total fish abundance (9% <R2 < 18.6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R2 < 6%,p > 0.05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.

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Kumarage, A. S., and S. C. Wirasinghe. "Updating of parameters in aggregate total demand models." Canadian Journal of Civil Engineering 19, no. 2 (April 1, 1992): 236–44. http://dx.doi.org/10.1139/l92-029.

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Research on demand-model transferability has consistently shown that the updated models perform better than the simple transfer of the original model with the original coefficients. Several methods are available for the updating of parameter estimates during model transfer. The scalar factor method has been extended to specify individual factors for each variable. This method allows the flexibility of removing insignificant variables in transfer; it also permits the grouping of parameters that have to be updated by a common factor. Individual scalar factors can also be identified for variables that are uniquely affected during transfer. This approach therefore incorporates the strength of both the sample data and the calibration model to its maximum showing that this method gives excellent fit to observed flows when tested for geographical transferability of an aggregate intercity total demand model for public transport in Sri Lanka. It is also shown that the Bayesian method becomes less efficient when sample sizes available for updating become smaller. Key words: travel, demand model, updating, transferability, Sri Lanka.

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Zhang, Weichun, Hongbin Liu, Wei Wu, Linqing Zhan, and Jing Wei. "Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability." Remote Sensing 12, no. 10 (May 19, 2020): 1620. http://dx.doi.org/10.3390/rs12101620.

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Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).

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Gurgiser, Wolfgang, Thomas Mölg, Lindsey Nicholson, and Georg Kaser. "Mass-balance model parameter transferability on a tropical glacier." Journal of Glaciology 59, no. 217 (2013): 845–58. http://dx.doi.org/10.3189/2013jog12j226.

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AbstractWe explore the small-scale spatial and temporal transferability of model parameters between two points in the ablation zone of tropical Glaciar Shallap, Cordillera Blanca, Peru (9°S, −77° W; ∼4800 m a.s.l.) in order to provide a robust assessment of the performance of a process-based glacier mass-balance model. Relative surface height change is calculated at hourly time-steps, and cumulative values are compared to surface height measurements made at irregular intervals (14–64 days) over the course of two continuous hydrological years (August 2006–August 2008). Best-performing parameter combinations were determined for each point from the outcome of 1000 model simulations for which parameters were varied randomly within a defined range. With these parameter combinations measurements for a specific location and time-span are well reproduced. Transferring the parameter combination as optimized for one location to the other location in the ablation zone increases the errors of modeled cumulative mass balance by 5–1326 mm ice eq.a−1. Transferring the parameter combinations as optimized for one year to the other year increases the modeled errors in cumulative mass balance by 18–3179 mm ice eq.a−1. Model errors generally increase during periods with frequent snowfall and snow cover. This could reflect either the inherent difficulty of modeling complex snow processes, or the inability of the model to correctly capture the pattern of albedo evolution at this site. The magnitude of errors associated with parameter transfer in space and time highlights the need for improving model performance for robust climatological and/or hydrological analyses on tropical glaciers.

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Devonec, Eve, and Ana P. Barros. "Exploring the transferability of a land-surface hydrology model." Journal of Hydrology 265, no. 1-4 (August 2002): 258–82. http://dx.doi.org/10.1016/s0022-1694(02)00111-7.

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Fu, Haoqiang, Chester G. Wilmot, and Earl J. Baker. "Sequential Logit Dynamic Travel Demand Model and Its Transferability." Transportation Research Record: Journal of the Transportation Research Board 1977, no. 1 (January 2006): 17–26. http://dx.doi.org/10.1177/0361198106197700103.

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Swierenga, H., P. J. de Groot, A. P. de Weijer, M. W. J. Derksen, and L. M. C. Buydens. "Improvement of PLS model transferability by robust wavelength selection." Chemometrics and Intelligent Laboratory Systems 41, no. 2 (July 1998): 237–48. http://dx.doi.org/10.1016/s0169-7439(98)00055-0.

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Patil, Sopan D., and Marc Stieglitz. "Comparing spatial and temporal transferability of hydrological model parameters." Journal of Hydrology 525 (June 2015): 409–17. http://dx.doi.org/10.1016/j.jhydrol.2015.04.003.

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Ben-Akiva, Moshe, Denis Bolduc, and Kalulumia Pene. "The combined estimator approach to model transferability and updating." Empirical Economics 20, no. 1 (March 1995): 167–75. http://dx.doi.org/10.1007/bf01235164.

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Ervin, Gary N., and D. Christopher Holly. "Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)." Invasive Plant Science and Management 4, no. 4 (December 2011): 390–401. http://dx.doi.org/10.1614/ipsm-d-10-00077.1.

