Quasi-empirical Models Research Articles

Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models

ABSTRACT The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West, Florida (United States). The model types examined were a commonly used quasi-empirical linear regression model and a decision tree-based Categorical Boosting (CatBoost) machine learning (ML) model. Image types examined were (four) cloud-free Sentinel-2 images and a maximum blue band (Band 2) value image composite of the four Sentinel-2 images. Initial models fitted were based on band reflectances alone. Geographical adaptivity was added by including UTM coordinates and refitting the models. Major findings were: 1) The ML/CatBoost models provided substantially better depth estimates than the quasi-empirical models. 2) The geographically adaptive models outperformed the non-geographically adaptive models. 3) The ML/CatBoost models that included non-visible spectral bands including infra-red improved SDB accuracy compared to ML/CatBoost and quasi-empirical models based only on visible spectral bands. 4) Accuracies from ML/CatBoost models were comparable across all individual images and the composite suggesting that CatBoost models eliminate or at least minimize the need to find “the best” cloud-free image nor is it necessary to create a composite image. 5) Localized SDB inaccuracy was spatially random. 6) Significant spatial hotspots where SDB accuracy was consistently higher or lower across all images and models were present. Results suggest that image selection is less important for global and local SDB accuracy than using ML models that detect hidden interactions and non-linear relationships among pixel reflectance and geographic location. The spatially random local deviation from global accuracy suggests a weak ability to infer local accuracy from neighboring accuracies. This lack of spatial autocorrelation among errors is potentially problematic for the use of SDB maps for navigation since error at any location is generally inferred from known uncertainties at neighboring locations. Rigorous and robust uncertainty analysis is necessary in any effort to improve SDB, and the uncertainty analysis techniques employed that characterize SDB uncertainty in both statistical and geographical space could be an important part of quality assurance and continuous improvement.

NCNA: Integrated platform for constructing, visualizing, analyzing and sharing human-mediated nitrogen biogeochemical networks

Human alterations to the nitrogen (N) cycle are closely associated with global environmental and climate change. New tools are necessary to model and analyze the highly complex N cycles emerging from human-mediated ecosystems. We developed a new software, NCNA, to provide three functions: a) rigorous reconstruction of quasi-empirical models (QEMs), b) computer-aided interface for data collection and automatic sensitivity analysis, and c) automatic generation, visualization and network environ analysis (NEA) of N cycling networks.

Wildland surface fire spread modelling, 1990 - 2007. 3: Simulation and mathematical analogue models

In recent years, advances in computational power have led to an increase in attempts to model the behaviour of wildland fires and to simulate their spread across landscape. The present series of articles endeavours to comprehensively survey and précis all types of surface fire spread models developed during the period 1990–2007. The present paper surveys models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one-dimensional models to two dimensions and then simulate the propagation of a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical concept (rather than a physical representation of fire spread) that coincidentally represents the spread of fire. Other papers in the series survey models of a physical or quasi-physical nature, and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.

Wildland surface fire spread modelling, 1990 - 2007. 2: Empirical and quasi-empirical models

In recent years, advances in computational power have led to an increase in attempts to model the behaviour of wildland fires and to simulate their spread across landscape. The present series of articles endeavours to comprehensively survey and précis all types of surface fire spread models developed during the period 1990–2007. The current paper surveys models of an empirical or quasi-empirical nature. These models are based on the statistical analysis of experimentally obtained data with or without some physical framework for the basis of the relations. Other papers in the series review models of a physical or quasi-physical nature, and mathematical analogues and simulation models. The main relations of empirical models are those of wind speed and fuel moisture content with rate of forward spread. The focus of the discussion is on the treatment of the wind speed and fuel moisture functions by the models.

Production of alcohol free wine by pervaporation

The alcohol content of Tokaji Hárslevelű (Linden leaves) wine samples was reduced by pervaporation process. The advantages of this modern membrane technique made us succeed in manufacturing a final product of a quality that matches the sensoric value of real wines. A wine almost free of alcohol produced by this method may be used as a substitute with no harmful effect due to its low ethanol content. The organoleptic characteristics are expected not to decrease due to of the mild process, so the positive physiological influence might be associated with the features close to the original character of the wine. Pervaporation experiments on a laboratory scale have been performed at different temperatures. The alcohol concentrate crossing the membrane, and thus separated, has been collected as a by-product and the remaining mixture of alcohol free wine compounds was considered the main product of the process. The separated wine samples and the extracted alcohol concentrates were then gas chromatographically analysed. The influence of process parameters on the result, from the aspect of optimization and planning, has been analysed, and the experiences of this analysis have been built in empirical and quasi-empirical models. Knowing the membrane-characteristics, we were capable of defining the equations that enable the planning of the pilot-plant scale unit operations and, the estimation of costs. The results of this investigation show that working temperature plays the most important role in the production of low alcoholic and alcohol free wines by pervaporation. At higher temperatures the membrane’s separation efficiency and the separation ability decrease. Thus, the permeate production gets faster, but less desired product is gained by separation. Economical analyses prognoses a great investment cost demand, that can be explained by the relatively high price of pervaporation membranes. Remuneration can seriously be promoted by the by-product utilization e.g. by the use of the separated alcohol concentrate as a raw material for wine distillates or industrial spirits.

Econometric versus quasiempirical models: Macroeconomic policy analysis in the Netherlands

The Netherlands has a long standing tradition with respect to the use of macroeconometric models for policy analysis. Next to these models, quasiempirical models, which are relatively small and calibrated models, also play a certain role in the policy making process. The present article evaluates the pros and cons of using quasiempirical models for actual policy analysis. A recent model of this type is analysed and compared with the empirical policy model of the Dutch central bank, MORKMON, as a representative of the modern generation of Dutch macroeconometric models. Some important features of this quasiempirical model are wage rigidity, downward price rigidity, flexible exchange rates, imperfect substitution between both various domestic assets and foreign assets, a Tobin's q type of monetary transmission, and exogenous expectations with respect to inflation and the exchange rate. The model stresses the importance of profitability for economic growth.