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AbstractSpecies distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

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Moon, Jessica B., Theodore H. Dewitt, Melissa N. Errend, Randall J. F. Bruins, Mary E. Kentula, Sarah J. Chamberlain, M. Siobhan Fennessy, and Kusum J. Naithani. "Model application niche analysis: assessing the transferability and generalizability of ecological models." Ecosphere 8, no. 10 (October 2017): e01974. http://dx.doi.org/10.1002/ecs2.1974.

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Li, C. Z., L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan. "The transferability of hydrological models under nonstationary climatic conditions." Hydrology and Earth System Sciences 16, no. 4 (April 26, 2012): 1239–54. http://dx.doi.org/10.5194/hess-16-1239-2012.

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Abstract. This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 yr of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE), modified index of agreement (d1) and water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation should be used to quantify uncertainties due to parameter instability and non-uniqueness.

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Li, C. Z., L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan. "The transferability of hydrological models under nonstationary climatic conditions." Hydrology and Earth System Sciences Discussions 8, no. 5 (September 23, 2011): 8701–36. http://dx.doi.org/10.5194/hessd-8-8701-2011.

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Abstract. This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 years of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE) and absolute percentage water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation can reduce uncertainties due to parameter instability and non-uniqueness.

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Karaman, Ali, Zehra Akdeniz, and Mario P. Tosi. "Transferable Deformation-Dipole Model for Ionic Materials." Zeitschrift für Naturforschung A 62, no. 5-6 (June 1, 2007): 265–69. http://dx.doi.org/10.1515/zna-2007-5-606.

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A model for the ionic interactions in polyvalent metal halides was originally built for chloroaluminate clusters using an analysis of data on static and dynamic structure of their molecular monomers [for a review see M. P. Tosi, Phys. Chem. Liquids 43, 409 (2005)]. Recently, by continuing the deformation-dipole model calculations, the transferability of the halogen parameters was tested through the calculation of the structure of alkali halides and alkaline-earth halides. In this work we test the usefulness of the deformation-dipole model in the study of ionic materials by examining the transferability of the overlap parameters for the halogen ions across families of halide compounds. Following a comparative discussion of alkali and alkaline-earth halide monomers near equilibrium, results on alkaline-earth halides are given. By using the transferable ionic potential model we also calculate the equilibrium structure of the molecular clusters, as well as the vibrational frequencies of ACl4 compounds (where A = U, Np, Pu, Am and Th).

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Elagamy, Sania Reyad, Sherif M. El-Badawy, Sayed A. Shwaly, Zaki M. Zidan, and Usama Elrawy Shahdah. "Segmentation Effect on the Transferability of International Safety Performance Functions for Rural Roads in Egypt." Safety 6, no. 3 (September 18, 2020): 43. http://dx.doi.org/10.3390/safety6030043.

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This paper examines the transferability of the Safety Performance Function (SPF) of the Highway Safety Manual (HSM) and other 10 international SPFs for total crashes on rural multi-lane divided roads in Egypt. Four segmentation approaches are assessed in the transferability of the international SPFs, namely: (1) one-kilometer segments (S1); (2) homogenous sections (S2); (3) variable segments with respect to the presence of curvatures (S3); and (4) variable segments with respect to the presence of both curvatures and U-turns (S4). The Mean Absolute Deviation (MAD), Mean Prediction Bias (MPB), Mean Absolute Percentage Error (MAPE), Pearson χ2 statistic, and Z-score parameters are used to evaluate the performance of the transferred models. The overdispersion parameter (k) for each transferred model and each segmentation approach is recalibrated using the local data by the maximum likelihood method. Before estimating the transferability calibration factor (Cr), three methods were used to adjust the local crash prediction of the transferred models, namely: (1) the HSM default crash modification factors (CMFs); (2) local CMFs; and (3) recalibrating the constant term of the transferred model. The latter method is found to outperform the first two methods. Besides, the results show that the segmentation method would affect the performance of the transferability process. Moreover, the Italian SPFs based on the S1 segmentation method outperforms the HSM and all of the investigated international SPFs for transferring their models to the Egyptian rural roads.

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Thuy Linh, Hoang, Muhammad Adnan, Wim Ectors, Bruno Kochan, Tom Bellemens, and Vu Anh Tuan. "Exploring the Transferability of FEATHERS – An Activity Based Travel Demand Model – For Ho Chi Minh City, Vietnam." International Journal of Traffic and Transportation Management 1, no. 2 (December 15, 2019): 1–10. http://dx.doi.org/10.5383/jttm.01.02.001.

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Swierenga, H., W. G. Haanstra, A. P. De Weijer, and L. M. C. Buydens. "Comparison of Two Different Approaches toward Model Transferability in NIR Spectroscopy." Applied Spectroscopy 52, no. 1 (January 1998): 7–16. http://dx.doi.org/10.1366/0003702981942528.

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Recently, efficient methods have become available to transfer a multivariate calibration model from one instrument to another. Two categories can be distinguished: improvement of the robustness of the calibration model by, for example, a proper data preprocessing; and adaptation of the calibration model by, for example, (piecewise) direct standardization. In direct standardization, a subset from the calibration set should be measured on both instruments. Usually, however, the calibration samples cannot be measured on both instruments. When data preprocessing is applied to the transfer of multivariate calibration models, there is no need for remeasurement of a subset on both instruments. In this paper, both categories are compared for the determination of the component concentrations in a ternary mixture of methanol, ethanol, and 1-propanol using NIR spectroscopy. The calibration models obtained on one instrument are transferred to other NIR instruments. It has been found that the results of proper data preprocessing are comparable with the results obtained by direct standardization when the models are transferred over three NIR instruments.

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Huang, Yingchun, and Andras Bardossy. "Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments." Water 12, no. 9 (August 21, 2020): 2352. http://dx.doi.org/10.3390/w12092352.

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The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

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Ben Daya, Ibrahim, Mohammad Javad Shaifee, Michelle Karg, Christian Scharfenderger, and Alexander Wong. "On Robustness of Deep Neural Networks: A Comprehensive Study on the Effect of Architecture and Weight Initialization to Susceptibility and Transferability of Adversarial Attacks." Journal of Computational Vision and Imaging Systems 4, no. 1 (December 24, 2018): 3. http://dx.doi.org/10.15353/jcvis.v4i1.329.

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Neural network models have shown state of the art performance inseveral applications. However it has been observed that they aresusceptible to adversarial attacks: small perturbations to the inputthat fool a network model into mislabelling the input data. Theseattacks can also transfer from one network model to another, whichraises concerns over their applicability, particularly when there areprivacy and security risks involved. In this work, we conduct a studyto analyze the effect of network architectures and weight initial-ization on the robustness of individual network models as well astransferability of adversarial attacks. Experimental results demon-strate that while weight initialization has no affect on the robustnessof a network model, it does have an affect on attack transferabilityto a network model. Results also show that the complexity of anetwork model as indicated by the total number of parameters andMAC number is not indicative of a network’s robustness to attackor transferability, but accuracy can be; within the same architec-ture, higher accuracy usually indicates a more robust network, butacross architectures there is no strong link between accuracy androbustness.

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Papadimitriou, Stavros, and Charisma F. Choudhury. "Transferability of Car-Following Models Between Driving Simulator and Field Traffic." Transportation Research Record: Journal of the Transportation Research Board 2623, no. 1 (January 2017): 60–72. http://dx.doi.org/10.3141/2623-07.

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During the past few decades, there have been two parallel streams of driving behavior research: models using trajectory data collected from the field (using video recordings, GPS, etc.) and models using data from driving simulators (in which the behavior of the drivers is recorded in controlled laboratory conditions). Although the former source of data is more realistic, it lacks information about the driver and is typically not suitable for testing effects of future vehicle technologies and traffic scenarios. In contrast, driving behavior models developed with driving simulator data may lack behavioral realism. However, no previous study has compared these two streams of mathematical models and investigated the transferability of the models developed with driving simulator data to real field conditions in a rigorous manner. The current study aimed to fill this research gap by investigating the transferability of two car-following models between a driving simulator and two comparable real-life traffic motorway scenarios, one from the United Kingdom and the other one from the United States. In this regard, stimulus–response–based car-following models were developed with three microscopic data sources: ( a) experimental data collected from the University of Leeds driving simulator, ( b) detailed trajectory data collected from UK Motorway 1, and ( c) detailed trajectory data collected from Interstate 80 in California. The parameters of these car-following models were estimated by using the maximum likelihood estimation technique, and the transferability of the models was investigated by using statistical tests of parameter equivalence and transferability test statistics. Estimation results indicate transferability at the model level but not fully at the parameter level for both pairs of scenarios.

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Uhlenbrook, S., Y. Mohamed, and A. S. Gragne. "Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia." Hydrology and Earth System Sciences 14, no. 10 (October 29, 2010): 2153–65. http://dx.doi.org/10.5194/hess-14-2153-2010.

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Abstract. Understanding catchment hydrological processes is essential for water resources management, in particular in data scarce regions. The Gilgel Abay catchment (a major tributary into Lake Tana, source of the Blue Nile) is undergoing intensive plans for water management, which is part of larger development plans in the Blue Nile basin in Ethiopia. To obtain a better understanding of the water balance dynamics and runoff generation mechanisms and to evaluate model transferability, catchment modeling has been conducted using the conceptual hydrological model HBV. Accordingly, the catchment of the Gilgel Abay has been divided into two gauged sub-catchments (Upper Gilgel Abay and Koga) and the un-gauged part of the catchment. All available data sets were tested for stationarity, consistency and homogeneity and the data limitations (quality and quantity) are discussed. Manual calibration of the daily models for three different catchment representations, i.e. (i) lumped, (ii) lumped with multiple vegetation zones, and (iii) semi-distributed with multiple vegetation and elevation zones, showed good to satisfactory model performances with Nash-Sutcliffe efficiencies Reff > 0.75 and > 0.6 for the Upper Gilgel Abay and Koga sub-catchments, respectively. Better model results could not be obtained with manual calibration, very likely due to the limited data quality and model insufficiencies. Increasing the computation time step to 15 and 30 days improved the model performance in both sub-catchments to Reff > 0.8. Model parameter transferability tests have been conducted by interchanging parameters sets between the two gauged sub-catchments. Results showed poor performances for the daily models (0.30 < Reff < 0.67), but better performances for the 15 and 30 days models, Reff > 0.80. The transferability tests together with a sensitivity analysis using Monte Carlo simulations (more than 1 million model runs per catchment representation) explained the different hydrologic responses of the two sub-catchments, which seems to be mainly caused by the presence of dambos in Koga sub-catchment. It is concluded that daily model transferability is not feasible, while it can produce acceptable results for the 15 and 30 days models. This is very useful for water resources planning and management, but not sufficient to capture detailed hydrological processes in an ungauged area.

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Sun, Lan, Chang Hsiung, and Valton Smith. "Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers." Molecules 24, no. 10 (May 24, 2019): 1997. http://dx.doi.org/10.3390/molecules24101997.

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Recent developments in compact near infrared (NIR) instruments, including both handheld and process instruments, have enabled easy and affordable deployment of multiple instruments for various field and online or inline applications. However, historically, instrument-to-instrument variations could prohibit success when applying calibration models developed on one instrument to additional instruments. Despite the usefulness of calibration transfer techniques, they are difficult to apply when a large number of instruments and/or a large number of classes are involved. Direct model transferability was investigated in this study using miniature near-infrared (MicroNIR™) spectrometers for both classification and quantification problems. For polymer classification, high cross-unit prediction success rates were achieved with both conventional chemometric algorithms and machine learning algorithms. For active pharmaceutical ingredient quantification, low cross-unit prediction errors were achieved with the most commonly used partial least squares (PLS) regression method. This direct model transferability is enabled by the robust design of the MicroNIR™ hardware and will make deployment of multiple spectrometers for various applications more manageable.

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Agyemang-Duah, Kwaku, and Fred L. Hall. "Spatial transferability of an ordered response model of trip generation." Transportation Research Part A: Policy and Practice 31, no. 5 (September 1997): 389–402. http://dx.doi.org/10.1016/s0965-8564(96)00035-3.

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Yasmin, Farhana, Catherine Morency, and Matthew J. Roorda. "Assessment of spatial transferability of an activity-based model, TASHA." Transportation Research Part A: Policy and Practice 78 (August 2015): 200–213. http://dx.doi.org/10.1016/j.tra.2015.05.008.

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Sanko, Nobuhiro. "Criteria for selecting model updating methods for better temporal transferability." Transportmetrica A: Transport Science 16, no. 3 (January 1, 2020): 1310–32. http://dx.doi.org/10.1080/23249935.2020.1746862.

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Knies, Saskia, André J. H. A. Ament, Silvia M. A. A. Evers, and Johan L. Severens. "The Transferability of Economic Evaluations: Testing the Model of Welte." Value in Health 12, no. 5 (July 2009): 730–38. http://dx.doi.org/10.1111/j.1524-4733.2009.00525.x.

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Chen, Yongsheng, Bhagwant Persaud, and Emanuele Sacchi. "Improving Transferability of Safety Performance Functions by Bayesian Model Averaging." Transportation Research Record: Journal of the Transportation Research Board 2280, no. 1 (January 2012): 162–72. http://dx.doi.org/10.3141/2280-18.

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Blackford, J., and A. Street. "Transferability of an ACP model across Australian community palliative care." BMJ Supportive & Palliative Care 1, no. 1 (June 1, 2011): 102. http://dx.doi.org/10.1136/bmjspcare-2011-000053.126.

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Koppelman, Frank S., and Chester G. Wilmot. "The effect of omission of variables on choice model transferability." Transportation Research Part B: Methodological 20, no. 3 (June 1986): 205–13. http://dx.doi.org/10.1016/0191-2615(86)90017-2.

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Apaydin, Halit, Alper S. Anli, and Ahmet Ozturk. "The temporal transferability of calibrated parameters of a hydrological model." Ecological Modelling 195, no. 3-4 (June 2006): 307–17. http://dx.doi.org/10.1016/j.ecolmodel.2005.11.032.

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Journal articles on the topic 'Model Transferability'

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